Abstract
As a staple food crop in China, paddy rice is one of the most widely grown crops in the country. Thanks to its abundant water resources, the Yangtze River has superiority in paddy rice planting. The prompt and accurate monitoring of paddy rice planting information plays an important role in providing essential technical support to safeguard China's food security. However, there is a scarcity of studies on the extraction of paddy rice planting areas along the Yangtze River using the remote sensing technology. In this paper, guided by paddy rice growth phenology information, we selected the Sentinel-2 satellite imagery that covered the paddy rice growing period (from May to September) along the Yangtze River in 2020. On the basis of high-spatial resolution images combined with field investigation, we obtained the real distribution map of the paddy rice planting area in Menghe Town and selected samples and validation samples from the map. Based on the maximum likelihood, neural network, SVM (Support Vector Machine) and other machine learning methods, we carried out the paddy rice extraction research, compared and analyzed the accuracy of extraction results of NDVI (Normalized Difference Vegetation Index) and REP (Red Edge Position Index) spectral index sets. The results show that the REP index sets of the three time phases in May, August and September are most effectively extracted using the neural network method. The overall accuracy of the results is 92.73%, with a corresponding Kappa coefficient of 0.72. We achieved global applicability by optimizing key parameters such as timing, spectral bands, and classifier methods for paddy rice extraction. Additionally, we conducted post-processing to refine the results. Finally, we obtained a vector dataset of paddy rice growing areas along the Yangtze River (Jiangsu section) in 2020, with an overall classification accuracy of 87.15%. This dataset is expected to provide a reference for further monitoring the paddy rice planting areas and enhancing the accuracy of grain production estimates throughout the Yangtze River region.
Published Version
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