PARENT SESSION
Poster Session 14: GIS and Remote Sensing
Tuesday, August 9, 5:00 PM - 6:30 PM, Exhibit Hall 220 A-E, Level 2, Palais des congrès de Montréal
Analysis on error sources of MODIS Gross Primary Productivity (GPP) product: errors induced by using DAO meteorological data.
Kang, Sinkyu 1, Kim, Youngil *,2, Kim, Youngjin3, 1 Forest Ecology, Chuchon, Kangwon, South Korea2 Forest Ecology, Seoul, South Korea3 Climate Dynamics, Chuchon, Kangwon, South Korea
ABSTRACT- In order to monitoring global terrestrial carbon cycle, NASA (National Aeronautics and Space Administration) has been providing 8-day GPP image by using satellite remote-sensing reflectance data from MODIS (Moderate Resolution Imaging Spectroradiometer) at 1-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 effect of errors from DAO data on MODIS GPP estimation in Korean peninsula from 2001 to 2003. Our analyses showed that DAO data underestimated daily average temperature, daily minimum temperature, and vapor pressure deficit (VPD), but overestimated short wave radiation during the study period. Each meteorological variable resulted in different spatial pattern of error distribution across Korean peninsula, respectively. In MODIS GPP estimation, DAO data resulted in overestimation of GPP by 25% for all biome types but up to 40% for forest biomes that is major biome types in Korean peninsula. MODIS GPP was more sensitive to errors in solar radiation and VPD than temperatures. Our results indicate that more reliable gridded meteorological data than DAO data is necessary for better estimation of MODIS GPP in Korean peninsula.
Key words: gross primary product, satellite remote sensing, daily meteorological data