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[국내논문] Sentinel-1 & -2 위성영상 기반 식생지수와 Water Cloud Model을 활용한 토양수분 산정
Soil moisture estimation using the water cloud model and Sentinel-1 & -2 satellite image-based vegetation indices 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.56 no.3, 2023년, pp.211 - 224  

정지훈 (건국대학교 일반대학원 사회환경플랜트공학과) ,  이용관 (건국대학교 공과대학 사회환경공학부) ,  김진욱 (건국대학교 일반대학원 사회환경플랜트공학과) ,  장원진 (건국대학교 일반대학원 사회환경플랜트공학과) ,  김성준 (건국대학교 공과대학 사회환경공학부)

초록
AI-Helper 아이콘AI-Helper

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.

Abstract ▼ AI-Helper 아이콘AI-Helper

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 Da...

주제어

#후방산란계수   #토양수분   #합성개구레이더   #식생지수  

표/그림 (11)

참고문헌 (72)

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