Global Gridded 1-km Annual Soil Respiration and Uncertainty Derived from SRDB V3

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.

Resources

Name Format Description Link
21 Search results for publications that cite this dataset by its DOI. https://scholar.google.com/scholar?q=10.3334%2FORNLDAAC%2F1736
21 This link allows direct data access via Earthdata login https://daac.ornl.gov/cms/CMS_Global_Soil_Respiration/
33 CMS: Global 1-km maps of predicted soil respiration and associated uncertainty: CMS_Global_Soil_Respiration.pdf https://data.ornldaac.earthdata.nasa.gov/public/cms/CMS_Global_Soil_Respiration/comp/CMS_Global_Soil_Respiration.pdf
34 A global map of predicted annual soil respiration (Rs) at 1-km spatial resolution created by applying the QRF model to gridded covariates. To the right is a plot of the latitudinal mean predicted annual Rs. (Figure from Warner et al., In Review). https://daac.ornl.gov/CMS/guides/CMS_Global_Soil_Respiration_Fig1.png
21 ORNL DAAC Data Set Documentation https://daac.ornl.gov/CMS/guides/CMS_Global_Soil_Respiration.html
21 Data set Landing Page DOI URL https://doi.org/10.3334/ORNLDAAC/1736

Tags

  • soils
  • land-surface
  • biosphere
  • agriculture
  • ecological-dynamics
  • earth-science

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