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2025 Vol.58, Issue 12 Preview Page

Research Article

Enhancing agricultural reservoir water body monitoring through the integration of optical and SAR remote sensing

광학 및 SAR 영상 기반 농업용 저수지 수체의 시·공간적 변화 모니터링

Hee-Jin Leea
,
Won-Ho Namb*
,
Mi-Hye Yangc
,
Hosun Leed
이 희진a
,
남 원호b*
,
양 미혜c
,
이 호선d
aPh.D. Student, Department of Integrated Systems Engineering, Hankyong National University, Anseong, Korea
bProfessor, School of Social Safety and Systems Engineering, Institute of Agricultural Environmental Science, National Agricultural Water Research Center, Hankyong National University, Anseong, Korea
cPh.D. Student, Department of Integrated Systems Engineering, Hankyong National University, Anseong, Korea
dPrincipal Specialist, Drought Information Analysis Center, Korea Water Resources Corporation (K-water), Daejeon, Korea
a한경국립대학교 융합시스템공학과 박사과정
b한경국립대학교 사회안전시스템공학부 교수
c한경국립대학교 융합시스템공학과 박사과정
dK-water 국가가뭄정보분석센터 책임위원
*Corresponding Author
31 December 2025. pp. 1451-1461
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Abstract

Changes in water bodies are critical indicators for diagnosing and predicting extreme hydrological hazards, and the ongoing decline in storage across dams and reservoirs has recently posed significant challenges for water resource management. As a result, there is a growing demand for reliable systems capable of monitoring the spatiotemporal dynamics of reservoir water bodies. In this study, we quantitatively compared optical and SAR-based methods for monitoring reservoir dynamics using Sentinel imagery processed on the Google Earth Engine (GEE) platform. The optical indices—the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI)—generally produced lower RMSE values and closely matched observed water surface areas; however, their reliability diminished during periods of reservoir drawdown due to indistinct boundaries and data gaps. In contrast, Synthetic Aperture Radar (SAR)-based approaches—including the Kittler-Illingworth (KI), Chan-Vese (CV), and K-means (KM) algorithms—offered greater year-round data availability and more stable time-series detection. Among these, the CV method provided the best balance between accuracy and robustness, yielding an R² of 0.7 for the Geumgwang Reservoir, an RMSE of 7.6 ha for the Madun Reservoir, and an overall classification accuracy of 87% relative to observed water surface areas. Overall, both optical- and SAR-based approaches effectively captured reservoir variations during periods of rising water levels, though performance declined under stable or decreasing conditions. Future research should focus on developing segment-specific corrections and integrating optical and SAR imagery to establish a continuous monitoring framework for medium- to large-scale agricultural reservoirs, with potential application as a core component of national hazard monitoring systems.

Keywords
Reservoir
Water body dynamics
Google Earth Engine (GEE)
NDWI
Chan-Vese (CV)

저수지 수체의 변화는 극한 수문 재해 발생 및 상황을 진단하고 예측하는 데 핵심적인 지표이다. 대규모 댐, 저수지 저수량의 지속적인 감소는 수자원 부존량에 직접적인 영향을 미치기 때문에 수리시설물 수체의 시공간 변동에 대한 모니터링이 필요하다. 본 연구에서는 구글어스엔진(GEE) 기반 Sentinel 영상 자료를 활용하여 광학 영상 기반 정규수분지수(NDWI) 및 수정정규수분지수(MNDWI)와 합성개구레이더(SAR) 기반 Kittler-Illingworth (KI), Chan-Vese (CV), K-Means clustering (KM)의 수체 변화 모니터링 성능을 비교하였다. 광학 기반 추정은 실제 수표면적에 근접하였으나, 수위 감소 구간에서 경계 불명확성과 관측 공백으로 변화 탐지 신뢰도가 저하되었다. SAR 기반 추정은 연중 가용성이 높고 시계열 패턴 포착이 안정적이었으며, 특히 CV는 금광 저수지에 대한 R2는 0.7, 마둔 저수지에 대한 RMSE는 7.6 ha로 정확성 및 가용성에 대한 균형이 우수하였다. 또한, 실제 수표면적 대비 수체 분류 정확도는 87%로 가장 높은 정확도를 나타내었다. 광학 및 SAR 기반 수체 분류는 수위 상승 구간에서 안정적인 수체 변화 정보를 제공하지만, 수위 감소 및 유지 구간에 대해서는 미흡한 것으로 판단된다. 본 연구의 결과는 향후 수위 구간에 따른 보완과 광학 및 SAR 이미지와의 융합을 통해 중규모 이상의 농업용 저수지에 대한 상시 수체 모니터링 체계를 구축할 수 있으며, 국가 단위 수자원 재해 감시 네트워크의 핵심 모듈로 확장될 수 있을 것으로 기대된다.

키워드
저수지
수체
구글어스엔진(GEE)
정규수분지수(NDWI)
Chan-Vese (CV)
합성개구레이더(SAR)
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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 :1451-1461
  • Received Date : 2025-09-12
  • Revised Date : 2025-12-01
  • Accepted Date : 2025-12-02
  • DOI :https://doi.org/10.3741/JKWRA.2025.58.12.1451
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