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2025 Vol.58, Issue 12 Preview Page

Research Article

High-resolution soil moisture estimation using a multiple linear regression model based on remote sensing data

원격탐사 자료 기반 다중선형회귀 모형을 활용한 고해상도 토양수분 추정 기법

Heejun Parka
,
Seokhwan Hwangb*
,
Narae Kangc
,
Jungsoo Yoond
박 희준a
,
황 석환b*
,
강 나래c
,
윤 정수d
aPostmaster Researcher, Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
bResearch Fellow, Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
cSenior Researcher, Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
dSenior Researcher, Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
a한국건설기술연구원 수자원하천연구본부 석사 후 연구원
b한국건설기술연구원 수자원하천연구본부 연구위원
c한국건설기술연구원 수자원하천연구본부 수석연구원
d한국건설기술연구원 수자원하천연구본부 수석연구원
*Corresponding Author
31 December 2025. pp. 1351-1364
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Abstract

Accurate soil moisture observation is essential for understanding hydrological processes and supporting disaster response under climate change. However, in South Korea, the complex topography and vegetation conditions limit the accuracy of satellite-based soil moisture estimation. This study, conducted from January 2020 to June 2025, aimed to produce high resolution soil moisture data using a multiple linear regression (MLR) model based on remote sensing data. Key input variables included the VV-polarized backscattering coefficient from Sentinel-1A, cumulative 1, 2, and 3day precipitation derived from Korea Meteorological Administration (KMA) radar data, and consecutive dry days. The model was validated against in situ soil moisture observations at 13 stations in the Chungcheong region provided by the Rural Development Administration (RDA), showing high performance (MAE: 2.49~10.3, R: 0.517~0.768). The model also highly reproduced soil moisture variations across different land cover types, reflecting the influence of both surface conditions and precipitation patterns. These results demonstrate that remotely sensed based data can effectively complement ground observations. The developed model provides a reliable foundation for various applications, including drought monitoring, agricultural productivity assessment, landslide prediction, and flood hazard evaluation.

Keywords
Soil moisture
Sentinel-1A
Synthetic Aperture Radar (SAR)
Radar precipitation
Multiple linear regression
Remote sensing

정확한 토양수분 관측은 수문 과정 이해와 기후 변화에 따른 재해 대응에 필수적이다. 그러나 한반도 지역은 복잡한 지형과 식생 조건으로 인해 기존 위성 기반 토양수분 산출의 정확도에 한계가 존재한다. 본 연구는 2020년 1월 부터 2025년 6월 까지 기간동안 원격탐사 자료를 활용한 다중선형회귀(Multiple Linear Regression, MLR) 모형을 구축하여 고해상도 토양수분 자료를 생산하는 것을 목표로 수행되었다. 주요 입력 변수로 Sentinel-1A 위성의 VV 편파 후방산란계수, 기상청 합성 레이더 기반 1일,2일,3일 누적 강우량, 그리고 연속 무강우일수를 활용하였다. 모형은 농촌진흥청(RDA)의 충청권 13개 지점 토양수분 관측자료를 이용해 검증하였으며, 높은 성능(MAE: 2.49~10.3, R: 0.517~0.768)을 나타내었다. 또한 토지피복 유형별 분석에서도 모형은 지표 특성과 강우 패턴에 따른 토양수분 변화를 안정적으로 재현하였다. 이는 원격 탐측 자료가 지상관측 토양수분 자료를 효과적으로 보완, 대체 할 수 있음을 의미하며, 개발된 모형은 가뭄 모니터링, 농업 생산성 평가, 산사태 및 수문재해 예측 등 다양한 분야의 기초자료로 활용될 수 있을 것으로 기대된다.

키워드
토양수분
Sentinel-1A
합성개구레이더(SAR)
레이더 강우
다중선형회귀모형
원격탐사
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Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 58
  • No :12
  • Pages :1351-1364
  • Received Date : 2025-11-03
  • Accepted Date : 2025-11-14
  • DOI :https://doi.org/10.3741/JKWRA.2025.58.12.1351
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