Cloud-free global color basemap imagery (Copernicus)
What it measures. Seamless, mostly cloud-free pictures of the land in red, green, blue, and near-infrared. Cloudy and bright pixels are filtered out by combining the clearest views over three-month windows.
How it's made. Made by ESA Copernicus from Sentinel-2 optical imagery, with cloud masking applied and the cleanest pixel values selected over each season.
How & where you'd use it. Great for basemaps, land-cover mapping, and land-use planning, and especially handy for large, frequently cloudy regions like Iceland.
What's measured
Coverage & cadence
- Time span2020-01-01 → ongoing
- Spatial extent-180, -90, 180, 90
What you can do with it
- Track deforestation, fire scars and land-cover change
- Monitor crop and vegetation health (NDVI/EVI)
- Map how built-up vs. green an area is over time
Official description
The mosaics have four bands of data (Red (B04), Green (B03), Blue (B02) and wide band Near Infrared (B08)). First, cloud masking based on the scene classification layer of the Sentinel-2 level 2 algorithm was applied, then for each pixel and band, within three-month time periods, the first quartile of the distribution of the pixel values was taken as the output value to filter out any bright pixels misclassified as not clouds. If there are no valid pixels for the given timeframe, the pixel is left empty. Sentinel-2 cloudless mosaics can be used for various purposes such as land cover classification, land use planning, or for the creation of basemaps. These images are especially useful for showing large and cloudy areas with interesting large-scale patterns. Iceland is a prime example, with frequent cloud cover and large areal extent spanning several Sentinel-2 swaths.
Get the data
# ESA Copernicus Data Space — open STAC API (free account)
from pystac_client import Client
cat = Client.open("https://stac.dataspace.copernicus.eu/v1")
search = cat.search(
collections=["sentinel-2-global-mosaics"], # add _cog or _nc for a format variant
bbox=(-10, 35, 30, 60), # your area (W,S,E,N)
datetime="2024-01-01/2024-12-31",
)
items = list(search.items()) # then read assets with rioxarray / xarray Browsing the Copernicus STAC is open; downloading bytes needs a free Copernicus Data Space account.
Official links
- Open data source Copernicus STAC