Overview
| DOI | https://doi.org/10.3334/ORNLDAAC/1736 |
|---|---|
| Version | 1 |
| Project | |
| Published | 2020-01-09 |
| Usage | 716 downloads |
| Citations | 5 publications cited this dataset |
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 NASA Earthdata Data Use Guidance.
Data Files
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Companion Files
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Dataset Companion Files
Dataset has 1 companion files.
- CMS_Global_Soil_Respiration.pdf