A New Method for Winter Wheat Mapping Based on Spectral Reconstruction Technology

文献类型： 外文期刊

第一作者： Li, Shilei

作者： Li, Shilei;Li, Fangjie;Gao, Maofang;Li, Zhaoliang;Leng, Pei;Duan, Sibo;Ren, Jianqiang;Li, Shilei;Li, Fangjie;Gao, Maofang;Li, Zhaoliang;Leng, Pei;Duan, Sibo;Ren, Jianqiang;Li, Zhaoliang

作者机构：

关键词： Sentinel-2 satellite; NDVI time series; singular value decomposition (SVD); winter wheat mapping; crop classification

期刊名称：REMOTE SENSING （ 影响因子：4.509； 五年影响因子：5.001 ）

ISSN：

年卷期： 2021 年 13 卷 9 期

页码：

收录情况： SCI

摘要： Timely and accurate estimation of the winter wheat planting area and its spatial distribution is essential for the implementation of crop growth monitoring and yield estimation, and hence for the development of national agricultural production and food security. In remotely sensed winter wheat mapping based on spectral similarity, the reference curve is obtained by averaging multiple standard curves, which limits mapping accuracy. We propose a spectral reconstruction method based on singular value decomposition (SR-SVD) for winter wheat mapping based on the unique growth characteristics of crops. Using Sentinel-2 A/B satellite data, we tested the SR-SVD method in Puyang County, and Shenzhou City, China. Performance was increased, with the optimal overall accuracy and the Kappa of Puyang County and Shenzhou City were 99.52% and 0.99, and 98.26% and 0.97, respectively. We selected the spectral angle mapper (SAM) and Euclidean Distance (ED) as the similarity measures. Compared to spectral similarity methods, the SR-SVD method significantly improves mapping accuracy, as it avoids excessive extraction, can identify more detailed information, and is advantageous in distinguishing non-winter wheat pixels. Three commonly used supervised classification methods, support vector machine (SVM), maximum likelihood (ML), and minimum distance (MD) were used for comparison. Results indicate that SR-SVD has the highest mapping accuracy and greatly reduces the number of misidentified pixels. Therefore, the SR-SVD method can achieve high-precision crop mapping and provide technical support for monitoring regional crop planting structure information.

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