← Search

Data · collection · 2021

Cropland and grassland map of Switzerland based on Sentinel-2 data

Listed in NASA Earthdata CMR

We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons.

Description

The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier.

Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

Links

Topics

Inferred from text
Geoinformatics 73% · Satellite remote sensing 65%
Provenance · 1 source records, 13 field assertions
SourceKeyLast seenRaw
NASA Earthdata CMRC2789814690-ENVIDAT15 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · NASA CMRconnector:nasa_cmr@1.0.0
concepts[field].anzsrc:group:3704enrichment · NASA CMRtaxonomy-embedding@1.1.0title+keywords+description (73%)
concepts[measured_variable].gcmd:earth-science/climate-indicators/atmospheric-ocean-indicatorssource · NASA CMRconnector:nasa_cmr@1.0.0
concepts[modality].local:modality:remote-sensingenrichment · NASA CMRkeyword-concept-rules@1.0.0title+description (65%)
concepts[platform].gcmd_platform:not-providedsource · NASA CMRconnector:nasa_cmr@1.0.0
created_datesource · NASA CMRconnector:nasa_cmr@1.0.0
descriptionsource · NASA CMRconnector:nasa_cmr@1.0.0/umm/Abstract
license_textsource · NASA CMRconnector:nasa_cmr@1.0.0
publication_datesource · NASA CMRconnector:nasa_cmr@1.0.0
spatialsource · NASA CMRconnector:nasa_cmr@1.0.0/umm/SpatialExtent
titlesource · NASA CMRconnector:nasa_cmr@1.0.0/umm/EntryTitle
updated_datesource · NASA CMRconnector:nasa_cmr@1.0.0
version_labelsource · NASA CMRconnector:nasa_cmr@1.0.0