CSTN: A cross-region crop mapping method integrating self-training and contrastive domain adaptation

• We proposed a new method to enhance cross-region crop mapping. • The method shows superior transferability compared to state-of-the-art methods. • The method is proven feasible for early-season crop mapping. • We analyzed the model interpretability in three ways. Crop mapping is essential for agricultural management and food production monitoring, but challenges like limited crop labels and poor model generalization significantly hinder large-scale crop mapping. Here, we introduce a novel Contrastive Self-Training Network (CSTN), integrating a self-training strategy and contrastive domain adaptation (CDA) for cross-region crop mapping. CSTN uses pseudo-labels in the target region generated by the self-training strategy to assist supervised learning, and aligns features across regions using class-aware prototypes. Qualitative and quantitative evaluations demonstrate that CSTN significantly outperforms state-of-the-art methods with a 12.29 % increase in average F1-score, particularly in maize identification. Moreover, CSTN also enables early-season crop classification for pre-harvest decision-making applications. The interpretability of the model is demonstrated through an in-depth analysis of feature map visualizations, attention map visualizations, and the effectiveness of the modules. This study provides a robust method for enhancing large-scale crop mapping and facilitating more accurate and timely agricultural practices.