한국농림기상학회지, 제 9권 제2호(2007) (pISSN 1229-5671, eISSN 2288-1859)
Korean Journal of Agricultural and Forest Meteorology, Vol. 9, No. 2, (2007), pp. 132~139
DOI: 10.5532/KJAFM.2007.9.2.132
ⓒ Author(s) 2014. CC Attribution 3.0 License.


입력자료 개선에 의한 MODIS 총일차생산성의 신뢰도 향상

김영일(1), 강신규(2), 김 준(3)
(1)McGill대학교 지리학과, (2)강원대학교 환경과학과, (3)연세대학교 대기과학과

(2007년 03월 12일 접수; 2007년 06월 11일 수락)

Enhancing the Reliability of MODIS Gross Primary
Productivity (GPP) by Improving Input Data

Young-Il Kim(1), Sinkyu Kang(2), Joon Kim(3)
(1)Department of Geography, McGill University, Montreal, Canada
(2)Department of Environmental Science, Kangwon National University, Chuncheon 200-702, Korea
(3)Department of Atmospheric Sciences, Yonsei University, Seoul 120-749, Korea

(Received March 12, 2007; Accepted June 11, 2007)

ABSTRACT
The Moderate Resolution Imaging Spectroradiometer (MODIS) regularly provides the eight-day gross primary productivity (GPP) at 1 km resolution. In this study, we evaluated the uncertainties of MODIS GPP caused by errors associated with the Data Assimilation Office (DAO) meteorology and a biophysical variable (fraction of absorbed photosynthetically active radiation, FPAR). In order to recalculate the improved GPP estimate, we employed ground weather station data and reconstructed cloud-free FPAR. The official MODIS GPP was evaluated as +17% higher than the improved GPP. The error associated with DAO meteorology was identified as the primary and the error from the cloud-contaminated FPAR as the secondary constituent in the integrative uncertainty. Among various biome types, the highest relative error of the official MODIS GPP to the improved GPP was found in the mixed forest biome with RE of 20% and the smallest errors were shown in crop land cover at 11%. Our results indicated that the uncertainty embedded in the official MODIS GPP product was considerable, indicating that the MODIS GPP needs to be reconstructed with the improved input data of daily surface meteorology and cloud-free FPAR in order to accurately monitor vegetation productivity in Korea.

Keyword: GPP, MODIS, Meteorology, FPAR, Uncertainty

MAIN

적요

현재 1 km 해상도의 Moderate Resolution Imaging Spectroradiometer(MODIS) 총일차생산성(GPP) 영상이 8일 간격으로 제공되고 있다. 본 연구에서 MODISGPP 산출에 사용되는 입력기상(DAO)자료와 광합성유효복사흡수율(FPAR) 자료의 오차를 정량화 하였고, 이들 오차가 MODIS GPP의 불확실성에 미치는 영향을 분석하였다. 입력자료의 평가를 위해 지상기상관측소의 기상자료를 사용하였고, 구름효과 등을 저감한 FPAR의 시계열을 복원하였다. 평가 결과 입력자료의 오차는 MODIS GPP에서 17% 정도 과대평가되었다. 두 오차중에서 기상자료의 오차가 주요 원인이었으며, FPAR의 오차는 부차적인 것으로 판명되었다. 다양한 토지피복 중에서 혼효림의 MODIS GPP 오차가 약 20%로 가장 크고, 농경지는 약 11%의 오차를 보였다. 입력자료에 의한 MODIS GPP의 오차는 GPP의 계절변화 뿐만 아니라 연간 GPP 변화에도 상이한 결과를 초래하였다. 따라서 MODIS GPP에 내재한 오차는 상당하다고 판단되며, 향후 GPP 모니터링에 응용하기 위해선 상기 기술한 오차 요인들을 저감한 입력자료에 의거해 MODIS GPP를 재가공할 필요가 있다.

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