Spatial Domain Transfer: Cross-regional Paddy Rice Mapping with a Few Samples Based on Sentinel-1 and Sentinel-2 Data on GEE

The global population is on the rise, leading to an increased demand for food resources. Accurate mapping and monitoring of paddy rice fields have become crucial for effective food management and yield estimation. Several large-scale paddy rice mapping products with medium-to-high resolution have been made by using threshold-based phenological methods. The main shortcoming is that paddy rice varies in different regions due to differences in climate and planting habits, which results in the limitations of fixed phenological indicator thresholds for large-scale paddy rice mapping. Therefore, paddy rice samples from different regions are needed to account for regional differences between paddy rice and improve paddy rice mapping accuracy. To address two key shortcomings in existing sample generation methods (the non-robustness of DTW distance and the limitation of fixed phenological indicator thresholds for generating samples) and the three difficulties of cross-regional sample generation (different planting times, growth cycles, and planting intensities), this study proposed a novel approach for cross-regional paddy rice sample generation based on spatial domain transfer, achieving cross-regional paddy rice mapping. The accuracy results (OA of 95.16–97.54%) in three study regions with different climatic and topographic conditions in China and improved accuracy (OA improvement of 5.89–19.06%) compared with the method of NESEA-Rice10, proved the effectiveness of the proposed method and the potential of the proposed method in large-scale paddy rice mapping. The proposed method can obtain high-accuracy paddy rice mapping results in regions without paddy rice samples and be applied to the mapping of other crops and land cover types.