Global mapping of vegetation solar-induced chlorophyll fluorescence over 1981−2023 with a light-use-efficiency constrained machine-learning approach

Satellite solar-induced chlorophyll fluorescence (SIF) provides a direct proxy for vegetation photosynthesis, yet the short lifespan and degradation of satellite sensors limit long-term global SIF records. Here, we developed a hybrid modeling framework integrating light use efficiency (LUE) and machine learning to reconstruct a spatiotemporally continuous global long-term daily and monthly SIF dataset (LTSIF) at 0.05° resolution from 1981 to 2023. Key drivers were identified using a far-red SIF decomposition framework, LUE theory, and random forest (RF) modeling. Hybrid models combining LUE with RF, extreme gradient boosting, and neural networks were developed, among which the LUE-RF model achieved the best performance (R2 = 0.97, RMSE = 0.08 mW m−2 sr−1 nm−1, KGE = 0.97) and showed robust performance across vegetation types and climate zones. Independent validation against tower-based SIF, OCO-2 SIF, and widely reconstructed SIF datasets demonstrated that LTSIF reliably captured global photosynthetic dynamics. From 1981 to 2023, LTSIF revealed increasing photosynthetic activity over 89.01% of global vegetated areas, with a global mean trend of 0.016 mW m−2 sr−1 nm−1 per decade (p < 0.01). LTSIF provides a valuable long-term dataset for assessing terrestrial photosynthesis responses to climate change.