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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
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Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery

机译：Sentinel-1和Sentinel-2影像揭示了冬小麦的作物含水量

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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.
机译：这项研究旨在使用基于高时空分辨率的卫星图像，有效地估算冬小麦的作物含水量。 Sentinel-1卫星收集的合成孔径雷达（SAR）数据和Sentinel-2卫星的光学图像分别用于建立冬小麦作物水分含量的反演模型。在Sentinel-1方法中，通过图像的Sentinel-1反向散射系数构建了多个增强的雷达指标，并选择了对土壤水分最敏感的一个作为水云模型的输入参数。最后，建立了冬小麦作物水分含量反演模型。在Sentinel-2方法中，通过图像的Sentinel-2光谱特征对几种光学植被指数进行了灰色关联分析，并选择了三种植被指数进行多元线性回归建模，以检索小麦作物的水分含量。 58个地面样本用于建模和验证。与基于Sentinel-1 SAR的反演模型（R = 0.433，RMSE = 0.026和nRMSE）相比，基于Sentinel-2光学图像的含水量反演模型具有更高的验证精度（R = 0.632，RMSE = 0.021和nRMSE = 19.65％）。 = 21.24％）。这项研究为利用Sentinel系列卫星的数据估算小麦作物的水分含量提供了参考。

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