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Full catalog/clms_ba_global_300m_monthly_v4
clms_ba_global_300m_monthly_v4·dataset

Monthly global burn scars with burned fraction (Copernicus)

CLMS BA Global 300m monthly V4
biosphere ESA ESA Copernicus active COGNetCDF
In plain English

What it measures. A monthly worldwide map of fire-damaged land, recording both the date an area burned and what fraction of each pixel was affected. Mapped at about 300-metre detail.

How it's made. Produced by ESA Copernicus from the OLCI and SLSTR instruments on the Sentinel-3 satellites, released a few months after the fires occur.

How & where you'd use it. Helps quantify how much of an area actually burned, supporting fire-season analysis, carbon accounting and land management. This edition runs from July 2018 to the present.

What's measured

CopernicusCLMSBABurnt Areafire disturbanceglobalmonthly300mSentinel-3OLCISLSTR

Coverage & cadence

  • Time span2018-07-01 → ongoing
  • Spatial extent-179.9999999, -59.9985119, 179.9985119, 80.0014881
  • FormatsCOG, NetCDF

What you can do with it

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

Maps burn scars, surfaces which have been sufficiently affected by fire to display significant changes in the vegetation cover (destruction of dry material, reduction or loss of green material) and in the ground surface (temporarily darker because of ash). In addition to the date of burn, the Burnt Areas version 4.0 provides the burnt fraction of the pixel. Monthly datasets are available at global scale, in the spatial resolution of 300 m, and with a time lag of few months. They cover the period from July 2018 to present.

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_ba_global_300m_monthly_v4"],   # 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.