1. GRID PARAMETERS
grid-name: Geographic Lat/Lon
pixel-size: 1/2 degrees
size-x: 360
size-y: 720
upper-left-lat: 90.0
upper-left-lon: -180.0
lower-right-lat: -90.0
lower-right-lon: 180.0

2. DATA FORMAT
period: 1982-2018
datatype: .tif 
fill value: NaN
Unit: g C/m2/yr

3. DATA READ EXAMPLE (For MATLAB)
filename = ‘Rh_RF_ensemble_mean_1982_2018.tif’;
[A, R] = geotiffread(filename);

4. Additional Description
This dataset provides a global gridded product at 0.5-degree resolution of predicted annual soil heterotrophic respiration (Rh) during 1982-2018. A Random Forest (RF) approach was used to derive the predicted Rh trained with 761 observations with 19 predictors (including climate, vegetation, soil biotic and abiotic variables). To improve the RF model accuracy, we developed a stratified 10-fold cross validation, by grouping our dataset into three climate zone classes (i.e., tropical, temperate and boreal zones) and ensuring each class was approximately equally represented across each fold. The average predicted map across the RF model ensemble was used as the final product.

5. DATA CITATION
He, Y., Ding, J., Dorji, T., Wang, T., Li, J., & Smith, P. (2022). Observation-based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models. Global Change Biology, 00, 1–13. https://doi.org/10.1111/gcb.16286.