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2023 Vol.56, Issue 3 Preview Page

Research Article

Soil moisture estimation using the water cloud model and Sentinel-1 & -2 satellite image-based vegetation indices

Sentinel-1 & -2 위성영상 기반 식생지수와 Water Cloud Model을 활용한 토양수분 산정

Jeehun Chunga
,
Yonggwan Leeb
,
Jinuk Kimc
,
Wonjin Jangd
,
Seongjoon Kime*
정 지훈a
,
이 용관b
,
김 진욱c
,
장 원진d
,
김 성준e*
aPh.D Candidate, Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University, Seoul, Korea
bPost-Doctoral Researcher, Division of Civil and Environmental Engineering, College of Engineering, Konkuk University, Seoul, Korea
cPh.D Candidate, Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University, Seoul, Korea
dPh.D Candidate, Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University, Seoul, Korea
eProfessor, Division of Civil and Environmental Engineering, College of Engineering, Konkuk University, Seoul, Korea
a건국대학교 일반대학원 사회환경플랜트공학과 박사수료
b건국대학교 공과대학 사회환경공학부 박사후연구원
c건국대학교 일반대학원 사회환경플랜트공학과 박사수료
d건국대학교 일반대학원 사회환경플랜트공학과 박사수료
e건국대학교 공과대학 사회환경공학부 교수
*Corresponding Author
31 March 2023. pp. 211-224
PDF XML Citation
Abstract

In this study, a soil moisture estimation was performed using the Water Cloud Model (WCM), a backscatter model that considers vegetation based on SAR (Synthetic Aperture Radar). Sentinel-1 SAR and Sentinel-2 MSI (Multi-Spectral Instrument) images of a 40 × 50 km2 area including the Yongdam Dam watershed of the Geum River were collected for this study. As vegetation descriptor of WCM, Sentinel-1 based vegetation index RVI (Radar Vegetation Index), depolarization ratio (DR), and Sentinel-2 based NDVI (Normalized Difference Vegetation Index) were used, respectively. Forward modeling of WCM was performed by 3 groups, which were divided by the characteristics between backscattering coefficient and soil moisture. The clearer the linear relationship between soil moisture and the backscattering coefficient, the higher the simulation performance. To estimate the soil moisture, the simulated backscattering coefficient was inverted. The simulation performance was proportional to the forward modeling result. The WCM simulation error showed an increasing pattern from about -12dB based on the observed backscattering coefficient.

Keywords
Backscattering coefficient
Soil moisture
Synthetic aperture radar
Vegetation index
Water cloud model

본 연구에서는 합성개구레이더(Synthetic Aperture Radar, SAR) 기반의 식생을 고려하는 후방산란모델 Water Cloud Model (WCM)을 활용한 토양수분 산정 연구를 수행하였다. 금강 상류의 용담댐유역을 포함한 40 × 50 km2 영역의 Sentinel-1 SAR 및 Sentinel-2 MSI (Multi-Spectral Instrument) 영상을 수집하여 연구에 활용하였다. WCM의 식생변수로는 Sentinel-1 기반의 식생지수 RVI (Radar Vegetation Index), 탈분극비(Depolarization Rario, DR)와 Sentinel-2 기반의 NDVI (Normalized Difference Vegetation Index)를 활용하였다. WCM의 정모델링(forward modeling)은 토양수분과 후방산란계수의 특성이 유사한 3개 Group으로 나누어 수행하였다. 토양수분과 후방산란계수의 선형적인 관계가 명확할수록 Group의 모의 성능이 더 높게 나타났으며, 식생지수 별로는 NDVI, RVI, DR 순으로 정확도가 높았다. 토양수분을 모의하기 위해 모의된 후방산란계수를 반전(inversion)하였으며, 모의 성능은 정모델링 결과와 비례하였다. WCM 모의의 오류는 실측 후방산란계수 기준 약 -12dB를 기점으로 증가하는 양상을 보였다.

키워드
후방산란계수
토양수분
합성개구레이더
식생지수
Water cloud model
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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 : 56
  • No :3
  • Pages :211-224
  • Received Date : 2022-09-13
  • Revised Date : 2022-12-24
  • Accepted Date : 2023-02-10
  • DOI :https://doi.org/10.3741/JKWRA.2023.56.3.211
Journal Informaiton Journal of Korea Water Resources Association Journal of Korea Water Resources Association
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