Full catalog/clms_vlcc_grassland-change-confidence-layer_europe_20m_3yearly_v1
clms_vlcc_grassland-change-confidence-layer_europe_20m_3yearly_v1·dataset

Reliability scores for the grassland-change map (Copernicus)

CLMS VLCC Grassland Change Confidence Layer (GRACCL) Europe 20m 3-yearly V1
biosphere ESA ESA Copernicus active
In plain English

What it measures. A companion quality layer describing how trustworthy each pixel is in the matching map of grassland gains and losses, rather than the grassland information itself.

How it's made. Refreshed every three years by the EU's Copernicus Land Monitoring Service as a 20-metre grid across Europe's 38 countries, including French overseas territories.

How & where you'd use it. A technical support file that helps analysts decide which parts of the grassland-change map are dependable before using them.

What's measured

CLMSCopernicusEuropeRasterLand coverLand useGrasslandGRA

Coverage & cadence

  • Time span2018-01-01 → ongoing
  • Spatial extent-180, -90, 180, 90

What you can do with it

  • Map vegetation, forests and biomass
  • Monitor ecosystem productivity and carbon
  • Support habitat and biodiversity studies
Official description

The High Resolution Layer Grassland Change Confidence Layer (GRACCL) is a quality support raster product. This dataset is provided in 20 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles covering the EEA38 countries. High Resolution Layer Grasslands product is part of the European Union's Copernicus Land Monitoring Service. This dataset includes data from the French Overseas Territories (DOMs).

Get the data

copernicus_access.py
# 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=["clms_vlcc_grassland-change-confidence-layer_europe_20m_3yearly_v1"],   # 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.