Modeling gross primary production of two steppes in Northern China using MODIS time series and climate data

Terrestrial carbon cycle plays an important role in global climate change. As a key component of terrestrial carbon cycle, gross primary production (GPP) is a major determinant of the exchange of carbon between the atmosphere and terrestrial ecosystems. With rapid advancement of remote-sensing technology, it has become a common practice to utilize parameters derived from remote-sensing data to estimate GPP at a regional or global scale. In this study, a satellite-driven model, Vegetation Photosynthesis Model (VPM) was introduced to estimate GPP of two steppes, Xilinhot (XH, 43.5544 degrees N, 116.6714 degrees E) and Duolun (DL, 42.0467 degrees N, 116.2836 degrees E), at Inner Mongolia in Northern China, by integrating moderate resolution imaging spectral radiometer (MODIS) and meteorological measurements at the two flux towers. As defined by the input variables of VPM, two improved vegetation indices (enhanced vegetation index (EVI) and land surface water index (LSWI)) derived from the standard data product MOD09A1 of MODIS, air temperature and photosynthetic active radiation at the flux towers, were included for the model calculating. Canopy-level maximum light use efficiency, a key parameter for VPM, was estimated by using the observed CO2 flux data and photosynthetic active radiation (PAR). Observed GPP derived from flux data were then used to critically evaluate the performance of the model. The results indicate that the seasonal dynamics of GPP predicted by the VPM model agreed well with measured GPP by the flux towers. The determination coefficient (R-2) of predicted GPP with measured GPP was 0.86 and 0.79 in 2006, 0.66 and 0.76 in 2007 for DL and XH, respectively. Further, time-series data for the EVI have a stronger linear relationship with the GPP than those for the Normalized Difference Vegetation Index. Results of this study demonstrate that the satellite-driven VPM has been potential for estimating site-level or regional grassland GPP, and might be an effective tool for scaling-up carbon fluxes. (C) 2011 Published by Elsevier B. V. Selection and/or peer-review under responsibility of School of Environment, Beijing Normal University.