SoilGrids data for the European Alps


Abstract

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.

FAIR Overall Score

77%

Keywords

soil, buik density, sand, silt, soil thickness, clay, PH, organic content

CC-BY-4.0


FAIR Overall Score: 77%

Evaluated by F-UJI - An Automated FAIR Data Assessment Tool: https://doi.org/10.5281/zenodo.6361400

Findable 57%
Accessible 100%
Interoperable 67%
Reusable 83%
Findable Moderate
Metadata and data are assigned a globally unique identifier.
FsF-F1-01MD · 1/1
  • Metadata identifier follows a defined unique identifier syntax or scheme (IRI, URL, UUID, HASH or PID) (1/1)
Metadata and data are assigned a persistent identifier.
FsF-F1-02MD · 0/1
  • Metadata identifier follows a defined persistent identifier syntax (0/0.5)
    • PID syntax is OK but the PID seems to resolve to a different entity, will not use this PID for content negotiation
    • Could not find any persistent identifier for metadata which is registered
  • Persistent identifier for metadata is registered and maintained by a PID authority (0/0.5)
    • Landing page domain resolved from PID found in metadata does not match with input URL domain -:eurac.edu <> copernicus.org
Metadata includes descriptive core elements (creator, title, data identifier, publisher, publication date, summary and keywords) to support data findability.
FsF-F2-01M · 0/2
  • Core data citation metadata is available (0/1)
    • Failed to parse RDF, trying to fix RDF string and retry parsing everything before line -: 1
    • Landing page domain resolved from PID found in metadata does not match with input URL domain -:eurac.edu <> copernicus.org
  • Core descriptive metadata is available (0/1)
    • Failed to extract Datacite JSON -: 'bytes' object has no attribute 'get'
    • Not all required core descriptive metadata elements exist, missing -: ['publisher', 'creator']
Metadata includes the identifier of the data it describes.
FsF-F3-01M · 1/1
  • Metadata contains a PID or URL which indicates the location of the downloadable data content (1/1)
Metadata is offered in such a way that it can be registered or indexed by search engines.
FsF-F4-01M · 2/2
  • Metadata is given in a way major search engines can ingest it for their catalogues (Dublin Core or schema.org or DCAT encoded in microdata, RDFa, embedded JSON-LD or meta tags see e.g. Google Dataset Search webmaster guidelines) (2/2)
Accessible Advanced
Metadata contains access level and access conditions of the data.
FsF-A1-01M · 1/1
  • Information about access restrictions or rights can be identified in metadata (1/1)
Metadata and data are retrievable by their identifier
FsF-A1-02MD · 2/2
  • Metadata are retrievable via their specified identifier (1/1)
  • Data are retrievable via the identifiers given in metadata (1/1)
A standardized communication protocol is used to access metadata and data.
FsF-A1.1-01MD · 2/2
  • Identifier leading to metadata matches a scheme indicating a standardized web communication protocol. (1/1)
  • Identifier leading to data are matching a schema indicating a standardized web communication protocol. (1/1)
Metadata and data are accessible through a standardized communication protocol which supports authentication.
FsF-A1.2-01MD · 2/2
  • The communication protocol found in identifiers (IRIs) leading to metadata supports authentication. (1/1)
  • The communication protocol identified in data links (IRIs) supports authentication. (1/1)
Interoperable Moderate
Metadata is represented using a formal knowledge representation language.
FsF-I1-01M · 2/2
  • Parsable, structured metadata (JSON-LD, RDFa) is embedded in the landing page XHTML/HTML code (2/2)
  • Parsable, structured metadata (RDF, JSON-LD) is accessible through content negotiation, typed links or sparql endpoint (2/2)
Metadata uses registered semantic resources
FsF-I2-01M · 2/2
  • Metadata uses terms from registered vocabularies that are identified by their namespaces (2/2)
Metadata includes qualified references between the data and its related entities.
FsF-I3-01M · 0/2
  • Related resources are referenced in plain text within appropriate metadata properties indicating the relation type (0/2)
    • Could not identify qualified related resources in metadata
  • Related resources are referenced by machine readable links or identifiers within appropriate metadata properties indicating the relation type (0/2)
Reusable Moderate
Metadata specifies the content of the data.
FsF-R1-01M · 1/2
  • Minimum information (resource type) about the available data content is specified in the metadata (1/1)
  • Information on the manner and form (file size and type or service (API) endpoint and protocol) in which data is delivered is provided (0/1)
Metadata includes license information under which data can be reused.
FsF-R1.1-01M · 1/1
  • Licence information is given in an appropriate metadata element (1/1)
Metadata includes provenance information about data creation or generation.
FsF-R1.2-01M · 1/1
  • Metadata contains elements which hold provenance information which can be mapped to PROV based on PROV-DC. (1/1)
  • Metadata contains elements which hold provenance information using formal provenance ontologies (PROV, PAV). (0/1)
Metadata follows a standard recommended by the target research community of the data.
FsF-R1.3-01M · 1/1
  • Community specific metadata standard is detected using namespaces or schemas found in provided metadata (1/1)
  • Multidisciplinary but community endorsed metadata (RDA Metadata Standards Catalog, fairsharing) standard is detected by namespace (1/1)
Data is available in a file format recommended by the target research community.
FsF-R1.3-02D · 1/1
  • Data is available in a file format recommended by the research community (long term file formats, open file formats or scientific file format) (1/1)

Assessed 2026-08-26 (metrics 0.8)

Snippet code

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##### ----Explore and download STAC data with Python ----- #####

from pystac_client import Client

## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)

## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
print(f"Number of collections: {len(stac_collections)}")

## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
    print(f"- {collection.id}")
print("-------------------------")

## Retrieve a specific collection
collection = catalog.get_collection("ID_OfTheCollection")

## Search for items in the collection
collection_items = list(catalog.search(collections=['ID_OfTheCollection'], max_items=10).items())
print(collection_items)

## Retrieve a list of the first 10 items belonging to a specific collection
item = collection.get_item("NameOfTheItem")
#print(list(item.assets.items())[0:10])

##Print the band name and the href of a specific item retrieved from the previous list
print(item.assets["ID_OfTheCollection"].title)
print(item.assets["ID_OfTheCollection"].href)

## Print Item’s assets through the assets attribute, which is a dictionary
for asset_key in item.assets:
    asset = item.assets[asset_key]
    print("{}: {} ({})".format(asset_key, asset.href, asset.media_type))

## ------------- DONWLOAD COG files from a specific collection ------- ####
from pystac_client import Client
import requests

## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)

## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
# print(f"Number of collections: {len(stac_collections)}")

## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
	print(f"- {collection.id}")
print("-------------------------")

## Retrieve a specific collection
collection = catalog.get_collection("ID_OfTheCollection")

## Search for items in the collection
collection_items = list(catalog.search(collections=['ID_OfTheCollection'], max_items=10).items())
print(collection_items)

url_basepath = "https://eurac-eo.s3-eu-west-1.amazonaws.com/"
collection_url = url_basepath + "ID_OfTheCollection" + "/"
print(collection_url)
for item_id in collection_items:
	# item = item.assets[asset_key]
	print(item_id.id)
	cog_file = str(item_id.id + '.tif')
	url = collection_url + cog_file
	response = requests.get(url)
	with open(cog_file, "wb") as f:
		f.write(response.content)

# Name Description Link Date published Category
1 MOOC Cubes and Clouds Free Online Course teaching the concepts of data cubes, cloud platforms and open science in geospatial and EO. Link March 8, 2024 OpenEO, STAC
2 STAC guidelines Documentation to browse and download items of the STAC catalog Link Feb. 22, 2024 STAC