文献类型： 外文期刊
作者： Han, Dong 1 ; Liu, Shuaibing 1 ; Du, Ying 1 ; Xie, Xinrui 1 ; Fan, Lingling 1 ; Lei, Lei 1 ; Li, Zhenhong 4 ; Yang, Hao 1 ;
作者机构： 1.Minist Agr China, Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China
2.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
3.China Univ Min & Technol Beijing, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China
4.Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
关键词： remote sensing; Sentinel-1; Sentinel-2; winter wheat; crop water content
期刊名称：SENSORS （ 影响因子：3.576； 五年影响因子：3.735 ）
ISSN：
年卷期： 2019 年 19 卷 18 期
页码：
收录情况： SCI
摘要： This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites.
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