Full cycle rice growth monitoring with dual-pol SAR data and interpretable deep learning

Addressing challenges in crop growth monitoring, such as limited assessment dimensions, incomplete coverage of growth cycles, and limited deep learning (DL) interpretability, a novel dual-pol SAR rice growth monitoring method using a new crop growth index (CGI) and an interpretable DL architecture is proposed. Initially, radar vegetation indices, polarimetric decomposition parameters, and backscattering coefficients characterize rice growth in multiple dimensions. Subsequently, a CGI is designed, combining fractional vegetation cover and leaf chlorophyll content to accurately depict rice growth status, capturing both the structural and physiological activity of rice. Finally, an interpretable DL architecture incorporating a feature selection explainer and a feature-aware enhanced segmentation model is introduced. This architecture incorporates feature-wise variable learning networks, interprets the importance of individual features, and optimizes the feature composition, significantly enhancing the interpretability of the DL model. This architecture is also applicable to other neural networks. The method is applied in Suihua City, Heilongjiang Province, China, using Sentinel-1 dual-pol data from 2022 and 2023. Experiments indicate that the proposed method achieves an overall accuracy of 90.57% in the test set and a generalization accuracy of 90.43%. The integration of CGI and the interpretable DL architecture enhances the reliability of rice growth monitoring results.