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

Overview

DOIhttps://doi.org/10.3334/ORNLDAAC/1736
Version1
Project
Published2020-01-09
Updated2020-01-09
Usage84 downloads

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.

Science Keywords

  • LAND SURFACE
  • SOILS
  • SOIL RESPIRATION
  • BIOSPHERE
  • ECOLOGICAL DYNAMICS
  • ECOSYSTEM FUNCTIONS
  • AGRICULTURE
  • SOILS
  • SOIL RESPIRATION

Data Use and Citation

This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use Policy.

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Crosscite Citation Formatter
Warner, D.L., B.P. Bond-Lamberty, J. Jian, E. Stell, and R. Vargas. 2019. Global Gridded 1-km Annual Soil Respiration and Uncertainty Derived from SRDB V3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1736

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Dataset has 1 companion files.

  • CMS_Global_Soil_Respiration.pdf