Gross primary productivity (GPP) is a crucial indicator for understanding the global carbon cycle and climate change. Sun-Induced Chlorophyll Fluorescence (SIF) provides a direct link to plant photosynthesis, offering a novel approach for GPP estimation in terrestrial ecosystems. Given the complex factors influencing the canopy SIF–GPP relationship and the limited generality of empirical linear models, we developed a Light Use Efficiency (LUE) model incorporating the photochemical reflectance index (PRI) and structural vegetation indices, with a nonlinear function fitted using support vector machine regression. The model was evaluated across multiple vegetation types, SIF products, and vegetation indices to identify optimal SIF–VI combinations. Results indicate that training by vegetation type improves accuracy by 8.5%–14.4%, with shrublands and evergreen broadleaf forests showing the best performance. The combination of GOSIF with NDVI achieved the highest overall estimation accuracy across all vegetation types. Additionally, the combination of GOME-2 SIF with NDVI × NIRv performed best in inland arid low-GPP regions, while TCSIF with NDVI × NIRv was most accurate in cold high-latitude low-GPP regions. All three combinations effectively captured interannual GPP variations at individual sites. Using these combinations, monthly GPP from 2007 to 2014 was estimated and compared with GPP-MODIS and GPP-GLASS datasets, showing consistent temporal and spatial patterns that reflect seasonal dynamics in the Northern Hemisphere. These findings demonstrate that integrating SIF with targeted vegetation indices enhances GPP estimation and provides a robust framework for large-scale photosynthesis monitoring.