Monthly Soil Respiration in China's Forests: Spatiotemporal Patterns and Drivers Over 21 Years.

Forests play a critical role as terrestrial carbon sinks in mitigating climate change. However, accurate quantification of soil respiration (Rs)-the primary CO2 efflux from forests-remains challenging due to existing studies' overreliance on annual-scale estimates, which obscure fine-scale spatiotemporal dynamics and key drivers of Rs. Here, we developed a 500 m resolution monthly Rs dataset for China's forests (2000-2020) using remote sensing data and a geographically weighted machine learning model. A geographically weighted extreme gradient boosting model achieved the highest accuracy in predicting monthly Rs (R2 = 0.74, RMSE = 0.8 g C m-2 day-1). The mean total annual Rs from 2000 to 2020 was 2.32 ± 0.09 Pg C year-1, with summer contributing most and winter least. Annual total Rs and seasonal total Rs showed significant increasing trends across China's forests from 2000 to 2020, with the strongest increases in southern China's young/middle-aged natural management forests and plantations. The relationships between Rs and its driving factors varied: gross primary productivity (GPP) was the primary driver of annual Rs across all forest management and age classes. Seasonally, temperature dominated Rs in spring/winter, and GPP dominated summer Rs across all forest management and age classes, while precipitation effects varied with management/age classes and season. Our findings highlight the necessity of monthly-scale analysis and the significant role of forest management and age in modulating Rs variability.