[논문]선행 강우를 고려한 Sentinel-1 SAR 위성영상과 다중선형회귀모형을 활용한 토양수분 산정

정지훈; 손무빈; 이용관; 김성준

doi:10.7780/kjrs.2021.37.3.12

선행 강우를 고려한 Sentinel-1 SAR 위성영상과 다중선형회귀모형을 활용한 토양수분 산정
Estimation of Soil Moisture Using Sentinel-1 SAR Images and Multiple Linear Regression Model Considering Antecedent Precipitations 원문보기

대한원격탐사학회지 = Korean journal of remote sensing, v.37 no.3, 2021년, pp.515 - 530

정지훈 (건국대학교 대학원 사회환경플랜트공학과) , 손무빈 (건국대학교 대학원 사회환경플랜트공학과) , 이용관 (건국대학교 대학원 사회환경플랜트공학과) , 김성준 (건국대학교 공과대학 사회환경공학부)

초록
AI-Helper

본 연구에서는 Sentinel-1 C-band SAR(Synthetic Aperture Radar) 위성영상을 기반으로 다중선형회귀모형을 활용하여 금강 유역 상류에 위치한 용담댐 유역의 토양수분을 산정하였다. 10 m 공간 해상도의 Sentinel-1A/B SAR 영상은 6일 간격으로 2015년부터 2019년까지 5년 동안 구축하였고, SNAP(SentiNel Application Platform)을 사용하여 기하 보정, 방사 보정 및 잡음(Noise) 보정을 수행하고 VV 및 VH 편파 후방산란계수로 변환하였다. 토양수분 산정 모형의 검증자료로 TDR로 측정된 6개 지점의 실측 토양수분 자료를 구축하였으며, 수문학적 개념인 선행 강우를 고려하기 위해 동지점에 대한 강수량 자료를 구축하였다. 다중선형회귀모형은 전체 기간 및 계절별로 나누어 모의하였으며, 독립변수의 증감에 따른 상관성 분석을 진행하였다. 산정된 토양수분은 결정계수(R²)와 평균제곱근오차(RMSE)를 활용하여 검증하였다. 초지 지역에서 후방산란계수만을 이용한 토양 수분 산정 결과 R²가 0.13, RMSE가 4.83%으로 나타났으며 선행강우를 5일까지 사용했을 경우 R²가 0.37, RMSE가 4.11%로 상관성이 상승하는 모습을 보였다. 이 때, 토양수분의 계절별 변동성과 감소 패턴의 반영을 위해 무강우누적일수의 적용과 계절별 회귀식을 작성한 결과 R²가 0.69, RMSE가 2.88%로 상관성이 크게 상승하였다. SAR 기반 토양수분 추정 시 선행강우 및 무강우누적일수의 활용이 효과적이었다.

Abstract ▼ AI-Helper

This study is to estimate soil moisture (SM) using Sentinel-1A/B C-band SAR (synthetic aperture radar) images and Multiple Linear Regression Model(MLRM) in the Yongdam-Dam watershed of South Korea. Both the Sentinel-1A and -1B images (6 days interval and 10 m resolution) were collected for 5 years from 2015 to 2019. The geometric, radiometric, and noise corrections were performed using the SNAP (SentiNel Application Platform) software and converted to backscattering coefficient of VV and VH polarization. The in-situ SM data measured at 6 locations using TDR were used to validate the estimated SM results. The 5 days antecedent precipitation data were also collected to overcome the estimation difficulty for the vegetated area not reaching the ground. The MLRM modeling was performed using yearly data and seasonal data set, and correlation analysis was performed according to the number of the independent variable. The estimated SM was verified with observed SM using the coefficient of determination (R2) and the root mean square error (RMSE). As a result of SM modeling using only BSC in the grass area, R2 was 0.13 and RMSE was 4.83%. When 5 days of antecedent precipitation data was used, R2 was 0.37 and RMSE was 4.11%. With the use of dry days and seasonal regression equation to reflect the decrease pattern and seasonal variability of SM, the correlation increased significantly with R2 of 0.69 and RMSE of 2.88%.

주제어

표/그림 (13)

그림 Fig. 1. Flowchart.
그림 Fig. 2. Locations of study sites.
표 Table 1. Relevant information of the soil moisture observation sites
표 Table 2. Specifications by image acquisition mode of Sentinel-1
표 Table 3. Scenario classification by input data for multiple linear regression model
표 Table 4. Comparison of correlation coefficient (R) between backscattering coefficient and observed soil moisture by speckle filtering method at each station
표 Table 5. Incidence angle information for study sites
그림 Fig. 3. Relation between observed soil moisture and Sentinel-1A VV polarization backscattering coefficient for each station: (a) Ancheon, (b) Bugwi, (c) Cheoncheon, (d) Gyebuk, (e) Jucheon, and (f) Sangjeon.
그림 Fig. 4. Time series graph of (a) observed soil moisture and simulated soil moisture in Scenario 8(S-8) and Scenario 9(S-9) at Cheoncheon station, (b) observed soil moisture and dry days at Cheoncheon station.
표 Table 6. Regression values of the multiple linear regression model according to each scenario at Cheoncheon station
그림 Fig. 6. Scatter plots of observed soil moisture and simulated soil moisture by (a) scenario 9, and (b) scenario 10.
그림 Fig. 5. Boxplots of observed soil moisture (Obs.), simulated result by scenario 9 (S-9), and scenario 10 (S-10).
그림 Fig. 7. Time series graph of observed soil moisture and observer soil moisture for each station: (a) Ancheon, (b) Bugwi, (c) Cheoncheon, (d) Gyebuk, (e) Jucheon, and (f) Sangjeon.

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저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

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