Spatiotemporal simulation and prediction of solar-induced chlorophyll fluorescence (SIF) across large-scale grasslands via multi-source data synergy: a case study of Northern China

Solar-induced chlorophyll fluorescence (SIF) is a direct quantitative indicator of photosynthesis. High-precision modeling and cross-scale prediction of SIF are crucial for deciphering multiscale ecological response mechanisms and assessing regional carbon sinks. However, existing SIF prediction models rely excessively on the simple stacking of spectral and meteorological variables, neglecting the process-based response mechanisms to stress factors. This limitation results in difficulties effectively capturing the nonlinear interactions among environmental factors and their compound effects on SIF within areas of high landscape heterogeneity. To address these limitations, this study taking the grasslands of Northern China as a case study, developed a multi-source data-driven spatiotemporal prediction framework for SIF. Through a synergistic multistep mechanism involving feature selection, spatiotemporal decoupling, hybrid modeling, and surface extension, this framework couples geographical spatiotemporal heterogeneity with ecological process-driven mechanisms. It achieved the high-precision simulation and dynamic prediction of SIF distribution across multiscale grassland areas. The results demonstrated that the hybrid LSTM + Transformer model significantly outperformed traditional architectures. Its gated memory units and self-attention modules performed synergistically to effectively capture cross-climatic zone photosynthetic synergy and the fluorescence suppression effect under low-temperature stress. Further analysis revealed that the fraction of absorbed photosynthetically active radiation (FPAR), the leaf area index (LAI), and surface solar radiation (SSR) predominantly govern SIF variations by regulating canopy light energy allocation and phenological rhythms. Future projections indicate that grasslands in Northern China will exhibit a trend of slow growth but enhanced spatial heterogeneity. This research not only provides critical methodological support for carbon sink function assessment and degradation restoration monitoring in Northern China’s grasslands and similar large-scale regions but also offers a novel perspective for advancing ecosystem process modeling under global change.