## 1 Introduction  
Satellite remote sensing is considered to be one critical component in developing and improving our understanding of the Earth's system. Numerous studies have shown the utility of satellite remote sensing in ecosystem dynamics studies in relation to climate change, including monitoring of ecosystem disturbances, biodiversity assessments, and carbon cycle modeling (e.g., [1, 2, 3]). In these studies, satellite remote sensing was often used in combination with a network of point-based ground measurements for their regional and temporal extrapolations. Since 2005, the Global Earth Observation Grid (GEO Grid) project has been developing and providing a grid technology-based e-Science infrastructure to facilitate conducting Earth system studies [4]. GEO Grid is being equipped with a large amount of both ground observational and satellite remote sensing data, and new advanced algorithms are being added for effective processing, integration, and assimilation of these data to generate higher-level products for the Earth system science. Recently, a GEO Grid subsystem for the radiometric calibration and atmospheric correction of Advanced Spaceborne Thermal Emission and Reflection Radometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) was developed [5]. Launched in December 1999, both ASTER and MODIS are research facility instruments onboard the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra satellite platform [6, 7]. Whereas MODIS is designed to provide long-term global observations every 1-2 days at moderate resolution (250 m-1 km), ASTER works as the \"zoom lens\" and provides the highest spatial resolution (15 m-60 m) surface spectral reflectance, temperature, and emissivity data of all the Terra instruments. Synergistic application of ASTER and MODIS is useful for effectively integrating ground-based measurements to regional to global scale MODIS measurements by using ASTER data as a medium for scaling up the ground measurements. Since the development of the subsystem, there have been more than 27,000 downloads of ASTER and/or MODIS products generated by the subsystem. The Normalized Difference Vegetation Index (NDVI) has been the most popular index for detecting vegetation abundance from satellite images. This index uses red and NIR reflectances, and NOAA AVHRR NDVI products have widely been used to understand long-term global vegetation changes. However, it is known that the NDVI has the demerits of being impacted by atmospheric and soil background brightness variations [8]. The NDVI also has the disadvantage of saturation in high biomass area [8]. The Enhanced Vegetation Index (EVI) was developed to optimize the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in aerosol influences [8]:  
\[EVI=G\cdot\frac{\rho_{NIR}-\rho_{red}}{\rho_{NIR}+C_{1}\cdot\rho_{red}-C_{2} \cdot\rho_{blue}+L} \tag{1}\]  
where \(\rho_{NIR}\), \(\rho_{red}\), and \(\rho_{blue}\) are the atmospherically corrected reflectances (totally or partially for molecular scattering, and water vapor and ozone absorptions) for the NIR, red, and blue spectral bands, \(L\) is the canopy background adjustment factor that addresses nonlinear, differential NIR and red radiant transfer through a canopy, and \(C_{1}\) and \(C_{2}\) are the coefficients of the aerosol resistance term, which uses the blue band to correct for aerosol influences in the red band. In the MODIS EVI algorithm, the coefficients of \(L=1\), \(C_{1}=6\), \(C_{2}=7.5\), and \(G=2.5\) are adopted [8, 9]. Recent studies confirm superiority of relationships between EVI and biophysical parameters, which include leaf area index(LAI), the fraction of absorbed photosynthetically-active radiation (fAPAR)) [8, 10, 11], and gross primary production (GPP) [12, 13, 14].