한국농림기상학회지, 제 7권 제2호(2005) (pISSN 1229-5671, eISSN 2288-1859)
Korean Journal of Agricultural and Forest Meteorology, Vol. 7, No. 2, (2005), pp. 171~183
ⓒ Author(s) 2014. CC Attribution 3.0 License.


MODIS 총일차생산성 산출물의 오차요인 분석: 입력기상자료의 영향

강신규(1), 김영일(2), 김영진(1)
(1)강원대학교 환경과학과, (2)서울대학교 환경대학원

(2005년 05월 05일 접수; 2005년 06월 08일 수락)

Errors of MODIS product of Gross Primary Production
by using Data Assimilation Office Meteorological Data

Sinkyu Kang(1), Youngil Kim(2), Youngjin Kim(1)
(1)Department of Environmental Science, Kangwon National University
(2)Graduate School of Environment Studies

(Received May 05, 2005; Accepted June 08, 2005)

ABSTRACT
In order to monitor the global terrestrial carbon cycle, NASA (National Aeronautics and Space Administration) provides 8-day GPP images by use of satellite remote-sensing reflectance data from MODIS (Moderate Resolution Imaging Spectroradiometer) at l-km nadir spatial resolution since December, 1999. MODIS GPP algorithm adopts DAO (Data Assimilation Office) meteorological data to calculate daily GPP. By evaluating reliability of DAO data with respect to surface weather station data, we examined the effect of errors from DAO data on MODIS GPP estimation in the Korean Peninsula from 2001 to 2003. Our analyses showed that DAO data underestimated daily average temperature, daily minimum temperature, and daily vapor pressure deficity (VPD), but overestimated daily shortwave radiation during the study period. Each meteorological variable resulted in different spatial patterns of error distribution across the Korean Peninsula. In MODIS GPP estimation, DAO data resulted in overestimation of GPP by 25% for all biome types but up to 40% for forest biomes, the major biome type in the Korean Peninsula. MODIS GPP was more sensitive to errors in solar radiation and VPD than in temperatures. Our results indicate that more reliable gridded meteorological data than DAO data are necessary for satisfactory estimation of MODIS GPP in the Korean Peninsula.

Keyword: Gross primary production, Satellite remote sensing, Daily meteorological data

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