Daily land-temperature cycle, 2017-2021 (Copernicus)
What it measures. A ten-day statistical picture of how surface temperature swings through each hour of the day, per pixel. This earlier release runs from 2017 to 2021 at coarser detail.
How it's made. Derived by ESA Copernicus from geostationary satellite thermal observations at about 5-kilometer resolution.
How & where you'd use it. Good for historical analysis of land heating and cooling rhythms, climate baselines, and comparing past years' daytime and nighttime temperatures.
What's measured
Coverage & cadence
- Time span2017-01-11 → 2021-01-10
- Spatial extent-179.9999999, -79.9776824, 179.9776872, 80.0223214
- FormatsCOG, NetCDF
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
Provides a statistical overview of the land surface temperature daily cycle for each 10-day compositing period and every geostationary sensor image pixel. The data are available at global scale in the spatial resolution of about 5 km and cover the period from 2017 to 2021.
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=["clms_lst-daily-cycle_global_5km_10daily_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 / xarrayBrowsing the Copernicus STAC is open; downloading bytes needs a free Copernicus Data Space account.
Official links
- Open data source Copernicus STAC