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Open AccessArticle

Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

by 1,2, 1,2,* and 1,2
1
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou 221116, China
2
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Water 2021, 13(2), 135; https://doi.org/10.3390/w13020135
Received: 8 December 2020 / Revised: 5 January 2021 / Accepted: 5 January 2021 / Published: 8 January 2021
(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Water Management)
The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction (fveg) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (R2) about 0.7390. View Full-Text
Keywords: soil moisture; AIEM; modified water cloud model; Oh model; effective roughness soil moisture; AIEM; modified water cloud model; Oh model; effective roughness
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MDPI and ACS Style

Zhang, M.; Lang, F.; Zheng, N. Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data. Water 2021, 13, 135. https://doi.org/10.3390/w13020135

AMA Style

Zhang M, Lang F, Zheng N. Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data. Water. 2021; 13(2):135. https://doi.org/10.3390/w13020135

Chicago/Turabian Style

Zhang, Min; Lang, Fengkai; Zheng, Nanshan. 2021. "Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data" Water 13, no. 2: 135. https://doi.org/10.3390/w13020135

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