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http://dx.doi.org/10.7780/kjrs.2021.37.3.12

Estimation of Soil Moisture Using Sentinel-1 SAR Images and Multiple Linear Regression Model Considering Antecedent Precipitations  

Chung, Jeehun (Graduate School of Civil, Environmental and Plant Engineering, Konkuk University)
Son, Moobeen (Graduate School of Civil, Environmental and Plant Engineering, Konkuk University)
Lee, Yonggwan (Graduate School of Civil, Environmental and Plant Engineering, Konkuk University)
Kim, Seongjoon (Department of Civil and Environmental Engineering, Konkuk University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.3, 2021 , pp. 515-530 More about this Journal
Abstract
This study is to estimate soil moisture (SM) using Sentinel-1A/B C-band SAR (synthetic aperture radar) images and Multiple Linear Regression Model(MLRM) in the Yongdam-Dam watershed of South Korea. Both the Sentinel-1A and -1B images (6 days interval and 10 m resolution) were collected for 5 years from 2015 to 2019. The geometric, radiometric, and noise corrections were performed using the SNAP (SentiNel Application Platform) software and converted to backscattering coefficient of VV and VH polarization. The in-situ SM data measured at 6 locations using TDR were used to validate the estimated SM results. The 5 days antecedent precipitation data were also collected to overcome the estimation difficulty for the vegetated area not reaching the ground. The MLRM modeling was performed using yearly data and seasonal data set, and correlation analysis was performed according to the number of the independent variable. The estimated SM was verified with observed SM using the coefficient of determination (R2) and the root mean square error (RMSE). As a result of SM modeling using only BSC in the grass area, R2 was 0.13 and RMSE was 4.83%. When 5 days of antecedent precipitation data was used, R2 was 0.37 and RMSE was 4.11%. With the use of dry days and seasonal regression equation to reflect the decrease pattern and seasonal variability of SM, the correlation increased significantly with R2 of 0.69 and RMSE of 2.88%.
Keywords
Backscattering Coefficient; Multiple Linear Regression Model; Sentinel-1; Synthetic Aperture Radar; Soil Moisture;
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