A Hybrid RTM-Informed Machine Learning Framework with Crop-Specific Canopy Structural Parameterization for Crop Fractional Vegetation Cover Estimation

Fractional vegetation cover of crops (CropFVC) is a critical indicator for remote sensing-based crop monitoring. However, existing inversion models are largely developed for general vegetation types, limiting their effectiveness for crop-specific applications. Here, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model. The model was validated with 43343 CropFVC samples of four major crops (winter wheat, rice, maize, and soybean) across China during March to August 2024, spanning key phenological stages, and further compared against SNAP (10 m) and GEOV3 (300 m) products. Results showed that (1) the proposed model achieved stable performance across diverse canopy structures, with average RMSE < 9.3% for wheat, rice, maize, and soybean; (2) compared with SNAP (10 m), RMSE decreased by 4.83%, 3.10%, 7.51%, and 8.63% for wheat, rice, maize, and soybean, respectively; compared with GEOV3 (300 m), reductions reached 7.88%, 9.49%, 13.63%, and 19.75%, respectively. Further observations showed that the model-derived CropFVC captured intra-field variability and abnormal crop conditions well, enabling more accurate monitoring of crop-specific FVC dynamics across phenological stages. The proposed operational framework enhances CropFVC estimation by improving canopy structural representation and reducing retrieval bias. By enabling more accurate 10 m CropFVC mapping at the field scale, the crop-specific approach provides practical support for precision agriculture and crop-related food security monitoring.