摘要：Soil heterotrophic respiration (RH) is one of the largestand most uncertain components of the terrestrial carbon cycle, directlyreflecting carbon loss from soils to the atmosphere. However, highvariations and uncertainties of RH existing in global carbon cycling modelsrequire RH estimates from different angles, e.g., a data-driven angle. Tofill this knowledge gap, this study applied a Random Forest (RF) algorithm(a machine learning approach) to (1) develop a globally gridded RH datasetand (2) investigate its spatial and temporal patterns from 1980 to 2016 atthe global scale by linking field observations from the Global SoilRespiration Database and global environmental drivers (temperature,precipitation, soil water content, etc.). Finally, a globally gridded RHdataset was developed covering from 1980 to 2016 with a spatial resolutionof half a degree and a temporal resolution of 1 year. Globally, the averageannual RH was 57.2±0.6 Pg C a−1 from 1980 to 2016, with asignificantly increasing trend of 0.036±0.007 Pg C a−2. However,the temporal trend of the carbon loss from RH varied in climate zones, andRH showed a significant and increasing trend in boreal and temperate areas.In contrast, such a trend was absent in tropical regions. Temperature-drivenRH dominated 39 % of global land and was primarily distributed at high-latitude areas. The areas dominated by precipitation and soil watercontent were mainly semiarid and tropical areas, accounting for 36 % and25 % of global land area, respectively, suggesting variations in thedominance of environmental controls on the spatial patterns of RH. Thedeveloped globally gridded RH dataset will further aid in the understanding ofthe mechanisms of global soil carbon dynamics, serving as a benchmark toconstrain terrestrial biogeochemical models. The dataset is publiclyavailable at https://doi.org/10.6084/m9.figshare.8882567(Tang et al., 2019a).
