Yongli Guo

The abrupt change of canopy structure at the flowering stage causes a long-standing monitoring blind zone for SAR-based inversion of winter rapeseed. This study proposed a feature-based inversion method for the Total Photosynthetic Area Index (TPAI) of winter rapeseed, in which a multi-dimensional spatiotemporal feature framework was constructed by integrating radar-derived features such as polarization interactions and spatial texture with acquisition-date-derived phenological priors including day-of-year harmonics and normalized z-score features, and a regularized linear Ridge model was selected from six machine-learning candidates. Evaluated under a spatial five-fold cross-validation protocol with geographically disjoint validation folds, the model achieved an overall R² of 0.615 and an RMSE of 1.033 m² m⁻², against an R² of 0.584 for a baseline encoding the growth calendar alone, indicating only a modest overall gain from the radar-derived features; the radar-derived features nevertheless delivered their largest within-stage improvement over this baseline at the flowering stage, suggesting that they can partly alleviate—though not resolve—the sensitivity loss of conventional single-polarization features during flowering. Error magnitudes were insensitive to systematic bias of up to ±40% in the SAI scaling assumption, and full-coverage TPAI mapping across five phenological stages, produced from seam-corrected Sentinel-1 mosaics, matched the observed growth trajectory. Given that the SAI was estimated indirectly and the analysis relied on a single growing season at a single study area, these results should be regarded as site- and season-specific. The framework therefore shows promise as a spatially validated, feature-based pathway toward TPAI monitoring of winter rapeseed in cloudy-rainy regions, with further validation across multiple seasons and regions required before operational application.