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

Abstract

Forests play a critical role as terrestrial carbon sinks in mitigating climate change. However, accurate quantification of soil respiration (R_s)-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 R_s. Here, we developed a 500 m resolution monthly R_s 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 R_s (R2 = 0.74, RMSE = 0.8 g C m-2 day-1). The mean total annual R_s from 2000 to 2020 was 2.32 ± 0.09 Pg C year-1, with summer contributing most and winter least. Annual total R_s and seasonal total R_s 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 R_s and its driving factors varied: gross primary productivity (GPP) was the primary driver of annual R_s across all forest management and age classes. Seasonally, temperature dominated R_s in spring/winter, and GPP dominated summer R_s 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 R_s variability.