A novel two-step method for ratoon rice mapping using Sentinel-1/2 time series

• Proposing a two-step ratoon rice (TSRR) mapping method. • Enabling large-scale ratoon rice mapping in cloud-prone regions with limited optical data. • The average overall accuracy (OA) and F1 score of ratoon rice mapping were 0.87 and 0.84, respectively. Ratoon rice plays a vital role in boosting land productivity and contributing to stable food supplies under the context of global climate change. This system offers these advantages by demonstrating the capacity to produce an additional grain yield of 5–6 t ha −1 from the ratoon season, while simultaneously reducing the growth duration by 44–48 days compared to conventional double-season rice cultivation. However, its precise spatial distribution remains unclear under varying climatic and surface conditions, and remote sensing-based monitoring of ratoon rice has received limited attention. Existing methods often struggle to accurately distinguish ratoon rice from other morphologically similar types, such as double-season rice, and are further hampered by frequent cloud cover and rainfall, compromising optical sensing effectiveness. This study proposes a two-step ratoon rice (TSRR) mapping method using multi-source remote sensing data at the parcel scale. The TSRR method integrates Sentinel-1A synthetic aperture radar (SAR) and Sentinel-2 optical imagery, utilizing the distinct growth characteristics of main-season and ratoon rice. It employs object-based segmentation and two novel indices—the SAR-based paddy rice index (SPRI) and the SAR-based ratoon rice index (SRRI)—without relying on detailed phenological information. Results indicate that the TSRR method effectively distinguishes ratoon rice from other paddy rice types, achieving an average overall accuracy (OA) of 0.87, with particularly high performance in separating ratoon rice from double-season rice. The TSRR method demonstrates strong robustness and transferability across different regions, which can provide a reliable solution for large-scale paddy rice mapping, especially in cloud-prone areas with limited optical data availability, and offers valuable support for crop monitoring, yield estimation, and national agricultural inventory initiatives.