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
Keywords
Legal constraints
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 Moderate
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Metadata identifier follows a defined unique identifier syntax or scheme (IRI, URL, UUID, HASH or PID) (1/1)
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Metadata identifier follows a defined persistent identifier syntax (0/0.5)
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Persistent identifier for metadata is registered and maintained by a PID authority (0/0.5)
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Core data citation metadata is available (0/1)
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Core descriptive metadata is available (0/1)
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Metadata contains a PID or URL which indicates the location of the downloadable data content (1/1)
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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
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Information about access restrictions or rights can be identified in metadata (1/1)
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Metadata are retrievable via their specified identifier (1/1)
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Data are retrievable via the identifiers given in metadata (1/1)
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Identifier leading to metadata matches a scheme indicating a standardized web communication protocol. (1/1)
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Identifier leading to data are matching a schema indicating a standardized web communication protocol. (1/1)
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The communication protocol found in identifiers (IRIs) leading to metadata supports authentication. (1/1)
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The communication protocol identified in data links (IRIs) supports authentication. (1/1)
Interoperable Moderate
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Parsable, structured metadata (JSON-LD, RDFa) is embedded in the landing page XHTML/HTML code (2/2)
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Parsable, structured metadata (RDF, JSON-LD) is accessible through content negotiation, typed links or sparql endpoint (2/2)
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Metadata uses terms from registered vocabularies that are identified by their namespaces (2/2)
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Related resources are referenced in plain text within appropriate metadata properties indicating the relation type (0/2)
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Related resources are referenced by machine readable links or identifiers within appropriate metadata properties indicating the relation type (0/2)
Reusable Moderate
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Minimum information (resource type) about the available data content is specified in the metadata (1/1)
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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)
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Licence information is given in an appropriate metadata element (1/1)
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Metadata contains elements which hold provenance information which can be mapped to PROV based on PROV-DC. (1/1)
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Metadata contains elements which hold provenance information using formal provenance ontologies (PROV, PAV). (0/1)
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Community specific metadata standard is detected using namespaces or schemas found in provided metadata (1/1)
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Multidisciplinary but community endorsed metadata (RDA Metadata Standards Catalog, fairsharing) standard is detected by namespace (1/1)
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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)
Snippet code
##### ----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)
Related docs
| # | 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 |