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http://dx.doi.org/10.5532/KJAFM.2007.9.2.132

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

Kim, Young-Il (Department of Geography, McGill University)
Kang, Sin-Kyu (Department of Environmental Science, Kangwon National University)
Kim, Joon (Department of Atmospheric Sciences, Yonsei University)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.9, no.2, 2007 , pp. 132-139 More about this Journal
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.
Keywords
GPP; MODIS; Meteorology; FPAR; Uncertainty;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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