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http://dx.doi.org/10.7780/kjrs.2017.33.6.1.4

Experimental Retrieval of Soil Moisture for Cropland in South Korea Using Sentinel-1 SAR Data  

Lee, Soo-Jin (Department of Spatial information Engineering, Pukyong National University)
Hong, Sungwook (Department of Environment, Energy, and Geoinfomatics, Sejong University)
Cho, Jaeil (Department of Applied Plant Science, Chonnam National University)
Lee, Yang-Won (Department of Spatial information Engineering, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.33, no.6_1, 2017 , pp. 947-960 More about this Journal
Abstract
Soil moisture plays an important role to affect the Earth's radiative energy balance and water cycle. In general, satellite observations are useful for estimating the soil moisture content. Passive microwave satellites have an advantage of direct sensitivity on surface soil moisture. However, their coarse spatial resolutions (10-36 km) are not suitable for regional-scale hydrological applications. Meanwhile, in-situ ground observations of point-based soil moisture content have the disadvantage of spatially discontinuous information. This paper presents an experimental soil moisture retrieval using Sentinel-1 SAR (Synthetic Aperture Radar) with 10m spatial resolution for cropland in South Korea. We developed a soil moisture retrieval algorithm based on the technique of linear regression and SVR (support vector regression) using the ground observations at five in-situ sites and Sentinel-1 SAR data from April to October in 2015-2017 period. Our results showed the polarization dependency on the different soil sensitivities at backscattered signals, but no polarization dependence on the accuracies. No particular seasonal characteristics of the soil moisture retrieval imply that soil moisture is generally more affected by hydro-meteorology and land surface characteristics than by phenological factors. At the narrower range of incidence angles, the relationship between the backscattered signal and soil moisture content was more distinct because the decreasing surface interference increased the retrieval accuracies under the condition of evenly distributed soil moisture (during the raining period or on the paddy field). We had an overall error estimate of RMSE (root mean square error) of approximately 6.5%. Our soil moisture retrieval algorithm will be improved if the effects of surface roughness, geomorphology, and soil properties would be considered in the future works.
Keywords
Soil Moisture; SAR; Sentinel-1; Backscatter;
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