Estimating Global Soil Heterotrophic Respiration Based on Environmentally Similar Zones and Remote Sensing Data

Accurately estimating global soil heterotrophic respiration (R-H) is crucial in evaluating whether terrestrial ecosystems act as carbon sources or sinks. However, current global R-H estimates were significantly restricted by the scarcity of in situ R-H observations and their biased distribution, leading to considerable uncertainties. This study developed a novel data-driven model of global R-H based on the environmentally similar zones of global in situ R-H sites with daily and subdaily observations and remote sensing data with high spatial and temporal resolutions. Compared with the unified modeling method using all available data as the training samples of data-driven models, our zone modeling method was more accurate. The relationship between observed and predicted R-H was improved, with the R-2 value increasing from 0.41 to 0.53 and the RMSE decreasing from 0.87 to 0.78 g C m(-2) d(-1). Our study effectively improved the problem that the data-driven models were highly affected by the spatial representativeness of in situ R-H observations and achieved a significantly improved accuracy for global R-H estimation entirely based on remote sensing data. Future research focusing on improving the sparse sampling of in situ R-H sites and the availability of remote sensing data will help to reduce the uncertainties of our study.