A robust index to extract paddy fields in cloudy regions from SAR time series

Timely and accurate mapping of paddy rice cultivation is needed for maintaining sustainable rice production, ensuring food security, and monitoring water usage. Synthetic Aperture Radar (SAR) remote sensing plays an important role in the continuous monitoring and mapping of rice cultivation in cloudy regions since it is not affected by weather conditions. To date, most SAR imagery-based rice mapping methods rely on prior knowledge (e.g., the planting date) and empirical thresholds for specific regions, which limits their applications in large spatial scales. To tackle this limitation, this study proposed a new SAR-based Paddy Rice Index (SPRI) to quantify the probability of land patches planted paddy rice. SPRI fully uses unique features of paddy rice during the transplanting-vegetative period in the Sentinel-1 VH backscatter time series. With the assistance of cloud-free Sentinel-2 images, SPRI can be calculated for each cropland object with adaptive parameters. Then, SPRI values of cropland objects can be converted to paddy rice maps using the binary-classification threshold. The proposed SPRI method was tested at five sites with diverse climate conditions, landscape complexity and cropping systems. Results show that the SPRI was able to produce an accurate classification map with an overall accuracy of over 88% and an F1 score of over 0.86 at all sites. Compared with the existing SAR-based rice mapping methods, our method performed much better in heterogeneous agricultural areas where rice is mosaiced with other crops. As SPRI does not need any prior knowledge, reference samples and many predefined parameters, it has high flexibility and applicability to support paddy rice mapping in large areas, especially for cloudy regions where optical remote sensing data is often not available.