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2026 Vol.59, Issue 3 Preview Page

Research Article

Impact of optical heterogeneity on satellite-derived bathymetry in reservoirs: Case of Lake Uiam

위성영상을 이용한 호소 수심 추정에 광학적 이질성이 미치는 영향: 의암호 사례

Jun Song Kima*
,
Kyong Oh Baekb
,
Sang Ug Kimc
김 준성a*
,
백 경오b
,
김 상욱c
aAssistant Professor, Department of Civil and Environmental Engineering, Hankyong National University, Anseong, Korea
bProfessor, Department of Civil and Environmental Engineering, Hankyong National University, Anseong, Korea
cProfessor, Department of Civil Engineering, Kangwon National University, Chuncheon, Korea
a한경국립대학교 건설환경공학부 조교수
b한경국립대학교 건설환경공학부 교수
c강원대학교 공과대학 건축토목환경공학부 교수
*Corresponding Author
31 March 2026. pp. 255-268
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Abstract

This study estimates the bathymetry of Uiam Lake using Sentinel-2 satellite imagery and investigates the influence of optical heterogeneity on depth-prediction accuracy. The spectral signal of natural inland waters exhibits substantial spatial heterogeneity due to variations in suspended sediment concentration, algal biomass, and bottom reflectance, which increases the nonlinearity and local variability in the depth-reflectance relationship. The global model, Random Forest (RF), effectively reproduced the overall depth pattern; however, prediction errors increased considerably in areas where optical properties changed rapidly. In contrast, Geographically Weighted Regression (GWR) locally estimated the spatially varying depth-reflectance relationship and successfully captured this heterogeneity. As a result, the GWR model achieved a 22.3% reduction in MAPE and a 9.3% improvement in R2 compared with the RF model. The substantial spatial variation in GWR coefficients further indicates strong optical heterogeneity within Lake Uiam. This study highlights the structural limitations of the global-model approaches in optically complex inland waters and demonstrates that the spatially adaptive methods such as GWR provide an effective alternative for improving satellite-derived bathymetry.

Keywords
Satellite imagery
Lake Uiam
Optical heterogeneity
Geographically weighted regression
Random forest

본 연구는 Sentinel-2 위성영상을 이용하여 의암호의 수심을 추정하고, 수체의 광학적 이질성이 예측 정확도에 미치는 영향을 분석하였다. 자연계 수체의 분광 신호는 부유물질 농도, 조류 성장, 바닥반사 특성 등의 변화로 인해 공간적 이질성이 크게 나타나며, 이는 수심-반사도 관계의 비선형성과 국지적 변동성을 증가시키는 요인으로 작용한다. 전역 모델인 Random Forest (RF)는 전체적인 수심 패턴을 재현하는 데에는 유효하였으나, 광학 특성이 급변하는 수역에서는 예측오차가 상대적으로 크게 증가하였다. 반면 Geographically Weighted Regression (GWR)은 공간적으로 변화하는 수심-반사도 관계를 국지적으로 추정함으로써 이러한 이질성을 효과적으로 반영하였다. 그 결과 GWR 기반 수심 예측 결과는 RF 모델 대비 MAPE는 22.3% 감소하고 R2 역시 9.3% 상승하는 등 수심 분포 예측 정확도가 향상되었다. 또한 GWR 회귀계수는 공간적으로 크게 변화하는 양상을 보였으며, 이는 의암호 수체가 강한 광학적 이질성을 지님을 시사한다. 본 연구는 광학적 이질성이 강한 내륙 호수 환경에서 단일 전역 모델 기반 접근법이 갖는 구조적 한계를 규명하였고, 공간 가변성을 반영한 GWR 기법이 위성영상을 활용한 수심 추정 정확도를 향상시키는 효과적인 대안임을 보여준다.

키워드
위성영상
의암호
광학적 이질성
Geographically weighted regression
Random forest
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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 :3
  • Pages :255-268
  • Received Date : 2025-12-15
  • Revised Date : 2026-01-12
  • Accepted Date : 2026-01-19
  • DOI :https://doi.org/10.3741/JKWRA.2026.59.3.255
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