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2026 Vol.59, Issue 4 Preview Page
Reservoir water level estimation using Sentinel-1 SAR-based water body detection

Sentinel-1 SAR 기반 수체 탐지를 활용한 저수지 저수위 산정

Yangwan Kima
,
Jongmin Parkb*
김 양완a
,
박 종민b*
aMaster Course, Department of Environmental Engineering, Korea National University of Transportation, Chungju, Korea
bAssistant Professor, Department of Environmental Engineering, Korea National University of Transportation, Chungju, Korea
a국립한국교통대학교 환경공학과 석사과정
b국립한국교통대학교 환경공학전공 조교수
*Corresponding Author
30 April 2026. pp. 319-332
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Abstract

Increasing variability in rainfall patterns driven by climate change has intensified extreme hydrological events, highlighting the need for accurate reservoir monitoring. However, conventional point-based observations are limited in capturing spatial variability. This study proposes a method for water body detection and reservoir water level and storage estimation using Sentinel-1 Synthetic Aperture Radar (SAR) data for four reservoirs — Naju, Gwangju, Jangsung, and Pyeonglim Dams — in the Yeongsan River basin from 2021 to 2024. Water bodies were extracted using K-means clustering and Otsu thresholding, and detection performance was evaluated using confusion matrices based on Esri Sentinel-2 Land Cover data. A Random Forest (RF) model was utilized to estimate reservoir water level and storage using SAR-derived water surface area, backscattering coefficients (i.e., σ0VV and σ0VH), and day of year (DOY) as input variables. The results showed stable water detection performance, with strong correlations between SAR-derived water body and reference data (R = 0.81-0.98). The RF model achieved high predictive accuracy for water level and storage across all reservoirs (R = 0.85-0.97, IOA ≥ 0.91). These findings highlight the potential of SAR-based satellite monitoring as a reliable tool for long-term reservoir monitoring and water resources management.

Keywords
Sentinel-1 SAR
Reservoir water body detection
Reservoir water level estimation
Harmonized Landsat and Sentinel-2(HLS)

기후 변화의 영향으로 인한 강우 패턴의 변동성 증가는 홍수와 가뭄과 같은 극한 수문 사상의 발생 빈도를 높이고 있으며, 이에 따라 저수지 수위 변동을 정밀하게 모니터링할 수 있는 체계가 요구되고 있다. 그러나 기존의 지점 기반 수위 관측은 저수지 전역의 공간적 수문 변동성을 충분히 반영하는 데 한계를 가진다. 이를 보완하기 위해 Sentinel-1 합성개구레이더(Synthetic Aperture Radar, SAR)를 활용하여 2021년부터 2024년까지 영산강 유역 내 4개 호소(나주댐, 광주댐, 장성댐, 평림댐)의 수체 탐지 및 저수위/저수량 산정 기법을 제안하였다. 수체 탐지에는 K-means Clustering과 Otsu Thresholding 기법을 적용하고, Esri Sentinel-2 Land Cover 자료를 참조하여 혼동행렬 기반 성능 평가를 수행하였다. 또한 SAR 기반으로 산정된 수체 면적과 후방산란계수(σ0VV, σ0VH), 연중일(DOY)을 입력 변수로 하는 Random Forest를 구축하여 저수위 및 저수량을 산정하였다. 분석 결과, K-means clustering은 다양한 토지피복 조건에서 안정적인 수체 탐지 성능을 보였으며, SAR 기반 수체 면적은 실측 자료와 높은 상관성을 나타냈다(R = 0.81-0.98). Random Forest 기반 저수위 및 저수량 산정 결과 또한 모든 댐에서 높은 예측 성능을 확보하였다(R = 0.85-0.97, IOA ≥ 0.91). 본 연구는 SAR 기반 수체 탐지 결과를 실측 수문 자료와 정량적으로 연계하여 저수지의 장기적인 수위 및 저수량 변동을 안정적으로 추정할 수 있음을 입증하고, 위성 기반 수문 모니터링의 적용 가능성을 제시한다.

키워드
Sentinel-1
수체 탐지
저수위 산정
Harmonized Landsat and Sentinel-2(HLS)
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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 : 59
  • No :4
  • Pages :319-332
  • Received Date : 2026-01-09
  • Revised Date : 2026-02-09
  • Accepted Date : 2026-02-09
  • DOI :https://doi.org/10.3741/JKWRA.2026.59.4.319
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