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 |
|---|---|---|---|
| Download this dataset | HTML | This link allows direct data access via Earthdata login | Open resource |
| Get a related visualization | PNG | 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). | Open resource |
| View documentation related to this dataset | HTML | ORNL DAAC Data Set Documentation | Open resource |
| View documentation related to this dataset | CMS: Global 1-km maps of predicted soil respiration and associated uncertainty: CMS_Global_Soil_Respiration.pdf | Open resource | |
| This dataset's landing page | HTML | Data set Landing Page DOI URL | Open resource |
| Google Scholar search results | HTML | Search results for publications that cite this dataset by its DOI. | Open resource |