Data discovery

Repositories:

Results: 541 items found

4DMED hydrological stations

Hydrological station data collected in the 4DMED projects: https://www.4dmed-hydrology.org/ Data for these stations are available for project partners only, via API using the token.

Maps

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ADO - Discharge hydrological datasets

Daily time-series of discharge stations present within the Alpine space, obtained from different providers. Is made up of four columns: id station, date, discharge and the data quality information given by its different providers. The database contains observational daily discharge data deriving from the first measurement (differs for each region) to the present, with more than 1400 stations. These data were collected from multiple data providers within the ADO study region, covering the countries Austria, France, Germany, Italy, Slovenia and Switzerland. The spanned period is 1869-2021. The missing dates were added in order to have continuous time-series.

PostgreSQL

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Jan. 1, 1869, 12:50 a.m. Dec. 31, 2021, 1 p.m.

ADO Factsheet: Soil Moisture Anomalies (SMA)

This factsheet provides a detailed technical description of the indicator Soil Moisture Anomaly (SMA), which is implemented in the Alpine Drought Observatory (ADO), and used for detecting and monitoring agricultural drought conditions.

datasets

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Dec. 19, 2023, 8 a.m. Dec. 19, 2023, 8 a.m.

ADO Factsheet: Standardized Precipitation-Evapotranspiration Index (SPEI)

The Standardised Precipitation-Evapotranspiration Index (SPEI) is a multiscalar drought index based on precipitation and potential evapotranspiration. It can be used for determining the onset, duration and magnitude of drought conditions with respect to normal conditions in a variety of natural and managed ecosystems such as agricultural ecosystems, forests, rivers, etc. The SPEI shows the anomalies (deviations from the long-term average) of the observed total surface water balance for any given location and accumulation period of interest. The name of the index is usually modified to include the accumulation period. SPEI-3, for example, refers to accumulation period of three months (Vicente-Serrano and Beguería, 2022).

datasets

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Dec. 19, 2023, 8 a.m. Dec. 19, 2023, 8 a.m.

ADO Factsheet: Standardized Precipitation Index (SPI)

The Standardised Precipitation Index (SPI) is the most commonly used index worldwide for detecting and characterising meteorological droughts. A meteorological drought is defined as a period with abnormal precipitation deficit, in relation to normal conditions for a region, represented as a long-term average. The SPI shows the normalisedanomalies (deviations from the long-term average) of the observed total precipitation for any given location and accumulation period of interest. The name of the index is usually modified to include the accumulation period. SPI-3, for example, refers to accumulation period of three months (JRC EDO, 2020).

datasets

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Dec. 18, 2023, 10 a.m. Dec. 18, 2023, 10 a.m.

ADO Factsheet: Standardized Snowpack Index (SSPI)

When precipitation falls in the form of snow, water may be stored as snowpack. This decreases runoff and groundwater recharge during the winter months and contributes to the increased runoff during the spring and early summer. The Standardized SnowPack Index provides information on the relative volume of the snowpack in the catchment on a ten-daily and monthly basis compared to the period of reference (JRC EDO, 2022). The name of the index is modified to include the averaging period in days. SSPI-10, for example, refers to an average period of 10 days (JRC EDO, 2020).

datasets

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Dec. 19, 2023, 8 a.m. Dec. 19, 2023, 8 a.m.

ADO Hydrological boundary

The layer shows the boundary considered in the ADO project for the hydrological datasets.The overall objective of the Alpine Drought Observatory - ADO project is to create an online drought monitoring platform and develop policy implementation guidelines for proactive drought management in the Alpine regions.

Maps

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March 2, 2021, 8:54 a.m. March 2, 2021, 8:54 a.m.

ADO SNOWGRID CL Validation Report

ADO Datasets from deliverable D.T2.1.1 are downscaled from ERA5 surface datasets (Hersbach et al. 2018) using quantile mapping. As ERA5 snow data cannot be downscaled in a similar manner (no snow data available from UERRA mescan surfex; Bazile et al. 2017) and its accuracy within the alpine domain was questioned, a modified version of the deterministic snow model SNOWGRID-CL (Olefs et al. 2020) was used to derive daily fields of snow depth and snow water equivalent within the scope of the ADO project (part of deliverable D.T1.2.1).

datasets

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Dec. 19, 2023, 8 a.m. Dec. 19, 2023, 8 a.m.

ADO Soil Moisture Validation Report

When precipitation falls in the form of snow, water may be stored as snowpack. This decreases runoff and groundwater recharge during the winter months and contributes to the increased runoff during the spring and early summer. The Standardized SnowPack Index provides information on the relative volume of the snowpack in the catchment on a ten-daily and monthly basis compared to the period of reference (JRC EDO, 2022). The name of the index is modified to include the averaging period in days. SSPI-10, for example, refers to an average period of 10 days (JRC EDO, 2020).

datasets

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Dec. 19, 2023, 8 a.m. Dec. 19, 2023, 8 a.m.

Air Quality Utilizing Indoor AI Sensors (AQUINAS) - Input data

This research provides knowledge on the performance and limitations of low-cost environmental sensors in real usage conditions. The EQ-OX platform will be used and the collected data will be compared to those of the reference station. The main purpose of this research is to compare these data and try to correct them through the use of machine learning algorithms.

InfluxDB

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March 27, 2026, midnight March 27, 2026, midnight

Air Quality Utilizing Indoor AI Sensors (AQUINAS) - Postprocessing data

This research provides knowledge on the performance and limitations of low-cost environmental sensors in real usage conditions. The EQ-OX platform will be used and the collected data will be compared to those of the reference station. The main purpose of this research is to compare these data and try to correct them through the use of machine learning algorithms.

InfluxDB

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March 27, 2026, midnight March 27, 2026, midnight

Air temperature - Venosta Valley

Daily averaged air temperature maps [C] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Air temperature - Venosta Valley

Daily averaged air temperature maps [C] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

OpenEO

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Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

Alpine space-Eusalp intersection

The Alpine Drought Observatory - ADO project is interested in discharge (only for stations with a catchment area > 1000 Km2 and currently active), groundwater (only for stations for major groundwater bodies), and major lake levels (only for major water bodies (surface > 5 km2)) data. The overall objective of the Alpine Drought Observatory - ADO project is to create an online drought monitoring platform and develop policy implementation guidelines for proactive drought management in the Alpine regions. The ADO project consortium includes 11 institutions from 6 Alpine countries with a wide range of expertise, covering meteorological and hydrological monitoring, specific knowledge on modeling, drought risk and impact assessment, as well as water governance in the different sectors. Further information about the ADO project can be found here: https://www.alpine-space.eu/projects/ado/en/about.

Maps

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Nov. 11, 2021, 8:34 a.m. Nov. 11, 2021, 8:34 a.m.

ALPS: Glaciers outline 2015-2020

High-resolution outline of glaciers from both Sentinel-1 and Sentinel-2 satellites over South Tyrol (years 2015 to 2020, included).

Maps

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July 8, 2022, 5:42 p.m. July 8, 2022, 5:42 p.m.

APPLE PlantVoice Database

Project: APPLE (Fusion Grant) Dataset: Tree sensor database for water-stress assessment Partners: PlantVoice; Laimburg Sites & nodes: Terlano vineyard (Laimburg) – 5; CSS Laboratories – 4; PlantVoice orchard/fields – 10+ Monitoring unit: 1 node per tree Sensors per node: PlantVoice custom sap sensor; temperature; humidity Total nodes: 19+ Date of data collection: started form 2025.06.01 and it is ongoing

datasets

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April 1, 2025, midnight April 1, 2025, midnight

Apple Trait Ontology (Crop Ontology - CO)

The Apple Trait Ontology (ATO) is a vital resource for the precise and standardized description of apple varieties, enabling a deeper understanding of their genetic, morphological, and agronomic characteristics. Apples (Malus domestica) are one of the most widely cultivated and consumed fruits globally, and their immense diversity poses challenges in terms of classification, breeding, and research. ATO serves as a comprehensive framework to overcome these challenges. ATO is designed to capture and categorize a wide range of apple traits, encompassing characteristics such as fruit shape, size, color, texture, flavor, nutritional content, disease resistance, and more. This ontology employs standardized terminology and hierarchical structures to create a common language for researchers, horticulturists, and fruit breeders. The ATO not only aids in data sharing and integration but also promotes collaboration among diverse stakeholders in the apple industry.

Other

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Aug. 23, 2022, 2 a.m. None

Archivio Tirolese -Argento Vivo

Archivio Tirolese per la documentazione e l'arte fotografica di Lienz (TAP): - Collezione Lisl Gaggl-Meirer (Paesaggio, montagna; Tirolo Orientale; 1970-1990) - Collezione Klebelsberg, Istituto di Geologia, Università di Innsbruck (Paesaggio, montagna, militari; Dolomiti; 1907-1910) - Collezione Hans Peter Falkner (Città, Lienz; ca. 1965-1985) - Collezione Foto Baptist (Paesaggio, montagna; Tirolo Orientale; 1965-1975)

Maps

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Jan. 18, 2018, 1 p.m. Jan. 18, 2018, 1 p.m.

Aree BIPVmeetsHistory

Mappatura delle 4 Aree di progetto del territorio di Como Risultati del progetto Interreg IT-CH "BIPV meets History" Attivita' 4 - Mappatura del potenziale solare www.bipvmeetshistory.eu

Maps

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Dec. 20, 2023, 11:10 a.m. Dec. 20, 2023, 11:10 a.m.

A validation of ADO drought indices SPI and SPEI in Slovenia

Drought indices are essential for tracking and warning of the possible drought related effects, impacts and outcomes (WMO and GWP, 2016). The use of a specific drought index in this manner requires a prior assessment of the index performance in detecting and monitoring different types of drought. Agricultural drought affects plants and crops, sometimes resulting in extensive crop failure. As agricultural drought is particularly relevant for the Alpine region and the Slovenian case study area, we investigated the ability of SPI and SPEI to detect agricultural drought through validation against annual yield data for several crops and types of grassland, averaged to statistical regions (NUTS3 level). Prior to evaluating indices against yield data, we evaluated SPI and SPEI values calculated from ERA5 reanalyses against SPI and SPEI values calculated from ground observations at representative meteorological stations in Slovenia in order to learn whether the indices calculated in the Alpine domain are relevant on a national level.

datasets

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Dec. 19, 2023, 8 a.m. Dec. 19, 2023, 8 a.m.

Biotop Bletterbach

Biotope area of the Bletterbach geological Park in South Tyrol.

Maps

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Oct. 5, 2020, 9:09 a.m. Oct. 5, 2020, 9:09 a.m.

BoscoFontana

This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

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July 13, 2026, midnight July 13, 2026, midnight

BOZEN: Voronoi diagram N.1

Partition of the the municipality of Bolzano into Voronoi regions for proper aggregation of sensitive data (created by APB Osservatorio del Lavoro).

Maps

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March 4, 2022, 6:58 p.m. March 4, 2022, 6:58 p.m.

BURUNDI: 2021 Population estimates by Communes

Population estimation by UNFPA with Institut de Statistiques et d'Etudes Economiques du Burundi (ISTEEBU). Burundi administrative level 0-2 2021 sex and age disaggregated projections from 2008 population census statistics

Maps

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June 3, 2021, 3:11 p.m. June 3, 2021, 3:11 p.m.

Burundi: Buildings count (100m)

This layer shows gridded buildings with 100m resolution for Burundi. It was extracted from Gridded maps of building patterns throughout sub-Saharan Africa, version 2.0. This raster contains counts of buildings that fall within a grid cell. Each buildings was counted in the grid cell that contained the centroid of its building footprint

Maps

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April 30, 2021, 6:15 p.m. April 30, 2021, 6:15 p.m.

BURUNDI: Cropland

Land classified as cropland over Burundi (from Copernicus Land Cover product).

Maps

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Oct. 22, 2021, 4:13 p.m. Oct. 22, 2021, 4:13 p.m.

Burundi - Dams (Aquastat)

Dam locations in Burundi extracted from Aquastat Dam database for Africa. AQUASTAT gathers detailed information about dams in each country, especially on location, height, reservoir capacity, surface area and main purpose. http://www.fao.org/aquastat/en/databases/dams

Maps

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Aug. 17, 2021, 7:20 p.m. Aug. 17, 2021, 7:20 p.m.

BURUNDI: Gridded Population estimates (2019 | 100m)

Gridded population estimates from WorldPop (10.5258/SOTON/WP00682) over Burundi calibrated to match the 2019 population projections by commune by ISTEEBU/UNFPA (https://data.humdata.org/dataset/burundi-administrative-level-0-2-population-statistics-2018).

Maps

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Oct. 25, 2021, 12:13 p.m. Oct. 25, 2021, 12:13 p.m.

BURUNDI: Gridded Population estimates (2021 | 100m)

Gridded population estimates from WorldPop (10.5258/SOTON/WP00682) over Burundi calibrated to match the 2021 population projections by commune by ISTEEBU/UNFPA (https://data.humdata.org/dataset/burundi-administrative-level-0-2-population-statistics-2018).

Maps

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Burundi: Health sites

This layer contains the health sites in Burundi classified by type. The data source is the Burundi Ministry of Health.

Maps

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Aug. 17, 2021, noon Aug. 17, 2021, noon

BURUNDI: Land Cover

Land classification over Burundi (from Copernicus Land Cover product).

Maps

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March 15, 2022, 8:55 a.m. March 15, 2022, 8:55 a.m.

BURUNDI: Landslides susceptibility map (May)

The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

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Nov. 5, 2021, 12:32 p.m. Nov. 5, 2021, 12:32 p.m.

Burundi: named settlements

Geographic names of populated places in Burundi by NGA Geonet Names Server (NGA). Last updated: 12. July 2021.

Maps

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Aug. 26, 2021, 5:40 p.m. Aug. 26, 2021, 5:40 p.m.

BURUNDI: OSM bridges

Bridges of Burundi (OSM). OSM Download from September 2020.

Maps

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April 23, 2021, 11:40 a.m. April 23, 2021, 11:40 a.m.

Burundi Population 2020

Estimated total number of people per grid-cell at a resolution of 3 arc seconds.

Maps

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April 30, 2021, 5:35 p.m. April 30, 2021, 5:35 p.m.

BURUNDI: Power grid

Electricity transmission network of Burundi (World Bank+REGIDISO). https://energydata.info/dataset/burundi-electricity-transmission-network-2007

Maps

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April 23, 2021, 12:20 p.m. April 23, 2021, 12:20 p.m.

BURUNDI: Power plants

Power plants in Burundi with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006). https://datacatalog.worldbank.org/dataset/burundi-power-plants

Maps

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April 23, 2021, 12:30 p.m. April 23, 2021, 12:30 p.m.

BURUNDI: Primary and secondary roads

This layer contains primary and secondary roads in Burundi catagorising the roads into three classes and providing information on surface and usability. The data originates from the Burundi Ministry of Health.

Maps

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June 30, 2021, 4:21 p.m. June 30, 2021, 4:21 p.m.

Burundi: Protected Areas

This layer shows protected areas in Burundi according to the World Database of protected areas.

Maps

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May 3, 2021, 6:20 p.m. May 3, 2021, 6:20 p.m.

BURUNDI: Roads topologic indicators

The layer contains two topologic indicators computed on the nodes (roads intersections and dead-ends) of the OpenStreetMap (OSM) drivable roads dataset: i) Betweenness Centrality, and ii) Current Flow Betweenness Centrality.

Maps

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July 23, 2021, 10:55 a.m. July 23, 2021, 10:55 a.m.

BURUNDI: Roads topologic indicators

The layer contains the Betweenness Centrailty indicator computed on the edges of the OpenStreetMap (OSM) main roads (up to tertiary).

Maps

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July 30, 2021, 9:10 p.m. July 30, 2021, 9:10 p.m.

BURUNDI: Roads topologic indicators by province

The layer contains two topologic indicators computed on the nodes (roads intersections and dead-ends) of the OpenStreetMap (OSM) drivable roads dataset: i) Betweenness Centrality, and ii) Current Flow Betweenness Centrality. The data has been computed separately on each province of Burundi, then merged on the same file.

Maps

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July 30, 2021, 6:20 p.m. July 30, 2021, 6:20 p.m.

Burundi: Schools

This layer shows the location of schools in Burundi. The data originates from BCG.

Maps

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Aug. 17, 2021, 7:10 p.m. Aug. 17, 2021, 7:10 p.m.

BURUNDI: Terrain Ruggedness (7.5 arc-sec)

Terrain ruggedness (elevation standard deviation) over Burundi at 7.5 arc-sec (225 m) of spatial resolution. Cropped from the original GMTED2010 global topographic elevation model from USGS/NGA.

Maps

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June 4, 2021, 3:30 p.m. June 4, 2021, 3:30 p.m.

Burundi: Touristic sites

This layers shows touristic sites in Burundi. The data was provided by BCG.

Maps

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Aug. 17, 2021, 4:40 p.m. Aug. 17, 2021, 4:40 p.m.

CastelPorziano

This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

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July 13, 2026, midnight July 13, 2026, midnight

CDD - NUTS level 0

Cooling Degree Days at country level (NUTS level 0) is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. CDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded CDD is aggregated and subsequently presented on NUTS-0 level.

Maps

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Oct. 6, 2020, 2:23 p.m. Oct. 6, 2020, 2:23 p.m.

CDD - NUTS level 2

Cooling Degree Day (CDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-2 level.

Maps

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Sept. 25, 2020, 3 p.m. Sept. 25, 2020, 3 p.m.

CE_demo_cases

The layer shows the following information about Plus Energy Buildings (PEB) demo cases: 1) context, 2) key features, 3) owner, 4) Location, 5) Technologies integration, 6) Demo community and 7) partnership.

Maps

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March 19, 2021, 3:54 p.m. March 19, 2021, 3:54 p.m.

census tracts for bolzano (2011) with exposure

Support dataset for the RETURN Bolzano pilot. case. All geospatial information described in the impact chain developed in the Bolzano pilot case to be uploaded in the RETURN Almaviva platform will be defined upon this geographical boundaries.  

Maps

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May 15, 2025, 9:57 a.m. May 15, 2025, 9:57 a.m.

Climate Classification - NUTS0

Climate classification in european countries. The climates were extracted by the Koppen-Geiger classification

Maps

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Nov. 30, 2020, 5:10 p.m. Nov. 30, 2020, 5:10 p.m.

Copernicus Land Monitoring Service - EU-DEM

EU-DEM is a digital surface model (DSM) of EEA member and cooperating countries representing the first surface as illuminated by the sensors. It is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach.

datasets

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June 6, 2024, 3:58 p.m. June 6, 2024, 3:58 p.m.

Copernicus Land Monitoring Service - EU-DEM

EU-DEM is a digital surface model (DSM) of EEA member and cooperating countries representing the first surface as illuminated by the sensors. It is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery, Aix-en-Provence_Pennes-Mirabeau, FR 3, 2025

The input RoseL-like imagery dataset is a copy of SAOCOM-1B SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data cover the Aix-en-Provence region, in South of France, including the Pennes-Mirabeau area, where wildfires happened in Summer 2025. Coordinates are [5.23, 43.71], [5.85, 42.93]. Data are acquired in descending (DSC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery, GrandLeez, BE 1, 2022

The input RoseL-like imagery dataset is a copy of SAOCOM-1A L-band SAR Stripmap dual pol (VH-VV) data collection over a sub-region of Wallon Region, Belgium, downloaded from saocom.asi.it. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given/provided here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. A geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery, Martigues, FR 1, 2025

The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data cover the Martigues area, South of France, where a wildfire happened in Summer 2025. Coordinates are [4.43, 43.44], [5.36, 42.92]. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery - Mazia Matsch catchment, South Tyrol, IT, 2023

The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it and covering the Glorenza-Merano region, including the Mazia-Matsch catchment, in South-Tyrol, Italy. Coordinates are [10.29, 46.96], [11.27, 46.38]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery - Mazia Matsch catchment, South Tyrol, IT, 2024

The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it and covering the Glorenza-Merano region, including the Mazia-Matsch catchment, in South-Tyrol, Italy. Coordinates are [10.33, 46.91], [11.33, 46.28]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery - Mazia Matsch catchment, South Tyrol, IT, 2025

The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it and covering the Glorenza-Merano region, including the Mazia-Matsch catchment, in South-Tyrol, Italy. Coordinates are [10.30, 46.92], [11.29, 46.34]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery, Vidauban, FR 2, 2024

The input RoseL-like imagery dataset is a copy of SAOCOM-1B SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Coordinates are [5.95, 43.46], [6.90, 42.88]. Data cover the Vidauban area, South of France, where a wildfire happened in Summer 2024. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery - Walloon Region - BE 1, 2025

The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection over a sub-region of Wallon Region, Belgium, downloaded from saocom.asi.it. Coordinates are [4.51,51.04], [5.58,50.53]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery, Walloon Region, BE 3, 2025

The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection over a sub-region of Wallon Region, Belgium, downloaded from saocom.asi.it. Coordinates are [4.02, 50.78], [4.73, 50.06]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in descending (DSC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery, Walloon Region, BE 4, 2025

The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection over a sub-region of Wallon Region, Belgium, downloaded from saocom.asi.it. Coordinates are [5.36,50.20], [6.05,49.42]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in descending (DSC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery, Walloon Region, BE 5, 2025

The input RoseL-like imagery dataset is a copy of SAOCOM-1A L-band SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it, Acquisitions cover a sub-region of Wallon Region, Belgium, including the forest of Grand-Leez, for 2022. Coordinates are [4.52,51.00], [5.60,50.49]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given/provided here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. A geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Senwise RoseL-like imagery, Walloon Region, Tellin, BE 2, 2024

The input RoseL-like imagery dataset is a copy of SAOCOM-1A L-band SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it, Acquisitions cover a sub-region of Wallon Region, Belgium, including the forest of Grand-Leez, for 2022. Coordinates are [4.52,51.00], [5.60,50.49]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given/provided here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. A geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Copernicus Surface Soil Moisture - 1km

The Soil Water Index quantifies the moisture condition at various depths in the soil. It is mainly driven by the precipitation via the process of infiltration. Soil moisture is a very heterogeneous variable and varies on small scales with soil properties and drainage patterns. Satellite measurements integrate over relative large-scale areas, with the presence of vegetation adding complexity to the interpretation.

OpenEO

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April 18, 2023, 4:05 a.m. April 18, 2023, 4:05 a.m.

CORINE Land Cover 2018 (CLC2018), Europe (Raster 100m)

The Corine Land Cover 2018 (CLC2018) dataset is one of the datasets produced within the CORINE Land Cover Program to capture land cover/ land use status for 2018. The Corine Land Cover (CLC) is an European programme, coordinated by the European Environment Agency (EEA), providing consistent information on land cover and land cover changes across Europe. CLC products are based on the photointerpretation of satellite images by the national teams of the participating countries - the EEA member or cooperating countries. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe. The resulting European database is based on standard methodology and nomenclature with following base parameters: - 44 thematic classes in the hierarchical 3-level Corine nomenclature - minimum mapping unit (MMU) for status layers is 25 hectares - minimum width of linear elements is 100 metres - minimum mapping unit (MMU) for Land Cover Changes (LCC) for change layers is 5 hectares. It is provided as 100m raster data here

STAC

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Jan. 1, 2018, 1 a.m. Jan. 1, 2018, 1 a.m.

Corine Land Cover (CLC) 2018

CLC2018 is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Corine Land Cover (CLC) 2018

CLC2018 is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe.

OpenEO

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Oct. 1, 2024, 9:45 a.m. Oct. 1, 2024, 9:45 a.m.

Daily evaporation product - Adige basin

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 20-m spatial resolution for the period 2017-2018 over the Adige basin in Italy. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in GeoTIFF format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations. This dataset was derived by linear interpolation of a 100-m evaporation dataset produced over the Po River Basin (Bartkowiak et al. 2023, 2024) and it is intended to be a sample dataset for a higher-resolution improved version currently under development within the project RETURN.

STAC

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June 14, 2023, 4:05 a.m. June 14, 2023, 4:05 a.m.

Daily evaporation product - Ebro basin

Evaporation (E) is the estimated flux of water evaporated from the land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over the Ebro basin in Spain. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

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June 14, 2023, 4:05 a.m. June 14, 2023, 4:05 a.m.

Daily evaporation product Ebro basin

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Ebro basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTiff (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Daily evaporation product - Herault basin

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Hérault basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

View
June 14, 2023, 4:05 a.m. June 14, 2023, 4:05 a.m.

Daily evaporation product Herault basin

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Herault basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTiff (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Daily evaporation product - Medjerda basin

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Medjerda basin in Tunisia. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

View
June 14, 2023, 4:05 a.m. June 14, 2023, 4:05 a.m.

Daily evaporation product Medjerda basin

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Medjerda basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTiff (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

Daily evaporation product - Po basin

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Po basin in Italy. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

View
June 14, 2023, 4:05 a.m. June 14, 2023, 4:05 a.m.

Daily evaporation product Po basin

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Po basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTiff (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Daily Height of Snow for the European Alps

S3M-Alps is a 500-m reanalysis of Snow Water Equivalent and Height of Snow for the European Alps. The product is driven by ERA5-Land meteorological reanalysis downscaled to 500 m using the MicroMet statistical–dynamical method (Liston and Elder, 2006). Downscaled meteorological fields are used to force the physically-based S3M snow model (Avanzi et al., 2022).

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Daily meteorological records - Climate Data Base

The Climate Database (CDB) contains meteorological time series of daily temperature (maximum, minimum and mean) and daily total precipitation for more than 250 station sites in Trentino – South Tyrol region. The data were collected from the regional meteorological networks of Bolzano and Trento Provinces and include open access records from several close sites in Austria. The spanned period is 1950 – 2021. Note that mean temperature is defined by averaging minimum and maximum values in all cases. The CDB was built in the framework of the Use-Case number 8 of the DPS4ESLAB project.

PostgresDB

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Jan. 15, 2020, midnight Jan. 15, 2020, midnight

Daily precipitation product

Daily precipitation (P) data at 1 km spatial resolution - Data are obtained by downscaling CPC and GPM-LR data to 1 km spatial resolution using CHELSA climatology and merging them through a triple collocation technique on CPC GPM and ERA5

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Daily Snow Water Equivalent for the European Alps

S3M-Alps is a 500-m reanalysis of Snow Water Equivalent for the European Alps. The product is driven by ERA5-Land meteorological reanalysis downscaled to 500 m using the MicroMet statistical–dynamical method (Liston and Elder, 2006). Downscaled meteorological fields are used to force the physically-based S3M snow model (Avanzi et al., 2022).

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Daily Surface Soil Moisture

A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture,Land Surface Temperature and Vegetation Optical Depth from passive microwave data.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Daily TWSC product-Ebro

A 1km experimental dataset for the four Mediterranean basins of daily TWSC data from GRACE monthly JPL mascon and a water budget model driven by GLEAM-1k SM2RAIN_GPM_CPC and HDMA model

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Daily TWSC product-Po

A 1km experimental dataset for the four Mediterranean basins of daily TWSC data from GRACE monthly JPL mascon and a water budget model driven by GLEAM-1k SM2RAIN_GPM_CPC and HDMA model

STAC

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July 13, 2026, midnight July 13, 2026, midnight

DinAlpConnect Project area

This data set represents the considered area between the Alps and Dinaric mountains to analyze the situation of ecological connectivity. Link to map: https://maps.eurac.edu/maps/1140/view File name: DinAlpConnect_Project_area.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Continuum Suitability Index

This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on land use, population pressure, protection status, fragmentation and topography. File name: DinaricAlps_CSI.tif Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Ecological Conservation Areas (SACA1)

This data set shows important Ecological Conservation Areas (SACA1), bigger than 100ha, in the Dinaric Alps. These areas are expected to have a high biological value and that ecological connectivity is functioning well. Strategic Connectivity Areas (SACAs) derive from the Continuum Suitability Index. This approach is a way to display via GIS the most important sites for the overall ecological network on a macro-regional level (SACA1). Here, the most important ones were selected by expert evaluation. Filename: DinaricAlps_SACA1_Ecological_Conservation_Areas.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Ecological Linkages (SACA2)

Ecological linkages are least cost paths, connecting the most important Ecological Conservation Areas (SACA1). They are part of the ecological intervention areas (SACA2). File name: DinaricAlps_SACA2_Regional_ecological_linkages_LCP_Assessment.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Ecological Restoration Areas / Barriers (SACA3)

Ecological Restoration Areas represent important barriers and have a low continuum suitability index (CSI). Ecological Restoration Areas (SACA3) are those ones, where ecological movements are not possible at the current stage and where it is necessary to implement restoration measures. These areas are currently the main barriers. For the calculation of these areas, all areas with a CSI of 1-4 were selected. File name: DinaricAlps_SACA3_Ecological_Barriers.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Ecological stepping stones (SACA1)

Stepping stones are representing areas with a high ecological value, important for ecological linkages. They are calculated by the Continuum Suitability Index (CSI) and the Strategic Connectivity Areas, considering small and less important Ecological Conservation Areas. File name: DinaricAlps_SACA1_Ecological_Stepping_Stones.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Environmental protection indicator

This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on the protection status of protected areas. File name: DinaricAlps_ENV.tif Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2020, 1 a.m. Jan. 1, 2020, 1 a.m.

Dinaric Alps: Fragmentation indicator

This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on the effective mesh size and the effective mesh density. File name: DinaricAlps_FRA.tif Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Land cover indicator

This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on land cover classes. File name: DinaricAlps_LAN.tif Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2018, 1 a.m. Jan. 1, 2018, 1 a.m.

Dinaric Alps: Motorway barriers

This layer is showing motorway barriers with potential ecological linkages in the Dinaric Alps. File Name: DinaricAlps_SACA2_motorway_barriers.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Population indicator

This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on population density data. File name: DinaricAlps_POP.tif Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2018, 1 a.m. Jan. 1, 2018, 1 a.m.

DinaricAlps: Regional ecological corridors (SACA2)

This layer shows the designed width of ecological corridors, that connect Ecological Conservation Areas. An approximate width of 2km was designed by truncating the normalized cost-weighted distances of the corridors at 40.000 km. File name: DinaricAlps_SACA2_Reg_ecological_corridors.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Topography indicator

This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on altitude and slope conditions. File name: DinaricAlps_TOP Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

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Jan. 1, 2020, 1 a.m. Jan. 1, 2020, 1 a.m.

DrSchär mais field monitoring timeseries

Timeseries of the DrSchär field monitoring in Este (PD) during summer 2022. Dataset of the soil temperature and soil humidity collected from different type of sensors distributed among the mais field. In the field were installed different types of sensors as many TDR sensors and one capacitive sensor; they collect temperature and humidity of the soil. The data from the sensor were collected by two networks: a Zigbee-based and one LoraWAN-based network.

InfluxDB

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Aug. 10, 2022, 11 a.m. Aug. 10, 2022, 11 a.m.

Ecological Connectivity for Red Deer in South Tyrol

Dieser Datensatz zeigt ein ökologisches Netzwerkmodell für den Rothirsch in Südtirol. Es umfasst sowohl bestehende als auch potenzielle Querungsmöglichkeiten und soll eine Orientierung für die Definition von konkreten Korridoren darstellen. Auf lokaler Ebene kann das Modell von der Realität abweichen, weshalb die Nutzung durch Wildtiere vor Ort kontrolliert werden muss. Das Modell besitzt keine rechtliche Gültigkeit und ist noch keinem wissenschaftlichen Peer-Review-Prozess unterzogen worden.   Questo dataset mostra un modello di rete ecologica per il cervo in Alto Adige. Include sia i passaggi esistenti che quelli potenziali, e può essere usato come riferimento per definire corridoi concreti. A livello locale, il modello può differire dalla realtà, pertanto è necessario controllare l'utilizzo da parte della fauna selvatica in loco. Il modello non ha validità giuridica e non è stato ancora sottoposto ad alcun processo di revisione "Peer-review" scientifica.   File: Ecological_network_Red_deer_South_Tyrol.shp Bericht zur Erstellung des Modells (Englisch): https://www.datocms-assets.com/31538/1737642672-d2-3-1_project-of-ecological-network-south-tyrol.pdf  Bericht zu technischen Verbesserungsvorschlägen: https://www.datocms-assets.com/31538/1770718944-d-2-5-1_technischer-vorschlag_okologisches-netzwerk_hirsch_sudtirol.pdf  Proposta tecnica: https://www.datocms-assets.com/31538/1757008589-d-2-5-1_proposta-tecnica_rete-ecologica-per-il-cervo_alto-adige.pdf  Eurac project website: https://www.eurac.edu/en/institutes-centers/institute-for-regional-development/projects/plantoconnect 

Maps

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June 17, 2025, 4:04 p.m. June 17, 2025, 4:04 p.m.

Edifici BIPVmeetsHistory

Edifici casi studio del progetto di ricerca Interregionale "BIPV meets History" nel territorio di Como. Attivita' di Mappatura solare dell'area di progetto. Sito del progetto: www.bipvmeetshistory.eu

Maps

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E_GLEAM_1km_2015-2021_D0_SSpain

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D10_SardiniaCorsica

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D11_Istria

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D12_Dalmatia

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D13_Epirus

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D14_Bulgaria

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D15_Greece

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D16_SWTurkey

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D17_Cyprus

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D18_Antioch

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D19_Cairo

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D1_ESpain

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D20_WEgypt

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D21_Cyrenaica

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D22_ELibya

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D23_WLibya

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D24_MidLibya

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D25_Tunisia

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D26_Algeria

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D27_Morocco

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D2_Ebro

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D3_BalearicIslands

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D4_Heraut

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D5_SFrance

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D6_Po

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D7_MidItaly

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D8_SItaly

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

E_GLEAM_1km_2015-2021_D9_Sicily

Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

Electrical energy consumption profiles – Residential Apartment, Bolzano, Italy (2026)

Energy Metering from a residential building (1 apartment) in Bolzano, Italy. Period 2/2026 - 5/2026Experimental setup including PZEM as meter+ ESP32 as node + RPi as gateway + InfluxDB), one reading per circuit per ~1-minute cycle (4 circuits × ~60 s sampling). NOTES: a microwave oven, a coffee machine and a cooking robot is supplied by "lights" circuit. "Kitchen" circuit only supplies an oven and an induction cooktop. "Laundry" circuit serves only a washing machine and a heat pump dryer. Among other minor loads, a dishwasher and a fridge are supplied by "outlets" circuit. Columns: `ISO 8601 timestamp (YYYY-MM-DD HH:MM:SS.ssssss)`, `voltage (V rms)`,`current (A rms)`, `energy (Wh)`, `frequency (Hz)`, `pf`, `power (W)` License: CC-BY 4.0 DOI: 10.5281/zenodo.20099037 Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: C. Pozza (2026). Electrical energy consumption profiles – Residential Apartment, Bolzano, Italy (2026). Zenodo. https://doi.org/10.5281/zenodo.20099037Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

Other

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Electricity Prices for Households

Electricity prices components for household consumers -annual data (from 2007 onwards). Annual values taken from the Eurostat dataset with a national level resolution.

Maps

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Feb. 16, 2021, 3:22 p.m. Feb. 16, 2021, 3:22 p.m.

Energy Cultures Drivers

The information provided intends to support a profiling exercise of users’ domestic energy use and how these variations are translated into different domestic energy-intensity practices across EU territories. This layer includes descriptive information on the energy demand dynamics at household level by means of taking into account the cultural-climatic aspects which characterise the EU climatic areas as part of this research.

Maps

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March 3, 2021, 9:52 a.m. March 3, 2021, 9:52 a.m.

Environmental Parameters

This layer contents specific Indoor Environmental Quality (IEQ) data sets provided which are rereferred to the closest possible locations of the Cultural-E demo cases. Specific locations and coordinates are included for each one in the IEQ related files that have been made available for downloading. Each geo-referred location contains IEQ related information and graphics, in the specific: 1. Reference year -.epw file-. 2. Climatic statistical data -.txt file-. Weather data plots, elaborated with Climate consultant and merged in a unique document -.pdf file-. 4. Weather data summary elaborated -.xls file-. Relevant information is provided in relation to: data source, used tool, implemented comfort tool, weather stations spec, software download links. This aims to illustrate the accuracy and "data fairness" of the data provided.

Maps

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March 16, 2021, 8:56 a.m. March 16, 2021, 8:56 a.m.

EO4MULTIHAZARDS Events Database

The EO4MULTIHAZARDS Events Database provides the scientific community with reliable data and promotes comprehensive research efforts on multi-hazard events. The collected datasets come from various sources and are updated automatically or manually when new data becomes available. The following is a list of data sources: - EMDAT portal provides a REST-API for authorized users to download data. - EFFIS portal provides a standard and open Web Feature Service (WFS) for downloading datasets. - British Geological Survey collects events datasets in United Kingdom and uploads new data to the database as it becomes available. Additionally the DB contains a list of Earth Observation datasets, useful to study natural hazard events. The collected information is available for visualization, but the full dataset is not downloadable due to license restrictions on the input data. Events are accessible via the web interface without restriction, and query results are available for download. Additional information is available on the Web Interface page. For this project, we harmonized the input datasets into a common format to easily compare them. The database was created in the project EO4MULTIHAZARDS, funded by the The European Space Agency’s (ESA).

PostgresDB

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April 1, 2025, midnight April 1, 2025, midnight

E-OBS daily gridded meteorological data for the European Alps from 1995 to present derived from in-situ observations

E-OBS is a daily gridded land-only observational dataset over Europe. The blended time series from the station network of the European Climate Assessment & Dataset (ECA&D) project form the basis for the E-OBS gridded dataset. All station data are sourced directly from the European National Meteorological and Hydrological Services (NMHSs) or other data holding institutions.

STAC

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Jan. 1, 1995, 1 a.m. Dec. 31, 2010, 1 a.m.

EU climate classification (Köppen-Geiger)

The most frequently used climate classification map is that of Wladimir Köppen, presented in its latest version 1961 by Rudolf Geiger. A huge number of climate studies and subsequent publications adopted this or a former release of the Köppen-Geiger map. While the climate classification concept has been widely applied to a broad range of topics in climate and climate change research as well as in physical geography, hydrology, agriculture, biology and educational aspects, a well-documented update of the world climate classification map is still missing. Based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service, we present here a new digital Köppen-Geiger world map on climate classification for the second half of the 20th century.

Maps

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Sept. 21, 2020, 1:49 p.m. Sept. 21, 2020, 1:49 p.m.

EU NUTS 3

2021

Maps

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Feb. 20, 2025, 1:33 p.m. Feb. 20, 2025, 1:33 p.m.

Eurac Snow Cloud Removal Modis

Snow maps with clouds removed by spatial and temporal filters. This product is not updated anymore. If you need the data please use this link: https://zenodo.org/records/3601891 to access them.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Evapotranspiration - Venosta valley

Daily evapotranspiration [mm/day] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Evapotranspiration - Venosta valley

Daily evapotranspiration [mm/day] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

OpenEO

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Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

Exposure indicator

Exposure indicator for the Vulnerability Map of Snow Toursim Destinations - BeyondSnow project

Maps

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Factor available water capacity

USDA soil property map: available water capacity. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015. For further information visit the website European soil data centre (ESDAC).

OpenEO

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Oct. 1, 2024, 9:45 a.m. Oct. 1, 2024, 9:45 a.m.

Factor available water capacity

USDA soil property map: available water capacity. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015. For further information visit the website European soil data centre (ESDAC).

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Factor distance to water

Distance [m] is calculated at each location to the nearest lakes, water reservoirs, and rivers. Rivers were filtered to Strahler order greater than 3.

OpenEO

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Oct. 1, 2024, 9:45 a.m. Oct. 1, 2024, 9:45 a.m.

Factor distance to water

Distance [m] is calculated at each location to the nearest lakes, water reservoirs, and rivers. Rivers were filtered to Strahler order greater than 3.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Factor elevation

ADO_elevation is a digital surface model (DSM). For further information visit website copernicus land monitoring service: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products/eu-dem-v1.0?tab=metadata

OpenEO

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Oct. 1, 2024, 9:45 a.m. Oct. 1, 2024, 9:45 a.m.

Factor elevation

ADO_elevation is a digital surface model (DSM). For further information visit website copernicus land monitoring service: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products/eu-dem-v1.0?tab=metadata

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Factor humus content

Organic Carbon Content In Topsoils In Europe (OCTOP) - ESDAC makes available the Maps of Organic carbon content (%) in the surface horizon of soils in Europe. Resolution: 1 km - Year: 2004. The result is available as a map and an explaining booklet: The Map of Organic Carbon Content In Topsoils In Europe: Version 1.2 September - 2003 (S.P.I.04.72).

OpenEO

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Oct. 1, 2024, 9:45 a.m. Oct. 1, 2024, 9:45 a.m.

Factor humus content

Organic Carbon Content In Topsoils In Europe (OCTOP) - ESDAC makes available the Maps of Organic carbon content (%) in the surface horizon of soils in Europe. Resolution: 1 km - Year: 2004. The result is available as a map and an explaining booklet: The Map of Organic Carbon Content In Topsoils In Europe: Version 1.2 September - 2003 (S.P.I.04.72).

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Factor landscape diversity

Shannon eveness index provides information on area composition and richness ranging from 0 to 1. It is calculated considering 9 Corine Land Cover classes of numeric matrices using a moving window algorithm of 5 pixels side and dividing this result by its maximum.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Factor landscape diversity

Shannon eveness index provides information on area composition and richness ranging from 0 to 1. It is calculated considering 9 Corine Land Cover classes of numeric matrices using a moving window algorithm of 5 pixels side and dividing this result by its maximum.

OpenEO

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Oct. 1, 2024, 9:45 a.m. Oct. 1, 2024, 9:45 a.m.

Factor presence of irrigation infrastructure

Permanently irrigated agricultural land is based on the corine land cover 2018 (CLC) from Copernicus. It has been extracted the permanent irrigated class, which is the class 12 in the CLC raster. The output is a binary raster, whereas 1 corresponds for permanent irrigated land and 0 corresponds to not permanent irrigated land.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Factor presence of irrigation infrastructure

Permanently irrigated agricultural land is based on the corine land cover 2018 (CLC) from Copernicus. It has been extracted the permanent irrigated class, which is the class 12 in the CLC raster. The output is a binary raster, whereas 1 corresponds for permanent irrigated land and 0 corresponds to not permanent irrigated land.

OpenEO

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Oct. 1, 2024, 9:45 a.m. Oct. 1, 2024, 9:45 a.m.

Factor slope

Slope derived from EU-DEM version 1.0. year: 2000. For further information visit: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products/slope?tab=metadata

OpenEO

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Oct. 1, 2024, 9:45 a.m. Oct. 1, 2024, 9:45 a.m.

Factor slope

Slope derived from EU-DEM version 1.0. year: 2000. For further information visit: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products/slope?tab=metadata

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Factor soil texture

USDA soil textural classes derived from clay, silt and sand maps. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015- Soil texture is classified into 12 classes: 1: Clay, 2: Silty-Clay, 3: Silty Clay-Loam, 4: Sandy Clay, 5: Sandy Clay-Loam, 6: Clay-Loam, 7: Silt, 8: Silt-Loam, 9: Loam, 10: Sand, 11: Loam Sand, 12: Sandy Loam. For further information visit the website European soil data centre (ESDAC).

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Factor soil texture

USDA soil textural classes derived from clay, silt and sand maps. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015- Soil texture is classified into 12 classes: 1: Clay, 2: Silty-Clay, 3: Silty Clay-Loam, 4: Sandy Clay, 5: Sandy Clay-Loam, 6: Clay-Loam, 7: Silt, 8: Silt-Loam, 9: Loam, 10: Sand, 11: Loam Sand, 12: Sandy Loam. For further information visit the website European soil data centre (ESDAC).

OpenEO

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Oct. 1, 2024, 9:45 a.m. Oct. 1, 2024, 9:45 a.m.

Farmhouses in South Tyrol

Layer to represent the position of eight case study buildings of exemplary energy efficient interventions in historic buildings. All buildings are retrofitted farm houses located in South Tyrol province.

Maps

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April 7, 2021, 2:42 p.m. April 7, 2021, 2:42 p.m.

Farmhouses in South Tyrol tour

Layer to represent the "tour" to eight case study buildings of exemplary energy efficient interventions in historic buildings. All buildings are retrofitted farm houses located in South Tyrol province.

Maps

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April 7, 2021, 2:56 p.m. April 7, 2021, 2:56 p.m.

Forest fires of July 2021 in Sardinia - One week after

On 24 July 2021, a large fire broke out on the Italian island of Sardinia. With strong winds, high temperatures, and dry vegetation, the blaze spread rapidly. In this image, taken about one week after, it is evident the extension area of the fire. credit: produced from ESA remote sensing data

Maps

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Aug. 2, 2021, 12:16 p.m. Aug. 2, 2021, 12:16 p.m.

Forest fires of July 2021 in Sardinia - Two days before

On 24 July 2021, a large fire broke out on the Italian island of Sardinia. With strong winds, high temperatures, and dry vegetation, the blaze spread rapidly. In this image, taken about two days before of the event, is it possible to see the destroyed vegetated area. credit: produced from ESA remote sensing data

Maps

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Aug. 2, 2021, 9:01 a.m. Aug. 2, 2021, 9:01 a.m.

Fragsburg_rgb_flight1_3035

UAV orthophoto of apple orchard maintained by Laimburg Research Centre near Fragsburg for precision mapping of Apple Proliferation

Maps

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Oct. 6, 2021, 11:51 a.m. Oct. 6, 2021, 11:51 a.m.

Gas Prices for Household

Layer about Gas prices components for household consumers - annual data, derived by Eurostat datasets at country level.

Maps

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Feb. 23, 2021, 3:37 p.m. Feb. 23, 2021, 3:37 p.m.

greening_2019

Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

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Dec. 5, 2025, 10:16 a.m. Dec. 5, 2025, 10:16 a.m.

greening_2020

Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

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Dec. 5, 2025, 10:16 a.m. Dec. 5, 2025, 10:16 a.m.

greening_2021

Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

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Dec. 5, 2025, 10:16 a.m. Dec. 5, 2025, 10:16 a.m.

greening_2022

Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

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Dec. 5, 2025, 10:16 a.m. Dec. 5, 2025, 10:16 a.m.

greening_2023

Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

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Dec. 5, 2025, 10:16 a.m. Dec. 5, 2025, 10:16 a.m.

greening_2024

Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

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Nov. 10, 2025, 2:06 p.m. Nov. 10, 2025, 2:06 p.m.

Gross Domestic Product (GDP) - OLD

Layer about Gross Domestic Product prices components for household consumers - annual data, derived by Eurostat datasets at country level.

Maps

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Nov. 27, 2020, 4:14 p.m. Nov. 27, 2020, 4:14 p.m.

Habitat suitability index for red deer in South Tyrol

The Habitat Suitability Index for red deer in South Tyrol was created to model the ecological network for this species. The habitat suitability was resampled to a cell size of 20 m by the bilinear method in ArcGIS. The area of investigation is the administrative boundary of South Tyrol with a 15 km buffer.

Maps

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April 8, 2026, 11:59 a.m. April 8, 2026, 11:59 a.m.

Harmful pollutants and microclimatic parameters from autonomous low-cost sensors deployed in the city center of Bolzano, Italy

O3, NO2, PM1, PM2.5, PM10, temperature, relative humidity, atmospheric pressure and solar radiation low-cost sensors have been co-located with the certified air quality monitoring station of Bolzano - Italy (UTM 46°49′44″ N, 11°34′24″ E, 262 m a.s.l.) managed by the local environmental agency (APPA). The filed campaign is aimed at comparing the low-cost sensors' timeseries to high-resolution instruments, which comply with the protocol for standardized acquisition released by the European Environmental Agency. The core unit of the low-cost sensors acquisition system is a Raspberry Pi 4, which couples to the sensors via the I2C bus. Specifically, the low-cost sensors involved in the campaign are: O3 (Alphasense OX-A431), NO2 (Alphasense NO2-A431) and PM1, 2.5, 10 (Alphasense OPC-N3), Tin/RHin (Sensirion SHT31), Tout/RHout (Galtech PM15PS), atmospheric pressure (Bosch Sensortech BMP388), and SR (Apogee SP 420 Smart).

InfluxDB

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Nov. 7, 2022, 10 a.m. Nov. 7, 2022, 10 a.m.

HDD - NUTS level 0

Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-0 level.

Maps

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Oct. 6, 2020, 2:37 p.m. Oct. 6, 2020, 2:37 p.m.

HDD - NUTS level 2

Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-2 level.

Maps

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Sept. 24, 2020, 3:52 p.m. Sept. 24, 2020, 3:52 p.m.

HEU MODERATE Building Stock Data

The HEU MODERATE Building Stock Data provides information regarding the building stock for all EU27 member states at the national level (i.e., NUTS 0) considering 2020 as the reference year. Regarding the Service Sector, the data distinguishes the following subsectors: single-family houses, multifamily houses, and apartment blocks. Regarding the Service Sector, the data distinguishes the following subsectors: offices, trade, education, health, hotels and restaurants, and other non-residential buildings. Moreover, for each subsector, the data distinguishes the following construction periods: before 1945, 1945-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2010, and 2011-2020. For each building stock subsector and construction period, the data provide information regarding total values at the national level for: - Number of buildings - Number of dwellings - Number of dwellings according to ownership (i.e., owner occupied, rented, social housing) - Number of dwellings according to occupation (i.e., occupied, vacant, secondary houses) - Total constructed area - Total heated area - Total cooled area - Total final energy consumption for space heating and domestic hot water - Total final energy consumption for space cooling Moreover, the following average values for single building characteristics are provided: - Number of floors - Volume-to-surface ratio - Vertical area - Ground area - Window surface - U-values for the different building elements (roof, walls, windows, and floors) - Useful energy demand (ued) differentiating between space heating, domestic hot water, and space cooling - Final energy consumption (fed) differentiating between space heating, domestic hot water, and space cooling Finally, the data provide information about the prevalence of: - Building materials and methodology for the different building elements (roof, walls, windows, and floors) - Different systems used for space heating, domestic hot water, and space cooling The data is provided as a `csv` file (long format with all details and data source) and as an excel file (wide format with separate sheets for each country). Data and a complete description of the available fields can be found at https://github.com/MODERATE-Project/building-stock-analysis/tree/main/T3.2-static-analysis The dataset was obtained by combining information from European and national resources and the review of scientific literature. Data gaps were subsequently filled via statistical modeling.

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June 15, 2023, 11 a.m. June 15, 2023, 11 a.m.

High resolution climatological large ensemble for the Alpine Region

The A-DROP LE is a single model initial condition large ensemble of the Alpine Region. It is based on the Canadian Earth System Model 2 Large Ensemble (CanESM2-LE), that is dynamically downscaled with the Canadian Regional Climate Model (CRCM5) to 12 km spatial resolution (CRCM5-LE) over the European domain (Leduc et al., 2019). It consists of 5 climate variables (air temperature, dew point temperature, precipitation, incoming shortwave radiation and wind speed), that are bias-corrected via MBCn (Cannon, 2018) and statistically downscaled to a spatial resolution of 500 m using the method of Marke, 2008. The temporal resolution is sub-daily (every three hours).

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July 13, 2026, midnight July 13, 2026, midnight

Household Cooking Practices

This Layer shows the share of fuels in the final energy consumption in the residential sector for coocking. The Frequency is annual.

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Dec. 11, 2020, 9:30 a.m. Dec. 11, 2020, 9:30 a.m.

Households Electricity consumption

Layer about Electricity Consumption in Households at nation level. The frequency of data is annual. The dataset is taken from Eurostat dataset.

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Dec. 10, 2020, 2:47 p.m. Dec. 10, 2020, 2:47 p.m.

Households Gas Consumption

Layer about Gas Consumption in Households at nation level. The frequency of data is annual. The dataset is taken from Eurostat dataset.

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Dec. 10, 2020, 3:04 p.m. Dec. 10, 2020, 3:04 p.m.

Household Space Cooling

The Layers shows the share of final energy consumption in the residential sector for space cooling. The frequency of data is annual.

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Dec. 11, 2020, 9:50 a.m. Dec. 11, 2020, 9:50 a.m.

Household Space Heating

The Layers shows the share of fuels in the final energy consumption in the residential sector for space heating. The frequency of data is annual.

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Dec. 11, 2020, 9:58 a.m. Dec. 11, 2020, 9:58 a.m.

Hunsruck_Hochwald_national_park

This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

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July 13, 2026, midnight July 13, 2026, midnight

Hydrological Basins in Africa (Sample record, please remove!)

Major hydrological basins and their sub-basins. This dataset divides the African continent according to its hydrological characteristics. The dataset consists of the following information:- numerical code and name of the major basin (MAJ_BAS and MAJ_NAME); - area of the major basin in square km (MAJ_AREA); - numerical code and name of the sub-basin (SUB_BAS and SUB_NAME); - area of the sub-basin in square km (SUB_AREA); - numerical code of the sub-basin towards which the sub-basin flows (TO_SUBBAS) (the codes -888 and -999 have been assigned respectively to internal sub-basins and to sub-basins draining into the sea)

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July 19, 2000, 2:45 p.m. July 19, 2000, 2:45 p.m.

hydro_station_wtl_ado_32632

Hydrological station with Water level values, collected and harmozied for ADO project.

Maps

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Sept. 28, 2023, 4:15 p.m. Sept. 28, 2023, 4:15 p.m.

IEQ Monitoring Dataset of a Coastal Hotel in Veneto, Italy (Summer–Autumn 2013)

This dataset contains indoor environmental quality (IEQ) monitoring data collected in a hotel located on the Veneto Adriatic coast (Italy) during summer–autumn 2013. Data were collected at 10-minute intervals across 10 indoor zones (8 guest rooms on 4 floors, bar, and restaurant) and one outdoor weather station. Variables include air temperature (°C), relative humidity (%), and illuminance (lux). The dataset is intended as an open benchmark resource for testing IEQ analytics tools, including thermal comfort assessment and anomaly detection algorithms, particularly within the MODERATE platform (Horizon Europe GA 101069834). LICENSE: CC-BY 4.0 DOI: 10.5281/zenodo.20122432 Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: C. Pozza (2026). IEQ Monitoring Dataset of a Coastal Hotel in Veneto, Italy (Summer–Autumn 2013). Zenodo. https://doi.org/10.5281/zenodo.20122432Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

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IEQ monitoring – Residential Apartment, Bolzano, Italy (2026)

IEQ monitoring – Residential Apartment, Bolzano, Italy (2026) Source: SwitchBot app weekly backup ZIP exports. Devices: SwitchBot Meter for temperature & humidity; SwitchBot Smart Radiator Thermostat for the living-room TRV. Sampling interval: ~5 minutes (median). Sensors log on state-change + periodic heartbeat; bursts of readings separated by 1–3 seconds within a polling cycle are rounded/deduped to nearest minute by the pipeline. Period: 2026-02-08 → 2026-05-02 (all seven sensors). LICENSE: CC-BY 4.0 DOI: 10.5281/zenodo.20099209 Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: C. Pozza (2026). IEQ monitoring – Residential Apartment, Bolzano, Italy (2026). Zenodo. https://doi.org/10.5281/zenodo.20099209Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

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Indoor Environmental Quality – Residential Apartment, Bolzano, Italy (2023–2026)

Time-series dataset of indoor environmental quality (IEQ) measurements collected at 5-minute intervals from March 2023 to May 2026 in a south-facing living room (25 m²) of a residential apartment in Bolzano, Italy. Data were acquired using a Netatmo HomeCoach sensor and cover four years of continuous monitoring across four CSV files (one per year). Variables include: timestamp, air temperature (°C), relative humidity (%), CO₂ concentration (ppm), noise level (dB), and atmospheric pressure (hPa). The apartment is equipped with radiators for heating and an air conditioning system for cooling. The dataset is suitable for occupancy pattern analysis, thermal comfort studies, indoor air quality benchmarking, and training/validation of building energy or comfort models. LICENSE: CC-BY 4.0DOI: 10.5281/zenodo.20098783 Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: C. Pozza (2026). Indoor Environmental Quality – Residential Apartment, Bolzano, Italy (2023–2026). Zenodo. https://doi.org/10.5281/zenodo.20098783Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

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Industry, trade and education buildings in Europe

Layer to represent the position of eight case study buildings of exemplary energy efficient interventions in historic buildings. The case studies represent historic buildings with a particular use, such as for industry, trade or education.

Maps

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April 7, 2021, 3:15 p.m. April 7, 2021, 3:15 p.m.

Industry, trade and education buildings in Europe tour

Layer to represent the "tour" to eight case study buildings of exemplary energy efficient interventions in historic buildings. The case studies represent historic buildings with a particular use, such as for industry, trade or education.

Maps

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April 13, 2021, 11:53 a.m. April 13, 2021, 11:53 a.m.

Innovathon MODERATE 2025 - datasets

Materiales para el desarrollo del Innovathon MODERATE en Octubre 2025 en la EPI Gijón Materials for the development of the Innovathon MODERATE in October 2025 at the EPI Gijón

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INTERFACE project UAV meteorological Database

Project: INTERFACE (Euregio Science Fund) Dataset: UAV meteorological data collection Partners: UniTrento, University of Innsbruck Sites: Mezzolombardo Sensors: anemometer (wind), temperature; humidity, pressure, solar radiation Date of data collection: 2025.08.01, 2025.09.18, ongoing

datasets

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April 1, 2025, midnight April 1, 2025, midnight

LagunaOrbetello

This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

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July 13, 2026, midnight July 13, 2026, midnight

Land Surface Temperature - 231m 8 day mean

The Land Surface Temperature (LST) is based on MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The Land Surface Temperature is expressed in degree Celsius.

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July 13, 2026, midnight July 13, 2026, midnight

LEC Location Assessment Dataset – Crevillent (Valencia Region)

This dataset supports the Local Energy Communities (LEC) Location Assessment Tool developed within the MODERATE project (Horizon Europe GA 101069834). It combines cadastral geometry, solar PV potential estimates, sociodemographic indicators, and building stock characteristics at the parcel level for the municipality of Crevillent (Valencia Region, Spain). The dataset was used to train and validate the LEC assessment analytics, enabling identification of optimal areas for the formation of local energy communities based on building morphology, roof-mounted PV capacity, energy demand proxies, and population data. Data sources include the Spanish Cadastre (Sede Electrónica del Catastro) and INE census sections. Tags/Keywords: local energy communities, LEC, solar PV, cadastral data, building stock, Valencia, Spain, energy communities, MODERATE, Horizon Europe, geo-clustering, renewable energy License: CC BY 4.0DOI: 10.5281/zenodo.20121502 Spatial coverage: Municipality of Crevillent, Province of Alicante, Valencia Region, SpainCoordinate reference system: EPSG:25830 (ETRS89 / UTM zone 30N) — geometry column; WGS84 lat/lon also present Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: E. Guillo Sansano (Grupo Enercoop) - 2026. LEC Location Assessment Dataset – Crevillent (Valencia Region). Zenodo. https://doi.org/10.5281/zenodo.20121502Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

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LiDAR-derived Forest Mapping in Mazia valley

This dataset contains forest structural and species information derived from RGB-colored LiDAR point clouds collected by Eurac Research. Using a standardized R workflow (lidR, ForestTools), the LiDAR data were processed through ground classification, height normalization, canopy height model (CHM) generation, individual tree detection, crown segmentation, point-cloud tree assignment, and RGB-based unsupervised species clustering. The result is a set of spatial layers representing tree locations, tree crowns, canopy structure, and species group labels. These data products support forest monitoring, digital twin development, ecological analysis, and modelling of forest dynamics.

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Oct. 22, 2025, 2 a.m. Oct. 22, 2025, 2 a.m.

Lighting and Appliances

This layer describes the energy consumption of lights and appliances on National Level for the households. The frequency of data is annual and it is connected with the Nuts0 Level.

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Feb. 8, 2021, 2:06 p.m. Feb. 8, 2021, 2:06 p.m.

Locali convenzionati

This layer shows 90 % of the locations of bars/restaurants where the Eurac lunchcard can be used. As of: 01.09.2021.

Maps

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Aug. 31, 2021, 4:44 p.m. Aug. 31, 2021, 4:44 p.m.

Localities in Victoria (VMADMIN.LOCALITY_POLYGON) - Comprehensive Elements

This dataset is the definitive set of locality boundaries for the state of Victoria as defined by Local Government and registered by the Registrar of Geographic Names. The boundaries are aligned to Vicmap Property. This dataset is part of the Vicmap Admin dataset series.

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Sept. 11, 2006, midnight Sept. 11, 2006, midnight

Localities in Victoria (VMADMIN.LOCALITY_POLYGON) - Comprehensive Elements

This dataset is the definitive set of locality boundaries for the state of Victoria as defined by Local Government and registered by the Registrar of Geographic Names. The boundaries are aligned to Vicmap Property. This dataset is part of the Vicmap Admin dataset series.

datasets

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Sept. 11, 2006, midnight Sept. 11, 2006, midnight

local_policies

The information provided in this layer gives an overview of the legislation and requirements in each country and shows how they impact the spread of PEB concepts, with the help of a practical example. Different policies and related boundary conditions in each country have a great influence on the successful implementation of plus energy concepts.Therefore, national funding schemes and local policies are analysed in regard to support renewable energy generation in buildings and favour the connection with the electric grid and other district buildings (e.g. direct delivery of power to neighbour buildings, grid feed-in) as well as the local energy market (e.g. energy prices, feed-in tariff) and foreseen developments and environmental aspects.

Maps

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March 19, 2021, 5:36 p.m. March 19, 2021, 5:36 p.m.

Mazia 1150m - Canopy Height Model 24032026

The canopy height model (CHM) of the site F1, part of the long-term monitoring sites of the LTER project. The data is based on a LiDAR drone flight (sensor: Riegl MiniVUX-1UAV). The creation of the CHM is done in R with the package lidR by first normalizing the height of the point cloud and then rasterizing it.

Maps

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March 24, 2026, 2:06 p.m. March 24, 2026, 2:09 p.m.

Mazia 1150m - Crown shapes 24032026

This data set includes crown shapes which are extracted from the Canopy Height Model with the use of the ForestTools package in R. The related CHM and RGB point cloud are included in related resources. In the attributes, the average and mean RGB values of the corresponding segmented tree in the RGB point cloud are included, together with the tree species, treeID, coordinates of the crown tip, tree height and crown area.

Maps

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March 24, 2026, 3:49 p.m. March 24, 2026, 3:50 p.m.

Mazia 1700m - Canopy Height Model 24032026

The canopy height model (CHM) of the site F2, part of the long-term monitoring sites of the LTER project. The data is based on a LiDAR drone flight (sensor: Riegl MiniVUX-1UAV). The creation of the CHM is done in R with the package lidR by first normalizing the height of the point cloud and then rasterizing it.

Maps

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March 24, 2026, 2:25 p.m. March 24, 2026, 2:25 p.m.

Mazia 1700m - Crown shapes 24032026

This data set includes crown shapes which are extracted from the Canopy Height Model with the use of the ForestTools package in R. The related CHM and RGB point cloud are included in related resources. In the attributes, the average and mean RGB values of the corresponding segmented tree in the RGB point cloud are included, together with the tree species, treeID, coordinates of the crown tip, tree height and crown area.

Maps

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March 24, 2026, 3:32 p.m. March 24, 2026, 3:32 p.m.

Mazia 2080m - Canopy Height Model 22102025

The canopy height model (CHM) of the site F5, part of the long-term monitoring sites of the LTER project. The data is based on a LiDAR drone flight (sensor: Riegl MiniVUX-1UAV). The creation of the CHM is done in R with the package lidR by first normalizing the height of the point cloud and then rasterizing it.

Maps

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Oct. 22, 2025, 2:29 p.m. Oct. 22, 2025, 2:30 p.m.

Mazia 2080m - Crown shapes 22102025

This data set includes crown shapes which are extracted from the Canopy Height Model with the use of the ForestTools package in R. The related CHM and RGB point cloud are included in related resources. In the attributes, the average and mean RGB values of the corresponding segmented tree in the RGB point cloud are included, together with the treeID, the coordinates of the crown tip, tree height and crown area.

Maps

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Oct. 22, 2025, 3:35 p.m. Oct. 22, 2025, 3:35 p.m.

MECHANICALLY VENTILATED buildings in SUMMER

In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

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Feb. 8, 2022, 2:03 p.m. Feb. 8, 2022, 2:03 p.m.

MECHANICALLY VENTILATED buildings in WINTER

In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

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Feb. 8, 2022, 2:03 p.m. Feb. 8, 2022, 2:03 p.m.

MERIT Hydro datasets

MERIT Hydro is a global hydrography datasets, developed based on the MERIT DEM and multiple inland water maps. It contains flow direction, flow accumulation, hydrologically adjusted elevations, and river channel width.

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March 1, 2024, 1 a.m. March 1, 2024, 1 a.m.

MERIT Hydro datasets for the European Alps

MERIT Hydro is a global hydrography datasets, developed based on the MERIT DEM and multiple inland water maps. It contains flow direction, flow accumulation, hydrologically adjusted elevations, and river channel width.

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March 1, 2024, 1 a.m. March 1, 2024, 1 a.m.

Meteo stations information

Layer with informations about meteorological stations for Trentino-Alto Adige region, Austria and Switzerland. The layer describe the Climate-Database of Eurac Research that contains meteorological time series of daily temperature (maximum, minimum and mean) and daily total precipitation for more than 250 station sites.

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Microclimatic and Meteorological Data from Orchards in South Tyrol – Project INSTINCT database

The monitoring campaign carried out within the Instinct project aims to analyze the correlation between insect population dynamics and environmental conditions through the collection of microclimatic and meteorological data in various orchards in South Tyrol. Sensors have been installed at several sites: Prato allo Stelvio, Vadena (Laimburg), Naz-Sciaves, Nave San Rocco, and Sinigo. The acquisition system is based on two platforms: STM32WL55JC1, which integrates a microcontroller and LoRa module, and Raspberry Pi Zero for managing meteorological sensors such as the anemometer (ATMOS 22), pyranometer (Hukseflux SR05), radar precipitation sensor (Luff WS100), and illuminance sensor (EKO ML-020-SO). The data collected, together with trap measurements and phenological analyses, will enable the development of predictive models to understand the influence of climatic variables on the presence and evolution of insects in orchards.

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May 1, 2025, 1 a.m. May 1, 2025, 1 a.m.

Mobile Microclimatic Urban Monitoring

Low-cost cloud-connected position-enriched sensors for mobile monitoring of several environmental parameters have been tested in the city of Bolzano (Italy), proving their suitability in identifying the spatial variability of the local climate in relation to the urban morphology, and for highlighting the presence of urban heat island. An exploratory field campaign has been carried out in May 2021 to monitor the diurnal evolution of the microclimate conditions (Tair/RH fields). Data have been acquired performing three sessions during daytime on weekdays: at morning (i.e. 08:30-10:30), noon (i.e. 12:00-14:00), and afternoon (i.e. 16:00-18:00). The measurements have been carried out in 8 days, chosen for the stationary weather conditions (i.e. clear sky and absence of wind). The selected pathway has a length of 9 km, starting and ending at NOI Techpark, crosses the city center and reaches the northern part of the city. It is specifically designed to monitor areas of Bolzano characterized by different land use, urban morphology, and human activities.

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Dec. 22, 2023, 12:46 p.m. Dec. 22, 2023, 12:46 p.m.

MOD10A1 daily snow cover fraction for the Senales valley

This data set provides a daily snow cover fraction derived from the "MODIS/Terra Snow Cover 5-Min L2 Swath 500m" data set (DOI:10.5067/MODIS/MOD10_L2.061). The dataset is reprojected and regridded to a specific area of interest (Senales catchment). The original MODIS snow-cover products provide Normalized Difference Snow Index (NDSI) values from 0 – 100. NDSI is then converted to snow cover fraction (SCF) from 0 – 100 (Salomonson and Appel, 2004; Riggs et al., 2019). Clouds are marked as 205.

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Jan. 1, 2016, 1 a.m. Feb. 1, 2023, 1 a.m.

MODERATE Solar Cadastre Dataset

This dataset contains building and rooftop characteristics for the city of Crevillent , Spain, together with estimated photovoltaic (PV) potential indicators, including suitable roof area, installable PV capacity, expected yearly electricity production, specific PV yield, and potential CO₂ emission savings. The PV potential assessment combines building footprints, annual irradiance data, terrain geometry, and shading analysis using a digital elevation model (DEM), while PV performance is derived from PVGIS and processed with PVLIB. Only technically and economically feasible rooftop areas are included. The dataset includes the following entries: Column Explanation ID Unique identifier of the building or roof section b_area Building or roof subsection area used for the calculation (in m²) p_area Total parcel or roof area associated with the building (m²) building_u Building use / building category year_cons Year of construction floors Number of floors of the building dwellings Number of residential units/apartments r_typology Residential typology thermal_ne Thermal need – Annual thermal energy demand of the building parcel, in MWh/year area_conv Roof area convenient/suitable for PV installation (m²) – this area is used for PV calculations PV_nominal Estimated installed PV peak power (kWp) PV_potential Estimated yearly PV electricity production (kWh/year) average_yi Specific PV yield (kWh/kWp/year) potentialC… Potential annual CO₂ emissions avoided (kg CO₂/year) centroid_l Latitude of building centroid centroid_1 Longitude of building centroid centroid_x X coordinate in projected coordinate system centroid_y Y coordinate in projected coordinate system Derivation of the dataset: The dataset combines building footprints with a spatial annual irradiance raster (e.g., a local solar‑cadastre / processed irradiance layer) and a digital elevation model (DEM) to capture site geometry and shading; hourly PV performance and solar geometry come from PVGIS [1] and are processed with PVLIB [2] for accurate sun positions; PVGIS hourly profiles are horizon‑corrected (using the DEM) and then scaled to match the raster’s annual totals. Finally, a set of internal technical and economic assumptions (module efficiency, performance ratio, self‑consumption/export tariffs, investment cost, discount rate, lifetime, etc.) converts energy into annual revenue and Net Present Value (NPV), and only pixels with positive NPV are considered feasible—producing per‑building feasible area, nominal capacity, annual generation (post‑shading, raster‑calibrated), specific yield, and estimated CO₂. [1] Photovoltaic Geographical Information System (PVGIS), European Commission Joint Research Centre. Available online: https://joint-research-centre.ec.europa.eu/photovoltaic-geographical-information-system-pvgis_en [2] Anderson, K., Hansen, C., Holmgren, W., Jensen, A., Mikofski, M., and Driesse, A. “pvlib python: 2023 project update.” Journal of Open Source Software, 8(92), 5994, (2023). DOI: 10.21105/joss.05994.

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MODERATE Synthetic energy consumption profiles

Synthetic dataset with 500 users hourly profile of energy metering data. The original training dataset contains both residential and non residential buildings, from 5000 users. Meaning of the columns: A+: Active energy consumed from the grid (Wh) A-: Active energy exported to the grid (Wh) R1: Consumed inductive reactive energy R2: Generated inductive reactive energy R3: Consumed cpacitive reactive energy R4: Generated capacitive reactive energy NOTE: The two variants "cpu" and "xpu" do not differ statistically from each other. They are both result obtained by MOSTLY.AI library running on a local instance, run with pytorch in the CPU (multi-thread) mode, vs a XPU pytorch run. Specific statistical metrics shows that the synthetic dataset is significantly different from original profiles. While daily and weekly patterns are maintained from the synthetization process set up, in this first release the seasonal and monthly patterns are not represented in the synthetic dataset.

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MODERATE Training dataset for synthetic load profile generator

Source: Fluvius Open Data portal Original dataset: Fluvius 2,400 digital meter profiles Description: This dataset contains 15-minute smart meter electricity readings from 2,400 anonymised residential customers connected to the Fluvius distribution network in Flanders, Belgium, covering a full calendar year. Each profile is labelled according to the presence of distributed energy technologies: photovoltaic (PV) systems, electric vehicles (EV), heat pumps (HP), or none of these. For this project, the dataset was resampled to hourly resolution and a subset of 1,300 profiles was selected, resulting in a matrix of 1,300 columns × 8,760 hourly values representing electricity consumption per building. Labels for each profile are provided in a separate indicator file. Use: The dataset was used as the basis for synthetic load profile generation within the MODERATE project.

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MODIS First Snow Day 500m

First Snow Day (FSD) represents the first date in the hydrological year with snow presence

STAC

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July 13, 2026, midnight July 13, 2026, midnight

MODIS Last Snow Day 500m

Last Snow Day (LSD) represents the last date in the hydrological year with snow presence

STAC

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July 13, 2026, midnight July 13, 2026, midnight

MODIS Leaf Area Index

The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies.

STAC

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Jan. 1, 2020, 1 a.m. Dec. 1, 2020, 1 a.m.

MODIS Snow Cover Area 500m

The yearly snow cover area derived derived from daily MOD10A1 images

STAC

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July 13, 2026, midnight July 13, 2026, midnight

MODIS Snow Cover Duration 500m

The yearly snow cover duration derived from daily MOD10A1 images indicationg the number of days with snow presence in one hydrological year

STAC

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July 13, 2026, midnight July 13, 2026, midnight

MODIS SNOW map over the ALPS

The collection contains binary snow cover maps covering the entire Alpine Arc. The maps have a daily frequency and a ground resolution of 250 m. They are derived from daily MODIS observations (Aqua and Terra) for the entire Alps arc. The input data are the atmospherically-corrected reflectances of MODIS MOD09GQ, MOD09GA for tile h19v04 and h18v04. The map has two bands - SNOW MAP: Snow cover classification map with four classes [0 = NO DATA - missing or corrupt data of one or more input bands; 1 = SNOW - pixel covered by snow; 2 = NO_SNOW - pixel not covered by snow; 3 = CLOUD - pixel covered by clouds; 4 = OCEAN WATER - pixel over sea and ocean; 5 = INLAND WATER - pixel over lakes and rivers], QUALITY FLAG: Snow cover quality map [NO DATA - missing or corrupt data of one or more input bands; QUALITY_INDEX - higher values indicate higher likeliness of correct classification].

OpenEO

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Oct. 1, 2024, 9:52 a.m. Oct. 1, 2024, 9:52 a.m.

MODIS SNOW map over the ALPS

The collection contains binary snow cover maps covering the entire Alpine Arc. The maps have a daily frequency and a ground resolution of 250 m. They are derived from daily MODIS observations (Aqua and Terra) for the entire Alps arc. The input data are the atmospherically-corrected reflectances of MODIS MOD09GQ, MOD09GA for tile h19v04 and h18v04. The map has two bands - SNOW MAP: Snow cover classification map with four classes [0 = NO DATA - missing or corrupt data of one or more input bands; 1 = SNOW - pixel covered by snow; 2 = NO_SNOW - pixel not covered by snow; 3 = CLOUD - pixel covered by clouds; 4 = OCEAN WATER - pixel over sea and ocean; 5 = INLAND WATER - pixel over lakes and rivers], QUALITY FLAG: Snow cover quality map [NO DATA - missing or corrupt data of one or more input bands; QUALITY_INDEX - higher values indicate higher likeliness of correct classification].

STAC

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July 13, 2026, midnight July 13, 2026, midnight

MONALISA - SOS timeseries

Stations and timeseries of the MONALISA-SOS service. Environmental timeseries collected in the SouthTyrol province by the MONALISA project partners. Query the features and click the link to view last values collected

Maps

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Jan. 28, 2021, 11:14 a.m. Jan. 28, 2021, 11:14 a.m.

MonteBondone

This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Monthly climatologies - Climate Data Base

The dataset contains the 1981 – 2010 monthly climatologies of mean, minimum and maximum temperature and total precipitation for more than 250 locations in Trentino – South Tyrol. They were derived from the observation records of the regional meteorological network after checking all series for quality and homogeneity. Climatologies (or normals) are the mean monthly values computed over a 30-years reference interval and they represent the mean local climatic conditions. The CDB was built in the framework of the Use-Case number 8 of the DPS4ESLAB project.

PostgresDB

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Jan. 15, 2020, midnight Jan. 15, 2020, midnight

Monthly damages LATEST

Mapping of the forest changes occurring in 2020, 2021, 2022, 2023 and 2024 at a monthly scale. The outputs shown here are based on the analysis of Sentinel 2 time series. The date corresponds to the first date at which a change was detected.

Maps

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Nov. 7, 2024, 1:48 p.m. Nov. 7, 2024, 1:48 p.m.

Multi-Family Residential Building – Hourly Energy and IEQ Monitoring Time Series - Bolzano area, Italy (2006–2009)

This dataset contains three years of hourly monitored data from a multi-family residential building located in the Bolzano area, South Tyrol, Italy (46°N, Alpine climate). It covers the period from 1 July 2006 to 21 July 2009 and provides continuous time series combining energy consumption measurements with indoor environmental quality (IEQ) indicators and meteorological context variables. The dataset is released as original monitored data: no outlier removal, smoothing, or gap-filling has been applied. A number of anomalous values and atypical operational trends have been intentionally preserved. This makes the dataset well-suited for benchmarking and stress-testing anomaly detection algorithms, fault detection and diagnostics (FDD) tools, and data quality assessment pipelines in the building energy domain. Note: Temperature profiles are obtained after averaging on similar profiles from 8 selected apartments. The energy consumption is the aggregated value at building level (same apartments). Variable Description Unit Min Max Mean Electricity Hourly electrical energy consumption kWh 0.00 78.00 1.62 CO₂ concentration Indoor CO₂ concentration (occupancy/ventilation proxy) ppm 452 1908 832.71 Internal temperature Indoor air temperature °C 12.60 31.20 24.27 Thermal energy consumption Hourly thermal energy (heating/DHW) kWh 0.00 27.00 5.98 External global radiation Horizontal global solar irradiance W/m² 0.00 1035.00 151.95 Outside temperature Outdoor ambient air temperature °C −8.10 38.30 13.33 Note: min, max, and mean are computed over all 25,699 records. No missing values are present. Extreme values (e.g., electricity max 78 kWh/h; CO₂ max 1,908 ppm) reflect both real operational peaks and intentionally retained anomalous readings. Users should not treat these values as errors before understanding the anomaly detection context of this release. Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: M. Castagna, C. Pozza (2026). Multi-Family Residential Building – Hourly Energy and IEQ Monitoring Time Series - Bolzano area, Italy (2006–2009). Zenodo. https://doi.org/10.5281/zenodo.20096697Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

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NATURALLY VENTILATED buildings in SUMMER

In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

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Feb. 8, 2022, 2:03 p.m. Feb. 8, 2022, 2:03 p.m.

NATURALLY VENTILATED buildings in WINTER

In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

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Feb. 8, 2022, 2:04 p.m. Feb. 8, 2022, 2:04 p.m.

Normalized Difference Vegetation Index - 231m 8 day Maximum Value Composite

The Normalized Difference Vegetation Index (NDVI) is based on MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The NDVI is calculated using the formula NDVI = (NIR - Red) / (NIR + Red). The NDVI expresses the vitality of vegetation. The data is provided as 8 day measures. The time series is starting from 2001. The NDVI values range from -1 - 1, whereas high values correspond to healthy vegetation.

OpenEO

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Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

Normalized Difference Vegetation Index - 231m 8 day Maximum Value Composite

The Normalized Difference Vegetation Index (NDVI) is based on MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The NDVI is calculated using the formula NDVI = (NIR - Red) / (NIR + Red). The NDVI expresses the vitality of vegetation. The data is provided as 8 day measures. The time series is starting from 2001. The NDVI values range from -1 - 1, whereas high values correspond to healthy vegetation.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Nuts2 alpinespace-Eusalp intersection

The Alpine Drought Observatory - ADO project is interested in discharge (only for stations with a catchment area > 1000 Km2 and currently active), groundwater (only for stations for major groundwater bodies), and major lake levels (only for major water bodies (surface > 5 km2)) data. The overall objective of the Alpine Drought Observatory - ADO project is to create an online drought monitoring platform and develop policy implementation guidelines for proactive drought management in the Alpine regions. The ADO project consortium includes 11 institutions from 6 Alpine countries with a wide range of expertise, covering meteorological and hydrological monitoring, specific knowledge on modeling, drought risk and impact assessment, as well as water governance in the different sectors. Further information about the ADO project can be found here: https://www.alpine-space.eu/projects/ado/en/about.

Maps

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Nov. 11, 2021, 3:42 p.m. Nov. 11, 2021, 3:42 p.m.

nuts2_simplified

NUTS region, level 2 for the EUSALP area. The border are simplified respect to the original data source to get a lighter version.

Maps

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Oct. 27, 2021, 11:34 a.m. Oct. 27, 2021, 11:34 a.m.

Occupant Behaviour Modelling

This layer aims at creating a knowledge base related to research on Occupant Behaviour Modelling. The main Cultural-E contribution referrers to the translation into a GIS format of the provision of open-access available review tables focused on occupants' perception and behaviour in buildings (OPA)(*). In contrast to tables often found in supplementary materials, which are static, the tables used are dynamically growing with new evidence appearing in scientific literature. Authors of original research are welcome to add their published and peer-reviewed research items to these tables. (*) Schweiker M, Andersen RK, Berger C, Carlucci S, Chinazzo G, Edappilly LP, Favero M, Mahdavi A, Piselli C, Bourikas L, Hong T, Dong B, Syndicus M and Hahn J (2021) Dynamic review tables for topical reviews on occupants’ perception and behaviour in buildings. OSF. Available at: osf.io/gnvp2

Maps

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Feb. 11, 2022, 5 p.m. Feb. 11, 2022, 5 p.m.

Ospitaletto District Heating Expansion – Building Heat Demand and Network Dataset

This dataset provides georeferenced building-level annual heating demand estimates and district heating network geometry for the municipality of Ospitaletto (Brescia, Lombardy, Italy), developed to assess expansion opportunities for an existing 5th Generation District Heating (5GDHC) network. The building heat demand layer (Ospitaletto_heat.gpkg) covers 1,957 buildings with 17 attributes including footprint area, building height and volume, S/V ratio, functional use (residential/non-residential), INSPIRE-based typological class (SFH, s-MFH, l-MFH), estimated annual heating demand (MWh/year), distance to the DH network, and priority selection flags under two criteria. The DH network layer (DH_ospitaletto_network.gpkg) contains 21 pipe segments of the existing network. Both layers use the EPSG:3035 coordinate reference system. Heating demand was estimated through a data-fusion methodology combining building geometry (OpenStreetMap, cadastral data), climate inputs (Heating Degree Days, Southern Continental zone), energy consumption density benchmarks (INSPIRE for residential, Hotmaps for non-residential), and available metered consumption data for calibration. Network expansion priorities were assessed using two criteria: Criterion A selects buildings by maximizing the ratio of annual demand to distance from the network (kWh/y/m); Criterion B applies a 250 m buffer around the existing network and filters by minimum demand threshold (100 kWh/y). Produced within the MODERATE project (Horizon Europe Grant Agreement No. 101069834).

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Path simulation

Simulation for the START project of the path of 3 person in the Park site. In the future this simulation will be replaced by a near real time updated layer. You can see on the map different position at different time using the arrow of the timeline tool on the map.

Maps

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Oct. 5, 2020, 4:57 p.m. Oct. 5, 2020, 4:57 p.m.

PEB_guidelines

This layer provides exemplary national initiatives and guidelines materials for the design and experimentation for high standards of energy efficient buildings such as PEB, ZEB and NZEB. The different national initiatives referenced intends to represent the 4 EU climates which are object of study of CULTURAL-E.

Maps

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March 17, 2021, 3:22 p.m. March 17, 2021, 3:22 p.m.

Physiographic Map of North and Central Eurasia (Sample record, please remove!)

Physiographic maps for the CIS and Baltic States (CIS_BS), Mongolia, China and Taiwan Province of China. Between the three regions (China, Mongolia, and CIS_BS countries) DCW boundaries were introduced. There are no DCW boundaries between Russian Federation and the rest of the new countries of the CIS_BS. The original physiographic map of China includes the Chinese border between India and China, which extends beyond the Indian border line, and the South China Sea islands (no physiographic information is present for islands in the South China Sea). The use of these country boundaries does not imply the expression of any opinion whatsoever on the part of FAO concerning the legal or constitutional states of any country, territory, or sea area, or concerning delimitation of frontiers. The Maps visualize the items LANDF, HYPSO, SLOPE that correspond to Landform, Hypsometry and Slope.

Maps

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Oct. 1, 1999, midnight Oct. 1, 1999, midnight

Pilot Working Areas

BeyondSnow Pilot Working Areas 

Maps

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Feb. 20, 2025, 11:59 a.m. Feb. 20, 2025, 11:59 a.m.

PlanToConnect lcp: regional linkages and distance local linkages

This layer shows the Least Cost Path (LCP), defining regional linkages and linkages less than 2.5 km.  “A Least-Cost-Path is defined as the pathway that offers the least resistance to an animal moving from one patch to another (Cushman et al., 2013) and is represented as the linear element (least-cost pathway) that connects two patches.” (Lumia et al., 2023) File name: LCP_Regional_Linkages_and_distance_local_linkages.shp Project website: https://www.alpine-space.eu/project/plantoconnect/

Maps

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Aug. 20, 2024, 4:55 p.m. Aug. 20, 2024, 4:55 p.m.

PlanToConnect: potential ecological network EUSALP

This data set shows regional potential ecological network in EUSALP areas. Filename: PlanToConnect_Potential_ecological_network_EUSALP.shp Project website: https://www.alpine-space.eu/project/plantoconnect/

Maps

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Aug. 1, 2024, 3:53 p.m. Aug. 1, 2024, 3:53 p.m.

Population Density

The ratio between the annual average population and the land area. The land area concept (excluding inland waters) should be used wherever available; if not available then the total area, including inland waters (area of lakes and rivers) is used. The frequency is annual.

Maps

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Nov. 27, 2020, 5:09 p.m. Nov. 27, 2020, 5:09 p.m.

Precipitation Anomalies - ERA5_QM REL_RR-1

Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

OpenEO

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Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Precipitation Anomalies - ERA5_QM REL_RR-1

Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Precipitation Anomalies - ERA5_QM REL_RR-12

Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

OpenEO

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Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Precipitation Anomalies - ERA5_QM REL_RR-12

Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Precipitation Anomalies - ERA5_QM REL_RR-2

Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Precipitation Anomalies - ERA5_QM REL_RR-2

Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

OpenEO

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Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Precipitation Anomalies - ERA5_QM REL_RR-3

Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

OpenEO

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Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Precipitation Anomalies - ERA5_QM REL_RR-3

Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Precipitation Anomalies - ERA5_QM REL_RR-6

Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Precipitation Anomalies - ERA5_QM REL_RR-6

Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

OpenEO

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Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

PV classification results for the MODERATE project

The csv files contain the results of an image classifier, trained to classify images of rooftops of having or not having PV. It was used on the regions Valencia and Bolzano. The IDs are the building identifiers from Open Street Maps and the prediction classifies each building as having a PV (1) or not having a PV (0). In the respective .json file for each region the latitude and longitude are saved for each Open Street Maps ID using EPSG 3035.

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Report on existing monitoring platforms and potential data for the integration into ADO

The purpose of this report is to prepare a foundation for this working process. An overview of the state of drought observation and detection in the Alpine region has been prepared, which will also serve as basis for its upgrade or extension to the entire Alpine area. The state of the art analysis prepared by project partners (at least one per participating country) was conducted by uniform questionnaire that was circulated among ADO project partners covering four topics: drought monitoring approach, drought-information platforms, the regional & EU drought monitoring approach, and potential drought indices in ADO.

datasets

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Dec. 19, 2023, 8 a.m. Dec. 19, 2023, 8 a.m.

RT1 Surface Soil Moisture

A experimental dataset of soil-moisture retrievals at 1km effective spatial resolution for all 28 basins of the Mediterranean study-area. The data was processed via the RT1 radiative transfer model using Sentinel-1 (VV) backscatter data together with auxiliary CGLS Leaf Area Index timeseries.

STAC

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July 13, 2026, midnight July 13, 2026, midnight

SCF_binary

Results for batch job j-2406072110944ac9ae59a2fb48d47a10 (SCF_Binary Map CDSE)

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Sciliar_Catinaccio

This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Sensitivity indicator

Sensitivity indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow project

Maps

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SENTINEL2_MOSAIC_20170601_20170930_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2017. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

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July 14, 2025, 4:31 p.m. July 14, 2025, 4:31 p.m.

SENTINEL2_MOSAIC_20170601_20170930_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2017. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

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July 14, 2025, 4:31 p.m. July 14, 2025, 4:31 p.m.

SENTINEL2_MOSAIC_20180601_20180930_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2018. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

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July 14, 2025, 4:48 p.m. July 14, 2025, 4:48 p.m.

SENTINEL2_MOSAIC_20180601_20180930_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2018. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

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July 14, 2025, 4:48 p.m. July 14, 2025, 4:48 p.m.

SENTINEL2_MOSAIC_20190601_20190930_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2019. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

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July 14, 2025, 4:48 p.m. July 14, 2025, 4:48 p.m.

SENTINEL2_MOSAIC_20190601_20190930_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2019. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

View
July 14, 2025, 4:48 p.m. July 14, 2025, 4:48 p.m.

SENTINEL2_MOSAIC_20200601_20200930_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2020. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

View
July 14, 2025, 4:48 p.m. July 14, 2025, 4:48 p.m.

SENTINEL2_MOSAIC_20200601_20200930_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2020. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

View
July 14, 2025, 4:48 p.m. July 14, 2025, 4:48 p.m.

SENTINEL2_MOSAIC_20210601_20210930_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2021. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

View
July 14, 2025, 4:48 p.m. July 14, 2025, 4:48 p.m.

SENTINEL2_MOSAIC_20210601_20210930_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2021. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

View
July 14, 2025, 4:48 p.m. July 14, 2025, 4:48 p.m.

SENTINEL2_MOSAIC_20220601_20220930_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2022. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

View
July 14, 2025, 4:48 p.m. July 14, 2025, 4:48 p.m.

SENTINEL2_MOSAIC_20220601_20220930_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2022. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

View
July 14, 2025, 4:48 p.m. July 14, 2025, 4:48 p.m.

SENTINEL2_MOSAIC_20230601_20230930_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2023. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

View
July 16, 2025, 9:56 a.m. July 16, 2025, 9:56 a.m.

SENTINEL2_MOSAIC_20230601_20230930_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2023. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

View
July 16, 2025, 11:54 a.m. July 16, 2025, 11:54 a.m.

SENTINEL2_MOSAIC_20240601_20240930_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2024. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

View
July 16, 2025, 11:55 a.m. July 16, 2025, 11:55 a.m.

SENTINEL2_MOSAIC_20240601_20240930_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2024. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

View
July 16, 2025, 11:55 a.m. July 16, 2025, 11:55 a.m.

SENTINEL2_MOSAIC_20250601_20250930_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2025. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

View
Sept. 30, 2025, 3:13 p.m. Sept. 30, 2025, 3:13 p.m.

SENTINEL2_MOSAIC_20250601_20250930_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2025. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

View
Sept. 30, 2025, 3:16 p.m. Sept. 30, 2025, 3:16 p.m.

SENTINEL2_MOSAIC_20260501_20260611_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within May and June 2026. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

View
June 1, 2026, 2 a.m. Sept. 30, 2026, 2 p.m.

SENTINEL2_MOSAIC_20260501_20260611_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within May and June 2026. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

View
June 1, 2026, 2 a.m. Sept. 30, 2026, 2 p.m.

SENTINEL2_MOSAIC_20260601_20260719_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and July 2026. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

View
June 1, 2026, 2 a.m. July 19, 2026, 2 p.m.

SENTINEL2_MOSAIC_20260601_20260719_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and July 2026. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

View
June 1, 2026, 2 a.m. July 19, 2026, 2 p.m.

Sentinel-2 tiny sample collection for testing purposes.

Tiny sample Sentinel-2 data collection, generated using openEO, stored initially as netCDF and splitted later into separate COGs (one for each band and each date) for more interoperability.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

Sentinel-2 tiny sample collection for testing purposes.

Tiny sample Sentinel-2 data collection (different dimension names for temporal (time) and bands (band) compared to the SENTINEL2_L2A_SAMPLE collection), generated using openEO, stored initially as netCDF and splitted later into separate COGs (one for each band and each date) for more interoperability.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

SENWISE Input LSTM Representative Dataset

Representative bands for the Land Surface Temperature Monitoring consolidated within the SENWISE initiative, gathered from sources as Sentinel-2, Sentinel-3 enhanced at high resolution, and simulation alogrithms

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

Snow cover phenology

The snow cover phenology dataset was generated by exploiting the snow product MOD10A1.061 and contains the following variables: first snow day (FSD), last snow day (LSD), yearly averaged snow cover area (SCA), snow cover duration (SCD).

STAC

View
June 14, 2023, 10 a.m. June 14, 2023, 10 a.m.

Snow depth - Venosta Valley

Daily snow depth [mm] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

OpenEO

View
Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

Snow depth - Venosta Valley

Daily snow depth [mm] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

Snow Water Equivalent-1000m

A 1km experimental dataset for the Mediterranean terrestrial region of Snow Water Equivalent from the assimilation of Sentinel-1 C-SNOW data into the S3M model

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

SoilGrids data for the European Alps

SoilGrids is a system for global digital soil mapping that uses state-of-the-art machine learning methods to map the spatial distribution of soil properties across the globe. SoilGrids prediction models are fitted using over 230 000 soil profile observations from the WoSIS database and a series of environmental covariates. Covariates were selected from a pool of over 400 environmental layers from Earth observation derived products and other environmental information including climate, land cover and terrain morphology. The outputs of SoilGrids are global soil property maps at six standard depth intervals (according to the GlobalSoilMap IUSS working group and its specifications) at a spatial resolution of 250 meters.

STAC

View
March 1, 2020, 1 a.m. March 1, 2020, 1 a.m.

Soil moisture and temperature datasets in LIDO orchard

Soil moisture and temperature timeserie datasets of the apple orchard located in the Laimburg Integrated Digital Orchard in Bolzano province. The timeseries collects data about soil moisture and temperature at 20 cm, 30 cm and 40 cm under the soil surface in 3 different position of the orchard. Acquisition frequency is 30 minutes for all the parameters.

InfluxDB

View
Oct. 19, 2023, 11 a.m. Oct. 19, 2023, 11 a.m.

Soil Moisture Anomalies - ERA5_QM

The Soil Moisture Anomaly is a drought indicator used to detect and monitor agricultural drought conditions, defined by a prolonged period of deficit in the availability of soil moisture to plants. The soil moisture anomalies provided as part of the Alpine Drought Observatory was derived from ERA5 Volumetric Soil Water Layers at different depths with Layer 1 (0-7cm), Layer 2 (7-28cm), Layer 3 (28–100cm), and Layer 4 (100-289cm). The input ERA5 soil moisture dataset was downscaled using a quantile mapping approach. Daily anomalies were calculated using a climatological mean and standard deviation of soil moisture from a smoothed time-series (running mean on a 10-day window) for a reference period of 1981–2020. The Soil Moisture Anomalies values range from -5 to +5, with negative values indicating drier than conditions while positive values indicate wetter than normal conditions, and -1 to +1 values indicates near-normal conditions. Datasets contains modified Copernicus Climate Change Service Information [1980–current year]; contains modified Copernicus Atmosphere Monitoring Service Information [1980-current year]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

STAC

View
Dec. 31, 1979, 1 p.m. None

Soil Moisture Anomalies - ERA5_QM

The Soil Moisture Anomaly is a drought indicator used to detect and monitor agricultural drought conditions, defined by a prolonged period of deficit in the availability of soil moisture to plants. The soil moisture anomalies provided as part of the Alpine Drought Observatory was derived from ERA5 Volumetric Soil Water Layers at different depths with Layer 1 (0-7cm), Layer 2 (7-28cm), Layer 3 (28–100cm), and Layer 4 (100-289cm). The input ERA5 soil moisture dataset was downscaled using a quantile mapping approach. Daily anomalies were calculated using a climatological mean and standard deviation of soil moisture from a smoothed time-series (running mean on a 10-day window) for a reference period of 1981–2020. The Soil Moisture Anomalies values range from -5 to +5, with negative values indicating drier than conditions while positive values indicate wetter than normal conditions, and -1 to +1 values indicates near-normal conditions. Datasets contains modified Copernicus Climate Change Service Information [1980–current year]; contains modified Copernicus Atmosphere Monitoring Service Information [1980-current year]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

OpenEO

View
Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

Solar energy in alpine historic buildings

Layer to represent the position of seven case study buildings of exemplary energy efficient interventions in historic buildings. In all buildings, a photovoltaic or solar thermal system was integrated as one of the renovation measures.

Maps

View
April 7, 2021, 11:09 a.m. April 7, 2021, 11:09 a.m.

Solar energy in alpine historic buildings tour

Layer to represent the "tour" to seven case study buildings of exemplary energy efficient interventions in historic buildings. In all buildings, a photovoltaic or solar thermal system was integrated as one of the renovation measures.

Maps

View
April 7, 2021, 11:11 a.m. April 7, 2021, 11:11 a.m.

SOUTH TYROL: impacts of landslide events (IFFI)

Impacts of landslides events in South Tyrol (IT) taken from the IFFI (Inventory of Landslide Phenomena in Italy) database [last update 18 Nov 2022].

Maps

View
Dec. 2, 2022, 6:48 p.m. Dec. 2, 2022, 6:48 p.m.

SOUTH TYROL: Traffic Report (current situation)

Accumulated records of the traffic situation over the roads of South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

View
Nov. 23, 2021, 6:07 p.m. Nov. 23, 2021, 6:07 p.m.

SOUTH TYROL: Traffic Report (mountain roads and passes)

Accumulated records of traffic events over mountain roads and passes in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

View
Nov. 23, 2021, 6:07 p.m. Nov. 23, 2021, 6:07 p.m.

SOUTH TYROL: Traffic Report (neighbouring countries)

Accumulated records of traffic events related to border areas in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

View
Nov. 23, 2021, 6:07 p.m. Nov. 23, 2021, 6:07 p.m.

SOUTH TYROL: Traffic Report (public transports)

Accumulated records of traffic events related to public transports in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

View
Nov. 23, 2021, 6:07 p.m. Nov. 23, 2021, 6:07 p.m.

SOUTH TYROL: Traffic Report (road works and locks)

Accumulated records of works and locks over the roads of South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

View
Nov. 5, 2021, 6:17 p.m. Nov. 5, 2021, 6:17 p.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-1

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-1

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-12

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-12

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-2

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-2

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-3

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-3

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-6

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-6

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Precipitation Index - ERA5_QM SPI-1

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Precipitation Index - ERA5_QM SPI-1

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Precipitation Index - ERA5_QM SPI-12

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Precipitation Index - ERA5_QM SPI-12

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Precipitation Index - ERA5_QM SPI-2

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Precipitation Index - ERA5_QM SPI-2

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Precipitation Index - ERA5_QM SPI-3

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Precipitation Index - ERA5_QM SPI-3

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Precipitation Index - ERA5_QM SPI-6

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Precipitation Index - ERA5_QM SPI-6

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Snow Pack Index - ERA5_QM SSPI-10

The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:50 a.m. Oct. 1, 2024, 9:50 a.m.

Standardised Snow Pack Index - ERA5_QM SSPI-10

The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Snow Pack Index - ERA5_QM SSPI-30

The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

View
Dec. 31, 1978, 1 p.m. None

Standardised Snow Pack Index - ERA5_QM SSPI-30

The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

View
Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

ST_GRIDDED_TIME_SERIES_PRECIPITATION

The product contains the gridded daily series of mean precipitation at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. The climatologies refer to the averages over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

ST_GRIDDED_TIME_SERIES_PRECIPITATION

The product contains the gridded daily series of mean precipitation at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. The climatologies refer to the averages over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

OpenEO

View
Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

ST_GRIDDED_TIME_SERIES_TEMPERATURE

The product contains the gridded daily series of mean temperature at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset currently spans the period 1980 – 2020, but it is expected to be regularly updated. It was obtained by applying an anomaly-based interpolation to the observations of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All station series used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. Mean temperature was here defined as the daily average of maximum and minimum temperature. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

OpenEO

View
Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

ST_GRIDDED_TIME_SERIES_TEMPERATURE

The product contains the gridded daily series of mean temperature at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset currently spans the period 1980 – 2020, but it is expected to be regularly updated. It was obtained by applying an anomaly-based interpolation to the observations of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All station series used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. Mean temperature was here defined as the daily average of maximum and minimum temperature. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

ST_MONTHLY_GRIDDED_CLIMATOLOGIES_PRECIPITATION

The product contains the gridded climatologies of monthly total precipitation for Trentino – South Tyrol for the period 1981–2010. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database:https://edp-portal.eurac.edu/cdb_doc/. The climatologies refer to the averages over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

OpenEO

View
Oct. 1, 2024, 9:46 a.m. Oct. 1, 2024, 9:46 a.m.

ST_MONTHLY_GRIDDED_CLIMATOLOGIES_PRECIPITATION

The product contains the gridded climatologies of monthly total precipitation for Trentino – South Tyrol for the period 1981–2010. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database:https://edp-portal.eurac.edu/cdb_doc/. The climatologies refer to the averages over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

ST_MONTHLY_GRIDDED_CLIMATOLOGIES_TEMPERATURE

The product contains the gridded climatologies of monthly mean temperature for Trentino – South Tyrol for the period 1981–2010. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database:https://edp-portal.eurac.edu/cdb_doc/. Mean temperature was here defined as the average of maximum and minimum temperature. The climatologies represent the mean values over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021).

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

ST_MONTHLY_GRIDDED_CLIMATOLOGIES_TEMPERATURE

The product contains the gridded climatologies of monthly mean temperature for Trentino – South Tyrol for the period 1981–2010. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database:https://edp-portal.eurac.edu/cdb_doc/. Mean temperature was here defined as the average of maximum and minimum temperature. The climatologies represent the mean values over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021).

OpenEO

View
Oct. 1, 2024, 9:46 a.m. Oct. 1, 2024, 9:46 a.m.

Süd-Ago-Ost: Betweenness centrality on OSM drivable roads

Betweenness centrality topologic indicator calculated on the OSM drivable roads over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

View
Sept. 24, 2021, 12:30 p.m. Sept. 24, 2021, 12:30 p.m.

Süd-Ago-Ost: Hospitals accessibility on OSM drivable roads

Hospital accessibility indicator calculated on the OSM drivable roads over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

View
Sept. 29, 2021, 10:10 a.m. Sept. 29, 2021, 10:10 a.m.

Süd-Ago-Ost: OSM drivable roads

Drivable roads from OpenStreetMap over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

View
Sept. 24, 2021, noon Sept. 24, 2021, noon

Süd-Ago-Ost: OSM main roads

Main roads from OpenStreetMap over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

View
Sept. 24, 2021, 1 p.m. Sept. 24, 2021, 1 p.m.

Synthetic Building Energy Consumption Dataset – Local Calendar Week Sampler (MODERATE Project)

This dataset contains 50 synthetic years of sub-hourly building energy and indoor environment data, generated from approximately 3 years of real monitored data using the Local Calendar Week Sampler method developed within the MODERATE project (Horizon Europe GA 101069834). The synthetic generation approach recombines true historical weekly profiles into statistically plausible synthetic ones, preserving seasonal patterns, weekday/weekend variation, and distributional characteristics of the original data, while providing anonymization through resampling, donor diversification, and a percentile-based source-recognizability guardrail. Each synthetic year covers a full calendar year at 2-hour temporal resolution, with the following variables: Column Unit Description timestamp — Date and time (2h intervals) el. Energy kWh Electrical energy consumption th. Energy kWh Thermal energy consumption CO2 ppm Indoor CO₂ concentration Temperature °C Indoor air temperature WZ warm_water_energy kWh Domestic hot water energy ext. Solar Irradiance W/m² External solar irradiance ext. Temperature °C External air temperature synthetic_year_id — Identifier of the synthetic year (001–050) Generation Method The Local Calendar Week Sampler operates as follows: 1. Historical weeks are validated and flagged for gaps (>3h) or extreme values (>95th percentile). 2. For each target week, a candidate pool of temporally aligned historical weeks is assembled. 3. A primary donor (donor A) provides context variables (CO₂, temperature); a secondary daily donor (donor B) provides energy shape variation. 4. The synthetic profile is constructed as a scaled convex combination of donor profiles, anchored to a candidate-pool median baseline. 5. Weekly totals are rescaled to match a randomly selected week from the candidate pool. 6. A cosine transition layer is applied at week boundaries for temperature and CO₂ to avoid discontinuities. 7. Data is resampled to 2-hour resolution (energy summed, other variables averaged). Anonymization measures include donor energy contribution capping (10–35%), CO₂ anomaly screening, resampling, and a source-recognizability guardrail that resamples weeks where the original donor remains the closest shape match. Quality Metrics Statistical fidelity of the synthetic data relative to historical data was assessed using shape-based metrics across all 50 generated years: Metric Value Mean nearest weekly NRMSE 2.008 Median nearest weekly NRMSE 1.605 Weekly close-match rate (p01) 0.177 Weekly close-match rate (p05) 0.329 Source-is-nearest-weekly rate 0.095 Mean source shape correlation 0.487 Source-is-top-shape-match rate 0.201 Dominant candidate share 0.025 Normalized candidate usage entropy 0.982 The high candidate usage entropy (0.982) indicates well-distributed sampling with no dominant source weeks. The source-is-nearest rate of 9.5% is low, indicating effective anonymization. Approximately 40% of synthetic days have a historical near-duplicate below the empirical non-self NRMSE threshold — a known limitation without formal anonymization guarantees. Seasonal daily profiles and distributions of electrical energy, thermal energy, and domestic hot water energy were validated visually against historical data: Limitations No formal anonymization guarantees are provided. Thermal energy exhibits near-zero values in summer (near-heating-off conditions), which may amplify relative NRMSE in that season. The dataset does not include the original historical data. Related Resources MODERATE project: moderate-project.eu Grant Agreement No.: 101069834 MODERATE open-source tools: github.com/MODERATE-Project MODERATE platform: moderate.cloud

Other

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Temperature Condition Index - 231 m 8 days

The Temperature Condition Index (TCI) is based on the Land Surface Temperature (LST) MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The TCI is calculated using the formula TCIi = (LSTmax,i - LSTi)/(LSTmax,i - LSTmin,i) * 100. The TCI expresses anomalies of the LST. The data is provided as 8 day measures. The time series is starting from 2001. The TCI values range from 0-100, whereas high values correspond to optimal vegetation conditions and low values indicate unfavorable vegetation conditions.

STAC

View
July 13, 2026, midnight July 13, 2026, midnight

The Alpine Drought Impact report Inventory (EDIIALPS)

The presented data of the Alpine Drought Impact report Inventory (EDIIALPS V1.1) is a slight update of the version 1.0 with some new entries until 2020. The EDIIALPS archives negative drought impacts as text-reports in the "Alpine Space" region and is a sub database of the European Drought Impact report Inventory (EDII). The data resulted from collaborative work from various project partners.

datasets

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Jan. 1, 2022, midnight Jan. 1, 2022, midnight

Timeseries data with Energy consumption profiles from buildings

O3, NO2, PM1, PM2.5, PM10, temperature, relative humidity, atmospheric pressure and solar radiation low-cost sensors have been co-located with the certified air quality monitoring station of Bolzano - Italy (UTM 46°49′44″ N, 11°34′24″ E, 262 m a.s.l.) managed by the local environmental agency (APPA). The filed campaign is aimed at comparing the low-cost sensors' timeseries to high-resolution instruments, which comply with the protocol for standardized acquisition released by the European Environmental Agency. The core unit of the low-cost sensors acquisition system is a Raspberry Pi 4, which couples to the sensors via the I2C bus. Specifically, the low-cost sensors involved in the campaign are: O3 (Alphasense OX-A431), NO2 (Alphasense NO2-A431) and PM1, 2.5, 10 (Alphasense OPC-N3), Tin/RHin (Sensirion SHT31), Tout/RHout (Galtech PM15PS), atmospheric pressure (Bosch Sensortech BMP388), and SR (Apogee SP 420 Smart).

InfluxDB

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June 9, 2022, 2 p.m. Aug. 10, 2022, 2 p.m.

Tofana Global Position System receiving station

Global Position System receiving station timeseries datasets. The receiving station is located on the above infrastructure for the cable lift of Freccia nel Cielo in Tofana di Mezzo at about 2800 m above the sea level. The main goal is monitoring the movement of the cable infrastructure, in the contest of the RESCUE PERMAFROST project .Time interval is double: 1 hour and 1 day. Spatial Reference System is EPSG:32633.

InfluxDB

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Oct. 19, 2023, 11 a.m. Oct. 19, 2023, 11 a.m.

TRANSALP Study Area

Extent of the cross-border study area for the TRANSALP project, which includes South Tyrol (IT), Valle Agordino (Veneto, IT), and East Tyrol (AT).

Maps

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May 5, 2022, 12:45 p.m. May 5, 2022, 12:45 p.m.

TRANSALP Study Area: Hexagonal tessellation (~250m)

Tessellation onto regular hexagonal cells of the TransAlp project's study area, which comprises South Tyrol (IT), Valle Agordina (Veneto) and East Tyrol (AU), at a resolution of ~250m.

Maps

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March 29, 2022, 11:14 a.m. March 29, 2022, 11:14 a.m.

TRANSALP Study Area: Tessellated population (~250m)

Aggregated population data over the TransAlp project's study area, which comprises South Tyrol (IT), Valle Agordina (Veneto) and East Tyrol (AU), onto an hexagonal tessellation of ~250m.

Maps

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March 31, 2022, 6:51 p.m. March 31, 2022, 6:51 p.m.

Tree_Mask_2018

This layer represents the first draft of a forest mask for the province of South Tyrol based on classification of Sentinel-2 images acquired between 2015 and 2018.

Maps

View
April 14, 2023, 3:31 p.m. April 14, 2023, 3:31 p.m.

Tree_Mask_2018_final

Final Tree Mask of 2018 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2015-2018 as well as slope and elevation information from the EU-DEM.

Maps

View
May 30, 2023, 6:09 p.m. May 30, 2023, 6:09 p.m.

Tree_Mask_2019

Tree Mask of 2019 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2017-2019 as well as slope and elevation information from the EU-DEM. Changes since mapping began in 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data).

Maps

View
May 30, 2023, 5:55 p.m. May 30, 2023, 5:55 p.m.

Tree_Mask_2020

Tree Mask of 2020 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2018-2020 as well as slope and elevation information from the EU-DEM. Changes since 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data).

Maps

View
May 30, 2023, 5:59 p.m. May 30, 2023, 5:59 p.m.

Tree_Mask_2021

Tree Mask of 2021 for the province of South Tyrol based on the classification of spectral-temporal metrics of Sentinel-2 images between 2019-2021 as well as slope and elevation information from the EU-DEM. Changes since 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data).

Maps

View
May 30, 2023, 6:02 p.m. May 30, 2023, 6:02 p.m.

Tree_Mask_2022

Tree Mask of 2022 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2020-2022 as well as slope and elevation information from the EU-DEM. Changes since 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data).

Maps

View
May 30, 2023, 6:04 p.m. May 30, 2023, 6:04 p.m.

TVO: OSM drivable roads

OpenStreetMap drivable roads over Trentino Alto-Adige, Veneto and Osttirol (Austria).

Maps

View
Sept. 20, 2021, 1:10 p.m. Sept. 20, 2021, 1:10 p.m.

Tyrol: Roads Network

Lineares Referenzsystem der Verkehrsinfrastrukturen von Tirol - beinhaltet Hochrangiges Strassen- und Bahnnetz bis hin zu den Fuss- und Wanderwegen. Originaldatensatz wird in der Graphenintegrations-Plattform Tirol gewartet.

Maps

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Sept. 29, 2021, 5:40 p.m. Sept. 29, 2021, 5:40 p.m.

UAV DataCubes

High resolution - hyperspectral maps as part of the data time series produced by MONALISA project. 16 Hyperspectral bands and Orthomosaic and Digital Surface models

OpenEO

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Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

UAV DataCubes 20150507

HR Digital surface models, HR Land monitoring, HR Multi-spectral imaging

OpenEO

View
Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

UAV DataCubes 20150821

HR Digital surface models, HR Land monitoring, HR Multi-spectral imaging

OpenEO

View
Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

UAV DataCubes 20150909

HR Digital surface models, HR Land monitoring, HR Multi-spectral imaging

OpenEO

View
Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

Vegetation Condition Index - 231 m 8 days

The Vegetation Condition Index (VCI) is based on the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VCI is calculated using the formula VCIi = (NDVIi - NDVImin,i)/(NDVImax,i - NDVImin,i) * 100. The VCI expresses anomalies of the NDVI. The data is provided as 8 day measures. The time series is starting from 2001. The VCI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.

STAC

View
Jan. 1, 2001, 1 a.m. None

Vegetation Condition Index - 231 m 8 days

The Vegetation Condition Index (VCI) is based on the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VCI is calculated using the formula VCIi = (NDVIi - NDVImin,i)/(NDVImax,i - NDVImin,i) * 100. The VCI expresses anomalies of the NDVI. The data is provided as 8 day measures. The time series is starting from 2001. The VCI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.

OpenEO

View
Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

Vegetation Health Index - 231 m 8 days

The Vegetation Health Index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and the land surface temperature, both derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance and the land surface temperature (LST) on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m, therefore the original 1000 m resolution of the MOD11A2 LST is downscaled to 231 m of the MOD09Q1 reflectance. Both products are masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VHI relies on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress. The data is provided as 8 day measures. The time series is starting from 2001. The VHI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.

OpenEO

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Oct. 1, 2024, 9:51 a.m. Oct. 1, 2024, 9:51 a.m.

Vegetation Health Index - 231 m 8 days

The Vegetation Health Index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and the land surface temperature, both derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance and the land surface temperature (LST) on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m, therefore the original 1000 m resolution of the MOD11A2 LST is downscaled to 231 m of the MOD09Q1 reflectance. Both products are masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VHI relies on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress. The data is provided as 8 day measures. The time series is starting from 2001. The VHI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.

STAC

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Jan. 1, 2001, 1 a.m. None

Veneto: Roads Network

Rete stradale derivata da DataBase strati prioritario in scala 1:10.000 (Regione Veneto,Sezione Pianificazione Territoriale Strategica e Cartografia)

Maps

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Sept. 29, 2021, 4:50 p.m. Sept. 29, 2021, 4:50 p.m.

Vulnerability indicator

Vulnerability indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow project

Maps

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Water Heating

The Layer of share of final energy consumption in the residential sector for water heating. The frequency of data is annual.

Maps

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Feb. 8, 2021, 2:07 p.m. Feb. 8, 2021, 2:07 p.m.

Watt's Up? Synthetic Data for Buildings

This repository contains the challenge data and links to the participating teams' repositories for the Watt's Up? Hack for Energy Efficiency – Synthetic Data for Buildings hackathon, held at TU Wien on 22–23 February 2025. 🔗 Hackathon event page This hackathon was funded by the EU project MODERATE (grant No 101069834) and VSC/EuroCC.

Other

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Weekly-aggregated irrigation product - Ebro basin

A 1 km experimental dataset of weekly-aggregated irrigation estimates retrieved through the SM-based inversion approach implemented with RT1 Sentinel-1 soil moisture and ERA5-Land rainfall and GLEAM 1 km potential evaporation

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Weekly-aggregated irrigation product - Po basin

A 1 km experimental dataset of weekly-aggregated irrigation estimates retrieved through the SM-based inversion approach implemented with RT1 Sentinel-1 soil moisture and ERA5-Land rainfall and GLEAM 1 km potential evaporation

STAC

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July 13, 2026, midnight July 13, 2026, midnight

Wetlands_Dunes_Circeo_national_park

This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

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July 13, 2026, midnight July 13, 2026, midnight

World Land Cover Himalayas 2015

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:33 a.m. Sept. 30, 2021, 10:33 a.m.

World Land Cover Himalayas 2016

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:33 a.m. Sept. 30, 2021, 10:33 a.m.

World Land Cover Himalayas 2017

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:34 a.m. Sept. 30, 2021, 10:34 a.m.

World Land Cover Himalayas 2018

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:34 a.m. Sept. 30, 2021, 10:34 a.m.

World Land Cover Himalayas 2019

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:34 a.m. Sept. 30, 2021, 10:34 a.m.

World Land Cover Himalayas 2020

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:35 a.m. Sept. 30, 2021, 10:35 a.m.

World Land Cover Province 2015

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:08 a.m. Sept. 30, 2021, 10:08 a.m.

World Land Cover Province 2016

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:30 a.m. Sept. 30, 2021, 10:30 a.m.

World Land Cover Province 2017

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:31 a.m. Sept. 30, 2021, 10:31 a.m.

World Land Cover Province 2018

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:31 a.m. Sept. 30, 2021, 10:31 a.m.

World Land Cover Province 2019

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:31 a.m. Sept. 30, 2021, 10:31 a.m.

World Land Cover Province 2020

Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

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Sept. 30, 2021, 10:31 a.m. Sept. 30, 2021, 10:31 a.m.

Yearly damages LATEST

Mapping of the forest changes occurring in 2020, 2021, 2022, 2023 and 2024 at a yearly scale. The outputs shown here are based on the analysis of Sentinel 2 time series. The date corresponds to the first date at which a change was detected.

Maps

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Nov. 5, 2024, 3:24 p.m. Nov. 5, 2024, 3:24 p.m.