Earth Data School
About
Full catalog/CMS_Global_Soil_Respiration_1736
CMS_Global_Soil_Respiration_1736·v1·dataset

How much carbon soils breathe out yearly (1 km)

Global Gridded 1-km Annual Soil Respiration and Uncertainty Derived from SRDB V3
land NASA ORNL_CLOUD Level 3 GeoTIFF
In plain English

What it measures. Global maps estimating how much carbon dioxide soils release each year as living things in the soil breathe, a process called soil respiration, plus maps showing how confident the estimate is.

How it's made. A machine-learning model was trained on thousands of real soil-respiration measurements collected worldwide between 1961 and 2011, then used to predict values everywhere on a fine 1-km grid.

How & where you'd use it. Carbon-cycle researchers use it to see where soils give off the most carbon and to improve estimates of how land contributes to the global carbon balance.

What's measured

LAND SURFACE › SOILS › SOIL RESPIRATIONBIOSPHERE › ECOLOGICAL DYNAMICS › ECOSYSTEM FUNCTIONSAGRICULTURE › SOILS › SOIL RESPIRATION

Coverage & cadence

  • Time span1963-01-01 → 2011-12-31
  • Measured byMODELS (Computer)
  • Processing levelLevel 3
  • Spatial extent-180, -90, 180, 90
  • FormatsGeoTIFF
  • StatusCOMPLETE

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

This dataset provides six global gridded products at 1-km resolution of predicted annual soil respiration (Rs) and associated uncertainty, maps of the lower and upper quartiles of the prediction distributions, and two derived annual heterotrophic respiration (Rh) maps. A machine learning approach was used to derive the predicted Rs and uncertainty data using a quantile regression forest (QRF) algorithm trained with observations from the global Soil Respiration Database (SRDB) version 3 spanning from 1961 to 2011. The two Rh maps were derived from the predicted Rs with two different empirical equations. These products were produced to support carbon cycle research at local- to global-scales, and highlight the immense spatial variability of soil respiration and our ability to predict it across the globe.

Get the data

cms_global_soil_respiration_1736_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="CMS_Global_Soil_Respiration_1736",
    version="1",
    bounding_box=(-122.5, 37.2, -121.8, 37.9),  # your area (W,S,E,N)
    temporal=("2024-01-01", "2024-12-31"),       # your dates
)
files = earthaccess.open(results)   # stream straight from ORNL_CLOUD
Browsing CMR needs no login. Downloading or streaming bytes needs a free Earthdata Login + the earthaccess package.