Data discovery

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Results: 145 items found

Air temperature - Venosta Valley

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

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Sept. 2, 2019, 2 a.m. Aug. 2, 2020, 2 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}

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May 1, 2021, 2 a.m. Sept. 28, 2023, 2 a.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}

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May 5, 2021, 2 a.m. Sept. 27, 2023, 2 a.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.

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Jan. 1, 2019, 1 a.m. None

Copernicus_Senwise_CHIME-like_BE

The input CHIME-like imagery dataset is produced based on EnMAP L2A and PRISMA L2D hyperspectral product respectivelly downloaded from EnMAP catalog (https://eoweb.dlr.de/egp/main) and from ASI PRISMA catalog (http://prisma.asi.it/js-cat-client-prisma-src) . The CHIME-like imagery aims to simulate the future CHIME sensor at Level 2 (Bottom-of-Atmosphere BOA reflectance) and consists of 192 spectral bands from 415 - 2450 nm with a band width of around 12 nm.

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April 8, 2020, 12:42 p.m. Sept. 19, 2024, 1:14 p.m.

Copernicus_Senwise_CHIME-like_FR

The input CHIME-like imagery dataset is produced based on EnMAP L2A and PRISMA L2D hyperspectral product respectivelly downloaded from EnMAP catalog (https://eoweb.dlr.de/egp/main) and from ASI PRISMA catalog (http://prisma.asi.it/js-cat-client-prisma-src) . The CHIME-like imagery aims to simulate the future CHIME sensor at Level 2 (Bottom-of-Atmosphere BOA reflectance) and consists of 193 spectral bands from 415 - 2450 nm with a band width of around 12 nm.

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Jan. 6, 2020, 11:41 a.m. July 28, 2025, 12:41 p.m.

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.

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June 25, 2025, 7:40 p.m. Sept. 29, 2025, 7:41 p.m.

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.

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July 30, 2022, 7:18 a.m. Dec. 22, 2022, 12:59 a.m.

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.

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June 26, 2025, 7:28 a.m. Sept. 30, 2025, 7:29 a.m.

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.

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Aug. 1, 2023, 6:57 a.m. Dec. 7, 2023, 5:59 a.m.

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.

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Jan. 8, 2024, 5:57 a.m. Dec. 25, 2024, 5:59 a.m.

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.

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Jan. 26, 2025, 5:57 a.m. Nov. 10, 2025, 5:59 a.m.

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.

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May 3, 2024, 7:19 a.m. Sept. 24, 2024, 7:20 a.m.

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.

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Oct. 10, 2025, 7:18 a.m. Nov. 11, 2025, 6:19 a.m.

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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April 20, 2025, 7:54 p.m. Nov. 14, 2025, 6:56 p.m.

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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June 28, 2025, 7:48 p.m. Nov. 19, 2025, 6:50 p.m.

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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June 15, 2025, 7:50 p.m. Nov. 22, 2025, 6:52 p.m.

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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March 29, 2024, 6:17 a.m. Dec. 26, 2024, 6:19 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

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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.

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Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily evaporation product Ebro basin

Daily evaporation data are produced from Two-Source Energy Balanced (TSEB) model driven by ESA Sentinel (both Sentinel-2 MSI and Sentinel-3 SLSTR) and ERA5 reanalysis data

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Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily evaporation product Herault basin

Daily evaporation data are produced from Two-Source Energy Balanced (TSEB) model driven by ESA Sentinel (both Sentinel-2 MSI and Sentinel-3 SLSTR) and ERA5 reanalysis data

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Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily evaporation product Medjerda basin

Daily evaporation data are produced from Two-Source Energy Balanced (TSEB) model driven by ESA Sentinel (both Sentinel-2 MSI and Sentinel-3 SLSTR) and ERA5 reanalysis data

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Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily evaporation product Po basin

Daily evaporation data are produced from Two-Source Energy Balanced (TSEB) model driven by ESA Sentinel (both Sentinel-2 MSI and Sentinel-3 SLSTR) and ERA5 reanalysis data

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Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily evapotranspiration product - 60m - 32TPS

Developed through the SENWISE project, this daily evapotranspiration (ET) demonstrates the type of ET products that could be generated from LSTM-like consolidated datasets, thereby supporting the assessment of their potential for agricultural and environmental applications. The dataset provides a consolidated, multi-temporal representation of daily ET, generated from the high-resolution LST products Land Surface Temperature Monitoring (LSTM) representative dataset product at 60m resolution developed within the project, together with ancillary structural, biophysical, and meteorological datasets. ET is provided at 60m resolution and is produced using the TSEB-PT model (Norman et al., 1995). Data collection covers the area of Sentinel-2 Tile: 32TPS (WGS84, EPSG4326: 10.2904840, 45.9334965, 11.7555849, 46.9461682). ET is provided in mm/day and stored with a scale factor of 0.01.

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April 1, 2017, 2 a.m. Nov. 1, 2025, 12:59 a.m.

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).

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Sept. 1, 1950, 1 a.m. Dec. 31, 2025, 1 a.m.

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

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Jan. 1, 2000, 1 a.m. Feb. 28, 2022, 1 a.m.

Daily precipitation product - 1000m

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

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Jan. 1, 2000, 1 a.m. Feb. 28, 2022, 1 a.m.

Daily 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

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Aug. 1, 2015, 2 a.m. Dec. 31, 2021, 1 a.m.

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).

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Sept. 1, 1950, 1 a.m. Dec. 31, 2025, 1 a.m.

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.

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Jan. 1, 2017, 1 p.m. Dec. 31, 2021, 1 p.m.

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

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Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

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

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Jan. 1, 2017, 1 a.m. Dec. 31, 2019, 1 a.m.

Daytime land surface temperature product - 60m - 31TGJ

Developed through the SENWISE initiative, this Land Surface Temperature Monitoring (LSTM) representative dataset aims to support the preparation of the upcoming mission by emulating its expected data products. The dataset provides a consolidated, multi-temporal representation of daytime land surface temperature (LST), structured to be a proxy of the future LSTM data outputs. Data are provided at 60m resolution and are produced using Sentinel-3 LST product at 1km resolution through Kernel Driven Downscaling (KDD). Data collection covers the area of Sentinel 2 Tile: 32TGJ (WGS84, EPSG4326: 5.4627354, 43.2012113, 6.8757389, 44.2259751). LST is provided in Kelvin and stored with an offset of 150, a scale factor of 0.01.

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April 1, 2017, 2 a.m. Nov. 1, 2025, 12:59 a.m.

Daytime land surface temperature product - 60m - 31UFR

Developed through the SENWISE initiative, this Land Surface Temperature Monitoring (LSTM) representative dataset aims to support the preparation of the upcoming mission by emulating its expected data products. The dataset provides a consolidated, multi-temporal representation of daytime land surface temperature (LST), structured to be a proxy of the future LSTM data outputs. Data are provided at 60m resolution and are produced using Sentinel-3 LST product at 1km resolution through Kernel Driven Downscaling (KDD). Data collection covers the area of Sentinel 2 Tile: 31UFR (WGS84, EPSG4326: 4.3826843, 49.5284182, 5.9593860, 50.5437391). LST is provided in Kelvin and stored with an offset of 150, a scale factor of 0.01.

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April 1, 2017, 2 a.m. Nov. 1, 2025, 12:59 a.m.

Daytime land surface temperature product - 60m - 32TPS

Developed through the SENWISE initiative, this Land Surface Temperature Monitoring (LSTM) representative dataset aims to support the preparation of the upcoming mission by emulating its expected data products. The dataset provides a consolidated, multi-temporal representation of daytime land surface temperature (LST), structured to be a proxy of the future LSTM data outputs. Data are provided at 60m resolution and are produced using Sentinel-3 LST product at 1km resolution through Kernel Driven Downscaling (KDD). Data collection covers the area of Sentinel 2 Tile: 32TPS (WGS84, EPSG4326: 10.2904840, 45.9334965, 11.7555849, 46.9461682). LST is provided in Kelvin and stored with an offset of 150, a scale factor of 0.01.

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April 1, 2017, 2 a.m. Nov. 1, 2025, 12:59 a.m.

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.

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Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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.

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Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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.

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Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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.

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Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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.

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Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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.

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Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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.

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Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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

View
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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

View
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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
Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

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

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

View
Dec. 31, 2009, 1 p.m. Dec. 31, 2019, 1 p.m.

Evapotranspiration - Venosta valley

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

STAC

View
Sept. 2, 2019, 2 a.m. Aug. 2, 2020, 2 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

View
Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 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

View
Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 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

View
Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 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

View
Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

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

View
Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 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

View
Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 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

View
Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

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

View
Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 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).

STAC

View
Jan. 1, 1991, 1 a.m. Dec. 31, 2098, 10 p.m.

HIPPA - Hyperspectral and RGB imaging of wound-inoculated apples with postharvest pathogens

This collection is part of the HIPPA project, which investigates physiological disorders, mechanical damage and fungal diseases affecting apple fruit at harvest and during postharvest using hyperspectral imaging, RGB imaging and spectroscopy. This collection contains RGB images and hyperspectral images of wound-inoculated apples infected with eight different pathogens and corresponding control samples. Healthy-looking apples were manually surface-sterilized and inoculated with spore suspensions. To cover all cultivars and pathogens, apples were prepared and acquired in groups referred to as 'batches' at different timestamps. Each batch contains apples from one specific cultivar and includes a subset of pathogen treatments together with control samples; each batch generally contains approximately 24 apples per included treatment and approximately 24 control apples. Each STAC Item represents one apple sample at one specific day post-inoculation. Item identifiers follow the pattern apple_{sample_id}_dpi{dpi:02d}, for example apple_57000_dpi03. The acquisition setup is an enclosed space of approximately 1 x 1 m, equipped with a lighting system, a rotating platform and multiple optical instruments measuring the apple surface. Acquisitions are performed in the Eurac Research - Center for Sensing Solutions laboratories. The Collection spatial extent represents the general area of origin of the apple samples, while Item geometries represent the laboratory acquisition location.

STAC

View
Oct. 21, 2024, 2 a.m. June 27, 2026, 1:59 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}

STAC

View
May 2, 2021, 2 a.m. Sept. 29, 2023, 2 a.m.

INSTINCT - Reflectance spectroradiometry of Drosophila suzukii infestation risk in cherries, harvest 2025

This collection is part of the INSTINCT project and contains reflectance spectra of sweet cherries acquired during the 2025 harvest season to investigate infestation risk caused by Drosophila suzukii. Protocol 2 focused on cherries at an early ripening stage, when susceptibility to infestation was changing. Untreated cherries were collected from net-protected Kordia and Regina orchard rows at Laimburg Research Centre and exposed to Drosophila suzukii. After exposure, each cherry was classified as non-infested, infested, or punctured without oviposition. Each STAC Item represents one cherry measured once. Three reflectance spectra were acquired at random surface points using an SVC HR-1024i spectroradiometer with the LC-RP PRO Leaf Clip and Reflectance Probe and its integrated light source. Measurements were performed in the Eurac Research - Center for Sensing Solutions laboratories. Item geometries represent the laboratory measurement location.

STAC

View
May 27, 2025, 7:39 p.m. June 10, 2025, 7:56 p.m.

INSTINCT - Reflectance spectroradiometry of Drosophila suzukii infestation status in cherries, harvest 2024

This collection is part of the INSTINCT project and contains reflectance spectra of sweet cherries acquired during the 2024 harvest season to investigate infestation status caused by Drosophila suzukii. Protocol 1 was conducted during the ripening period in which cherries were susceptible to infestation. Untreated and initially non-infested cherries were collected from net-protected orchard rows at Laimburg Research Centre. A subset was exposed to Drosophila suzukii in cages and only correctly infested cherries were retained; an equal group of non-infested control cherries was not exposed in the cages. The same cherries were measured repeatedly at multiple nominal hours after the batch-specific exposure reference time, making the experimental design longitudinal. Each STAC Item represents one cherry at one measurement time. Three reflectance spectra were acquired at random surface points on each cherry using an SVC HR-1024i spectroradiometer with the LC-RP PRO Leaf Clip and Reflectance Probe. Measurements were performed in the Eurac Research - Center for Sensing Solutions laboratories. Item geometries represent the laboratory measurement location. Experimental batches B00 and B01 were preliminary tests and are excluded.

STAC

View
May 30, 2024, 9:26 a.m. June 27, 2024, 12:07 p.m.

INSTINCT - Reflectance spectroradiometry of Drosophila suzukii infestation status in cherries, harvest 2025

This collection is part of the INSTINCT project and contains reflectance spectra of sweet cherries acquired during the 2025 harvest season to investigate infestation status caused by Drosophila suzukii. Protocol 1 was conducted during the ripening period in which cherries were susceptible to infestation. Untreated and initially non-infested cherries were collected from net-protected orchard rows at Laimburg Research Centre. A subset was exposed to Drosophila suzukii in cages and only correctly infested cherries were retained; an equal group of non-infested control cherries was not exposed in the cages. Different cherries were measured at the different nominal hours after exposure, making the experimental design repeated-timepoint rather than longitudinal. Each STAC Item represents one cherry at one measurement time. Three reflectance spectra were acquired at random surface points on each cherry using an SVC HR-1024i spectroradiometer with the LC-RP PRO Leaf Clip and Reflectance Probe. Measurements were performed in the Eurac Research - Center for Sensing Solutions laboratories. Item geometries represent the laboratory measurement location.

STAC

View
June 5, 2025, 3:08 p.m. June 25, 2025, 6:42 p.m.

INSTINCT - Transmittance spectroradiometry of Drosophila suzukii infestation risk in cherries, harvest 2025

This collection is part of the INSTINCT project and contains transmittance spectra of sweet cherries acquired during the 2025 harvest season under Protocol 2 to investigate susceptibility to Drosophila suzukii infestation during early fruit maturation. Untreated cherries of the Kordia and Regina cultivars were collected from net-protected orchard rows at Laimburg Research Centre. All cherries were exposed to Drosophila suzukii and subsequently classified as non-infested, infested, or punctured without oviposition. Each STAC Item represents one cherry measured once and contains one transmittance spectrum. Measurements were acquired with an SVC HR-1024i spectroradiometer using a Fresnel Junior 150 W tungsten light source positioned on one side of the fruit and the SVC optical fibre on the opposite side. Measurements were performed in the Eurac Research - Center for Sensing Solutions laboratories. Item geometries represent the laboratory measurement location. Asset SPT-T_B20_H034_C047_0.sig is excluded because no infestation label is available.

STAC

View
May 27, 2025, 8:15 p.m. June 10, 2025, 8:13 p.m.

INSTINCT - Transmittance spectroradiometry of Drosophila suzukii infestation status in cherries, harvest 2024

This collection is part of the INSTINCT project and contains transmittance spectra of sweet cherries acquired during the 2024 harvest season to investigate infestation status caused by Drosophila suzukii. Protocol 1 was conducted during the ripening period in which cherries were susceptible to infestation. Untreated and initially non-infested cherries were collected from net-protected orchard rows at Laimburg Research Centre. A subset was exposed to Drosophila suzukii in cages and only correctly infested cherries were retained; an equal group of non-infested control cherries was not exposed in the cages. Each STAC Item represents one cherry at one measurement time and contains one transmittance spectrum. Measurements were acquired using an SVC HR-1024i spectroradiometer, with a Fresnel Junior 150 W tungsten light source positioned on one side of the fruit and the SVC optical fibre on the opposite side. Measurements were performed in the Eurac Research - Center for Sensing Solutions laboratories. Item geometries represent the laboratory measurement location. Only batches B10 to B17 are represented in this collection.

STAC

View
June 14, 2024, 3:07 p.m. June 27, 2024, 12:43 p.m.

INSTINCT - Transmittance spectroradiometry of Drosophila suzukii infestation status in cherries, harvest 2025

This collection is part of the INSTINCT project and contains transmittance spectra of sweet cherries acquired during the 2025 harvest season to investigate infestation status caused by Drosophila suzukii. Protocol 1 was conducted during the ripening period in which cherries were susceptible to infestation. Untreated and initially non-infested cherries were collected from net-protected orchard rows at Laimburg Research Centre. A subset was exposed to Drosophila suzukii in cages and correctly infested cherries were compared with non-infested controls. Different cherries were measured at the represented nominal hours, making the experimental design repeated-timepoint. Each STAC Item represents one cherry at one measurement time and contains one transmittance spectrum. Measurements were acquired using an SVC HR-1024i spectroradiometer, with a Fresnel Junior 150 W tungsten light source positioned on one side of the fruit and the SVC optical fibre on the opposite side. Measurements were performed in the Eurac Research - Center for Sensing Solutions laboratories. Item geometries represent the laboratory measurement location.

STAC

View
June 5, 2025, 3:46 p.m. June 24, 2025, 5:12 p.m.

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}

STAC

View
May 3, 2021, 2 a.m. Sept. 27, 2023, 2 a.m.

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.

STAC

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Jan. 1, 2001, 1 a.m. Jan. 3, 2021, 1 a.m.

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.

STAC

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Oct. 22, 2025, 2 a.m. June 8, 2026, 2 a.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.

STAC

View
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.

STAC

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

STAC

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

MODIS First Snow Day 500m

FSD represents the first date in the hydrological year with snow presence

STAC

View
Oct. 1, 2000, 2 a.m. Sept. 30, 2023, 2 a.m.

MODIS Last Snow Day 500m

LSD represents the last date in the hydrological year with snow presence

STAC

View
Oct. 1, 2000, 2 a.m. Sept. 30, 2023, 2 a.m.

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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Oct. 1, 2000, 2 a.m. Sept. 30, 2023, 2 a.m.

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

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Oct. 1, 2000, 2 a.m. Sept. 30, 2023, 2 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].

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July 3, 2002, 2 p.m. None

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}

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May 1, 2021, 2 a.m. Sept. 28, 2023, 2 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.

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Jan. 1, 2001, 1 a.m. Jan. 3, 2021, 1 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).

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

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).

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

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

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

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).

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

RoseL-like DEM differencing, Aix-en-Provence_Pennes-Mirabeau, FR 3, 2025

The RoseL-like DEM difference results of differentiating the X-band Copernicus DEM and an L-band Interferometric DEM built from InSAR processing of the SAOCOM-1B time series acquired during the period from November 2024 to September 2025. Data cover the Aix-en-Provence region, in South of France, including the Pennes-Mirabeau area, where wildfires happened in Summer 2025. Coordinates are [5.094, 43.656], [5.995, 42.999]. With the assumption that the InSAR DEM corresponds to the DTM (i.e. assuming the L-band SAR signal fully reaches the soil, i.e. it is not affected by moisture effects), the result of differencing both DEM's could be considered as a rough proxy of forest height.

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Nov. 13, 2024, 1 a.m. Sept. 29, 2025, 2 a.m.

RoseL-like DEM differencing, GrandLeez, BE, 2022

The RoseL-like DEM difference results of differentiating the X-band Copernicus DEM and an L-band Interferometric DEM built from InSAR processing of the SAOCOM-1A time series acquired during the period from July to December 2022. Coordinates are [4.726,50.622], [4.85,50.567]. Data cover part of the Walloon Region, in Belgium, including the Forest of Grand-Leez. With the assumption that the InSAR DEM corresponds to the DTM (i.e. assuming the L-band SAR signal fully reaches the soil, i.e. it is not affected by moisture effects), the result of differencing both DEM's could be considered as a rough proxy of forest height.

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July 30, 2022, 2 a.m. Dec. 21, 2022, 1 a.m.

RoseL-like DEM differencing, Martigues, FR 1, 2025

The RoseL-like DEM difference results of differentiating the X-band Copernicus DEM and an L-band Interferometric DEM built from InSAR processing of the SAOCOM-1A time series acquired during the period from November 2024 to September 2025.Data cover the Martigues area, South of France, where a wildfire happened in Summer 2025. Coordinates are [4.426, 43.55], [5.266, 43.312]. With the assumption that the InSAR DEM corresponds to the DTM (i.e. assuming the L-band SAR signal fully reaches the soil, i.e. it is not affected by moisture effects), the result of differencing both DEM's could be considered as a rough proxy of forest height.

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Nov. 14, 2024, 1 a.m. Sept. 30, 2025, 2 a.m.

RT1 Surface Soil Moisture - 1 Km

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.

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Jan. 1, 2017, 1 a.m. June 30, 2022, 2 a.m.

SCF_binary

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

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Oct. 1, 2015, 2 a.m. Sept. 30, 2022, 2 a.m.

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}

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May 1, 2021, 2 a.m. Sept. 28, 2023, 2 a.m.

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.

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June 2, 2022, 2 a.m. June 30, 2022, 2 a.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.

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June 2, 2022, 2 a.m. June 30, 2022, 2 a.m.

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

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July 6, 2020, 12:22 p.m. July 6, 2020, 12:40 p.m.

Snow depth - Venosta Valley

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

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Sept. 2, 2019, 2 a.m. Aug. 2, 2020, 2 a.m.

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.

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

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Dec. 31, 1979, 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].

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

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].

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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].

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

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].

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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].

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

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].

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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].

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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].

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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].

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

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].

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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].

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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].

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

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.

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Jan. 1, 2020, 1 p.m. Jan. 1, 2023, 1 p.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.

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Jan. 1, 2020, 1 p.m. Jan. 1, 2023, 1 p.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.

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Jan. 1, 1981, 1 p.m. Jan. 1, 2010, 1 p.m.

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).

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Jan. 1, 1981, 1 p.m. Jan. 1, 2010, 1 p.m.

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.

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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.

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

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.

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

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

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Jan. 1, 2016, 1 a.m. Dec. 31, 2021, 1 a.m.

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

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Jan. 1, 2016, 1 a.m. Dec. 31, 2021, 1 a.m.

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}

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May 5, 2021, 2 a.m. Sept. 27, 2023, 2 a.m.