A Training-Independent Sentinel-1/2 Phenological Approach for Interpretable Paddy Rice Mapping in Heilongjiang, China

Accurate identification of paddy rice from satellite imagery is challenging in heterogeneous agricultural landscapes because water, wetland, and vigorous summer vegetation can exhibit spectral and radar responses similar to those of rice at individual observation dates. This study develops an interpretable, training-independent rice-mapping approach, meaning that the final classification does not rely on supervised classifier training, although manually interpreted locations were used for exploratory threshold selection. The approach identifies paddy fields from their characteristic transition from early-season inundation to subsequent canopy development. These phenological signals were integrated using explicit decision rules within a cropland-constrained domain, followed by persistent-water exclusion and spatial refinement. The framework was applied to the 2025 rice-growing season in the agricultural regions analyzed in Heilongjiang Province, China, using 30 m output mapping. Validation with 695 independent reference observations yielded an overall accuracy of 92.09%, Cohen’s kappa of 0.791, rice producer’s accuracy of 94.87%, user’s accuracy of 75.90%, and an F1-score of 84.33%. The high producer’s accuracy indicates effective detection of reference rice locations, whereas the lower user’s accuracy shows that commission error remains the principal limitation, particularly in areas with moisture and vegetation conditions resembling paddy fields. Sequential screening further showed that combining phenological evidence with cropland and water constraints substantially reduced the candidate mapping domain. The proposed approach provides an auditable alternative to training-dependent classification workflows and offers a transparent basis for repeatable rice mapping and agricultural monitoring, while requiring further evaluation across multiple years and regions before broader operational transfer.