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.
N: 90.0 S: -90.0 E: 180.0 W: -180.0
Distribution: ICARTT, NetCDF
|Temporal Extent:||Platform(s):||Environmental Modeling|
Variables mapped on uniform space-time grid scales with completeness and consistency
|Project Short Name||Campaigns||Project Dates|
|CMS||No campaigns listed.||No dates provided.|
|Coverage Type||Zone Identifier||Geometry||Granule Representation|
ORNL DAAC User Services Office, P.O. Box 2008, MS 6407, Oak Ridge National Laboratory
Oak Ridge, Tennessee 37831-6407
There are no listed data contacts for this collection.
Format: ICARTT, NetCDF
Format Type: Native