Accurate large-scale crop yield estimation remains challenging due to the indirect nature of most remote sensing indicators of photosynthesis. Solar-induced chlorophyll fluorescence (SIF) directly traces photosynthetic activity and offers a promising alternative, yet its mechanistic integration into yield models is still limited. In this study, given that the two different photosynthesis pathways C3 and C4 differ greatly in their environmental requirements, we apply this framework to estimate crop yield for two crops (C3: winter wheat and C4: summer maize) in the North China Plain. This framework is based on the mechanistic light reactions (MLRs) model utilizing remotely sensed SIF as a major input. The performance of the model was rigorously evaluated against official statistical yield records. Results indicate that from 2019 to 2022, both SIF and gross primary productivity (GPP) exhibited consistent and synchronous seasonal cycles, with clearly distinguishable dual-growing seasons corresponding to winter wheat and summer maize. Significant linear correlations between GPP and SIF were observed for both C3 and C4 crops, with stronger associations evident in C4 species. In addition, the strength of the SIF-yield relationship varied across phenological stages, and the cumulative SIF over the entire growth period (SIFtotal) demonstrated the strongest correlation with yield. The MLR-SIF model achieved robust estimation accuracy for both crops (C3: R2 = 0.6195, MAE = 0.6802 ton ha−1, RMSE = 0.8301 ton ha−1; C4: R2 = 0.6093, MAE = 0.6054 ton ha−1, RMSE = 0.7483 ton ha−1). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, offering a novel satellite-based approach for agricultural monitoring and providing scientific support for crop management, policy decisions and regional/global food security.

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