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Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

by 1,2, 1,* and 1
1
School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China
2
Zhengzhou Vocational College of Industrial Safety, Zhengzhou 451192, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jan Wesseling
Water 2021, 13(14), 1981; https://doi.org/10.3390/w13141981
Received: 31 May 2021 / Revised: 17 July 2021 / Accepted: 17 July 2021 / Published: 19 July 2021
(This article belongs to the Section Hydrology)
Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data. View Full-Text
Keywords: surface soil moisture; sentinel-1 SAR; Sentinel-2; vegetation water content; water cloud model; support vector regression surface soil moisture; sentinel-1 SAR; Sentinel-2; vegetation water content; water cloud model; support vector regression
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MDPI and ACS Style

Li, Y.; Zhang, C.; Heng, W. Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2. Water 2021, 13, 1981. https://doi.org/10.3390/w13141981

AMA Style

Li Y, Zhang C, Heng W. Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2. Water. 2021; 13(14):1981. https://doi.org/10.3390/w13141981

Chicago/Turabian Style

Li, Yan, Chengcai Zhang, and Weidong Heng. 2021. "Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2" Water 13, no. 14: 1981. https://doi.org/10.3390/w13141981

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