Remote Sensing and Machine Learning Uncover Dominant Drivers of Carbon Sink Dynamics in Subtropical Mountain Ecosystems

作者：Leyan Xia, Hongjian Tan, Jialong Zhang, Kun Yang, Chengkai Teng, Kai Huang, Jingwen Yang, Tao Cheng · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17162843 · 被引用次数：5 · 研究领域：Atmospheric and Environmental Gas Dynamics、Remote Sensing in Agriculture、Fire effects on ecosystems

Net ecosystem productivity (NEP) serves as a key indicator for assessing regional carbon sink potential, with its dynamics regulated by nonlinear interactions among multiple factors. However, its driving factors and their coupling processes remain insufficiently characterized. This study investigated terrestrial ecosystems in Yunnan Province, China, to elucidate the drivers of NEP using 14 environmental factors (including topography, meteorology, soil texture, and human activities) and 21 remote sensing features. We developed a research framework based on “Feature Selection–Machine Learning–Mechanism Interpretation.” The results demonstrated that the Variable Selection Using Random Forests (VSURF) feature selection method effectively reduced model complexity. The selected features achieved high estimation accuracy across three machine learning models, with the eXtreme Gradient Boosting Regression (XGBR) model performing optimally (R2 = 0.94, RMSE = 76.82 gC/(m2·a), MAE = 55.11 gC/(m2·a)). Interpretation analysis using the SHAP (SHapley Additive exPlanations) method revealed the following: (1) The Enhanced Vegetation Index (EVI), soil pH, solar radiation, air temperature, clay content, precipitation, sand content, and vegetation type were the primary drivers of NEP in Yunnan. Notably, EVI’s importance exceeded that of other factors by approximately 3 to 10 times. (2) Significant interactions existed between soil texture and temperature: Under low-temperature conditions (−5 °C ...