Abstract. Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. However, there is no high-resolution map of rice covering all of China. This study developed a new rice mapping method by combining optical and synthetic aperture radar (SAR) images in cloudy areas based on the time-weighted dynamic time warping (TWDTW) method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at 10 or 20-m resolution. The accuracy was examined by using 108195 survey samples and county-level statistical data. On average, the user’s, producer’s, and overall accuracy over all investigated provincial administrative regions were 73.08 %, 82.81 %, and 85.23 %, respectively. Compared with the statistical data from 2017 to 2019, the distribution map explained 83 % of the spatial variation of county-level planting areas on average. The distribution maps can be obtained at https://doi.org/10.57760/sciencedb.06963 (Shen et al., 2023).
Introduction: Timely and accurately mapping the spatial distribution of rice is of great significance for estimating crop yield, ensuring food security and freshwater resources, and studying climate change. Double-season rice is a dominant rice planting system in China, but it is challenging to map it from remote sensing data due to its complex temporal profiles that requires high-frequency observations.Methods: We used an automated rice mapping method based on the Synthetic Aperture Radar (SAR)-based Rice Mapping Index (SPRI), that requires no samples to identify double-season rice. We used the Sentinel-1 SAR time series data to capture the growth of rice from transplanting to maturity in 2018, and calculated the SPRI of each pixel by adaptive parameters using cloud-free Sentinel-2 imagery. We extensively evaluated the methods performance at pixel and regional scales.Results and discussion: The results showed that even without any training samples, SPRI was able to provide satisfactory classification results, with the average overall accuracy of early and late rice in the main producing provinces of 84.38% and 84.43%, respectively. The estimated area of double-season rice showed a good agreement with county-level agricultural census data. Our results showed that the SPRI method can be used to automatically map the distribution of rice with high accuracy at large scales.
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