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Reliable and accurate rice mapping using synthetic aperture radar (SAR) in cloudy and rainy areas is essential for achieving the United Nations Sustainable Development Goal 2 of 2030. An interpretable deep learning SAR rice area mapping method is proposed in this letter to suppress the interference of wetlands and other land covers to multitemporal SAR rice area mapping and improve the accuracy and confidence of the “black box” deep learning model results. Combining the temporal backscattering coefficients and interferometric coherence, three interpretable temporal features are extracted to effectively distinguish rice. Then, the explainable feature-aware network (XFANet), which can provide the learned importance weights of the normalization methods as self-interpretation, is constructed, and the pixel-wise gradient-weighted class activation mapping (PGCAM) post-hoc interpretation method is introduced to interpret the feature variation within the model. The experimental results in the Kampong Chhang and Kampong Chham provinces of Cambodia show that the proposed three interpretable features well suppressed the wetland disturbance to rice. With high interpretability, the overall accuracy (OA) of XFANet reaches 93.43%. [ABSTRACT FROM AUTHOR]