A Study on C-band Synthetic Aperture Radar Soil Moisture Estimation Based on Machine Learning Using Soil Physics, Topography, and Hydrological Information
Jeehun Chung,
Yonggwan Lee,
Jinuk Kim,
Wonjin Jang,
Seongjoon Kim
Affiliations
Jeehun Chung
Ph.D. Student, Department of Civil, Environmental, and Plant Engineering, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, 05029 Seoul, South Korea
Yonggwan Lee
Research Professor, Division of Civil and Environmental Engineering, College of Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, 05029 Seoul, South Korea
Jinuk Kim
Ph.D. Student, Department of Civil, Environmental, and Plant Engineering, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, 05029 Seoul, South Korea
Wonjin Jang
Ph.D. Student, Department of Civil, Environmental, and Plant Engineering, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, 05029 Seoul, South Korea
Seongjoon Kim
Professor, Division of Civil and Environmental Engineering, College of Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, 05029 Seoul, South Korea
In this study, we applied machine learning to estimate soil moisture levels in South Korea by harnessing data from the Sentinel-1 C-band synthetic aperture radar (SAR). Our approach incorporated not only the relationship between backscattering coefficients and soil moisture but also diverse physical characteristics. This encompassed topographic information, soil physics data, and antecedent precipitation which is a hydrological factor influencing the initial condition of soil moisture. We applied a variety of machine-learning techniques and conducted a comprehensive analysis to compare the performance of each model.
