Solar-induced chlorophyll fluorescence (SIF), a byproduct of photosynthesis, exhibits a strong correlation with electron transport in the photosystem and functions as a reliable indicator of vegetation photosynthetic activity. However, its broad application in global productivity assessments, vegetation growth monitoring, and environmental stress detection remains constrained by data sparsity and insufficient long-term time series. To address these limitations, research focusing on SIF prediction through multiple explanatory factors has gained prominence. Studies incorporating multi-source explanatory factors have partially addressed issues concerning spatial discontinuity and low resolution of SIF products from atmospheric satellite sensors. However, a significant challenge persists: optical remote sensing imaging’s inherent susceptibility to cloud reflection or absorption results in lost ground-level information. Consequently, optical remote sensing-based vegetation indices cannot retrieve information from cloud-obscured areas, compromising SIF prediction accuracy. In contrast to optical remote sensing, microwave remote sensing enable ground-level information retrieval because of microwave penetrating non-precipitating and some precipitation clouds. This characteristic makes microwave remote sensing particularly suitable for continuous monitoring in cloud-prone regions. Microwave remote sensing-derived vegetation indices demonstrate both robust cloud penetration capabilities and reflection of vegetation’s internal physiological characteristics. Therefore, this study introduces a method for SIF prediction based on microwave vegetation indices, facilitating surface information acquisition in cloud-covered areas and enhancing SIF prediction accuracy.

This study utilizes global ozone monitoring experiment-2 (GOME-2) SIF data from 2013 to 2015, integrating it with MODIS-derived normalized bidirectional reflectance (NBAR), land surface temperature (LST), photosynthetically active radiation (PAR), and vegetation optical depth datasets. A bilinear interpolation or aggregation method is then applied to generate 0.25° grid data for dataset construction. The research implements machine learning algorithms for model training and employs cross-validation for hyperparameter optimization, establishing a microwave vegetation index-based SIF prediction model. Additionally, independent satellite-based observation data, including TROPOMI SIF, OCO-2 SIF, and MODIS gross primary productivity (GPP), were collected. The model’s performance evaluation metrics such as the coefficient of determination (R2) and root mean square error (RMSE) are used to assess differences between predicted results and original satellite data.

The microwave vegetation index-based SIF prediction model developed in this study demonstrates validation results against multi-source satellite SIF and GPP products, as illustrated in Fig. 3, achieving R2 values of 0.921, 0.935, 0.923, 0.875, 0.812, and 0.802. The study compared the effectiveness of microwave and optical vegetation indices for SIF prediction under extreme satellite observation conditions. The experimental results indicate that for each 10% increase in effective cloud coverage, the R2 value of the optical vegetation index-based SIF prediction model decreases by 0.042, 0.041, and 0.031 in 2013, 2014, and 2015, respectively (Fig. 6). In comparison, the R2 values of the proposed model decrease by only 0.025, 0.022, and 0.017 (Fig. 6). The decay rate of R2 values for the proposed model decreased by 43%, indicating that the decay rate of SIF prediction accuracy based on optical remote sensing exceeds that of the presented model by a factor of 1.7 (Table 3).

This study presents an SIF prediction method based on microwave vegetation indices, and the potential for satellite-scale SIF prediction of the method is investigated. The method’s accuracy undergoes quantitative evaluation using SIF/GPP products from multi-source remote sensing satellites in orbit (including GOME-2 SIF, TROPOMI SIF, OCO-2 SIF, and MODIS GPP). The results demonstrate that the microwave vegetation indices-based model achieves an R2 value of up to 0.935. Additionally, compared to optical remote sensing-based SIF prediction models, the microwave-based model exhibits a 43% decay rate with increasing effective cloud coverage. This finding confirms that microwave remote sensing’s superior penetration capability effectively mitigates cloud contamination effects on SIF predictions. However, microwave data typically offer lower spatial resolution than optical data, limiting the spatial detail in SIF predictions. For instance, in the current dataset, the microwave vegetation index has a spatial resolution of 0.25°, whereas the optical vegetation index provides a finer resolution of 0.05°. Consequently, applications requiring high spatial resolution should employ optical vegetation index-based methods for SIF prediction, while areas with substantial cloud cover would benefit from microwave vegetation index-based methods for enhanced accuracy.