![ESA/WorldCover/v100](https://developers.google.com/earth-engine/datasets/images/ESA/ESA_WorldCover_v100_sample.png?authuser=19)
- Dataset Availability
- 2020-01-01T00:00:00Z–2021-01-01T00:00:00Z
- Dataset Provider
- ESA/VITO/Brockmann Consult/CS/GAMMA Remote Sensing/IIASA/WUR
- Earth Engine Snippet
-
ee.ImageCollection("ESA/WorldCover/v100")
- Tags
Description
The European Space Agency (ESA) WorldCover 10 m 2020 product provides a global land cover map for 2020 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes and has been generated in the framework of the ESA WorldCover project, part of the 5th Earth Observation Envelope Programme (EOEP-5) of the European Space Agency.
See also:
Bands
Resolution
10 meters
Bands
Name | Description |
---|---|
Map |
Landcover class |
Map Class Table
Value | Color | Description |
---|---|---|
10 | #006400 | Tree cover |
20 | #ffbb22 | Shrubland |
30 | #ffff4c | Grassland |
40 | #f096ff | Cropland |
50 | #fa0000 | Built-up |
60 | #b4b4b4 | Bare / sparse vegetation |
70 | #f0f0f0 | Snow and ice |
80 | #0064c8 | Permanent water bodies |
90 | #0096a0 | Herbaceous wetland |
95 | #00cf75 | Mangroves |
100 | #fae6a0 | Moss and lichen |
Terms of Use
Terms of Use
Citations
Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, Linlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021. ESA WorldCover 10 m 2020 v100. (doi:10.5281/zenodo.5571936)
Explore with Earth Engine
Code Editor (JavaScript)
var dataset = ee.ImageCollection('ESA/WorldCover/v100').first(); var visualization = { bands: ['Map'], }; Map.centerObject(dataset); Map.addLayer(dataset, visualization, 'Landcover');
import ee import geemap.core as geemap
Colab (Python)
dataset = ee.ImageCollection('ESA/WorldCover/v100').first() visualization = { 'bands': ['Map'], } m = geemap.Map() m.center_object(dataset) m.add_layer(dataset, visualization, 'Landcover') m