Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Adjustment of A Simplified Satellite-Based Algorithm for Gross Primary Production Estimation Over Korea

  • Pi, Kyoung-Jin (Department of Spatial Information Engineering, Pukyong National University) ;
  • Han, Kyung-Soo (Department of Spatial Information Engineering, Pukyong National University) ;
  • Kim, In-Hwan (Department of Spatial Information Engineering, Pukyong National University) ;
  • Lee, Tae-Yoon (Department of Environmental Engineering, Pukyong National University) ;
  • Jo, Jae-Il (Department of Spatial Information Engineering, Pukyong National University)
  • Received : 2013.06.12
  • Accepted : 2013.06.21
  • Published : 2013.06.30
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Abstract

Monitoring the global Gross Primary Pproduction (GPP) is relevant to understanding the global carbon cycle and evaluating the effects of interannual climate variation on food and fiber production. GPP, the flux of carbon into ecosystems via photosynthetic assimilation, is an important variable in the global carbon cycle and a key process in land surface-atmosphere interactions. The Moderate-resolution Imaging Spectroradiometer (MODIS) is one of the primary global monitoring sensors. MODIS GPP has some of the problems that have been proven in several studies. Therefore this study was to solve the regional mismatch that occurs when using the MODIS GPP global product over Korea. To solve this problem, we estimated each of the GPP component variables separately to improve the GPP estimates. We compared our GPP estimates with validation GPP data to assess their accuracy. For all sites, the correlation was close with high significance ($R^2=0.8164$, $RMSE=0.6126g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$, $bias=-0.0271g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$). We also compared our results to those of other models. The component variables tended to be either over- or under-estimated when compared to those in other studies over the Korean peninsula, although the estimated GPP was better. The results of this study will likely improve carbon cycle modeling by capturing finer patterns with an integrated method of remote sensing.

Keywords

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