Divergent GPP dynamics in alpine and temperate grasslands: Hierarchical climatic controls across the Qinghai-Tibetan and Mongolian Plateaus

Alpine grasslands on the Qinghai-Tibet Plateau and temperate grasslands on the Mongolian Plateau are key components of the global carbon cycle but differ markedly in their responses to climate change. To investigate the spatiotemporal variations in Gross Primary Productivity (GPP) and their response to climate change in these two types of grasslands, we developed a novel Random Forest Regression-Light Use Efficiency-Solar Induced Fluorescence (RFR-LUE-SIF) model that integrates machine-learning regression with physiological efficiency principles and satellite-derived SIF observations. This framework bridges tower-based GPP observations with large-scale remote-sensing estimates, improving model interpretability and accuracy. The model reproduced observed GPP with high fidelity (R 2 = 0.91), identifying EVI, NIRv, and GOSIF as the most influential predictors. Spatially, alpine grassland GPP decreases from southeast to northwest, while temperate grassland GPP declines from northeast to southwest. From 2001 to 2023, both grassland types exhibited increasing GPP trends, with temperate grasslands showing a faster rise, indicating stronger climatic sensitivity. Further, partial correlation analysis and Structural Equation Modeling (SEM) reveal that alpine grassland productivity is generally more sensitive to temperature, particularly under adequate moisture conditions, whereas temperate grasslands exhibited stronger dependence on precipitation and vapor pressure deficit (VPD). The proposed RFR-LUE-SIF model provides a scalable, data-driven, and physiologically consistent approach for assessing grassland carbon dynamics and their hierarchical climatic responses across contrasting ecosystems.