Abstract:

[Objective] Rice is one of the most important staple crops in China. Accurate monitoring of rice planting areas is vital for ensuring national food security, evaluating greenhouse gas emissions, optimizing water resource allocation, and maintaining agricultural ecosystems. In recent years, the integration of remote sensing technologies—particularly the fusion of optical and synthetic aperture radar (SAR) data—has significantly enhanced the capacity to monitor crop distribution, even under challenging weather conditions. However, many current studies still rely heavily on phenological features captured at specific key stages, such as the transplanting phase, while overlooking the complete temporal dynamics of vegetation and water-related indices throughout the entire rice growth cycle. Therefore, there is an urgent need for a method that fully leverages the time-series characteristics of remote sensing indices to enable accurate, scalable, and timely rice mapping. [Methods] Focusing on the Hangjiahu Plain, a typical rice-growing region in eastern China, a novel approach—Dynamic NDVI-SDWI Fusion Method for Rice Mapping (DNSF-Rice) was proposed in this research to accurately extract rice planting areas by synergistically integrating Sentinel-1 SAR and Sentinel-2 optical imagery on the Google Earth Engine (GEE) platform. The methodological framework included the following three steps: First, using Sentinel-2 imagery, a time series of the Normalized Difference Vegetation Index (NDVI) was constructed. By analyzing its temporal dynamics across key rice growth stages, potential rice planting areas were identified through a threshold-based classification method; Second, a time series of the Sentinel-1 Dual-Polarized Water Index (SDWI) was generated to analyze its dynamic changes throughout the rice growth cycle. A thresholding algorithm was then applied to extract rice field distribution based on microwave data, considering the significant irrigation involved in rice cultivation. Finally, the spatial intersection of the NDVI-derived and SDWI-derived results was intersected to generate the final rice planting map. This step ensures that only pixels exhibiting both vegetation growth and irrigation signals were classified as rice. The classification datasets spanned five consecutive years from 2019 to 2023, with a spatial resolution of 10 m. [Results and Discussions] The proposed method demonstrated high accuracy and robust performance in mapping rice planting areas. Over the study period, the method achieved an average overall accuracy of 96% and an F₁-Score exceeding 0.96, outperforming several benchmark products in terms of spatial consistency and precision. The integration of NDVI and SDWI time-series features enabled effective identification of rice fields, even under the challenging conditions of frequent cloud cover and variable precipitation typical in the study area. Interannual analysis revealed a consistent increase in rice planting areas across the Hangjiahu Plain from 2019 to 2023. The remote sensing-based rice area estimates were in strong agreement with official agricultural statistics, further validating the reliability of the proposed method. The fusion of optical and SAR data proved to be a valuable strategy, effectively compensating for the limitations inherent in single-source imagery, especially during the cloudy and rainy seasons when optical imagery alone was often insufficient. Furthermore, the use of GEE facilitated the rapid processing of large-scale time-series data, supporting the operational scalability required for regional rice monitoring. This study emphasized the critical importance of capturing the full temporal dynamics of both vegetation and water signals throughout the entire rice growth cycle, rather than relying solely on fixed phenological stages. [Conclusions] By leveraging the complementary advantages of optical and SAR imagery and utilizing the complete time-series behavior of NDVI and SDWI indices, the proposed approach successfully mapped rice planting areas across a complex monsoon climate region over a five-year period. The method has been proven to be stable, reproducible, and adaptable for large-scale agricultural monitoring applications. The findings provide valuable methodological support for crop mapping in cloud-prone regions and offer insights for agricultural resource management and food security decision-making.

Key words: remote sensing, GEE, cultivation area, Sentinel-1, SAR, plain