## 1 Introduction  
Understanding the mechanisms of global climate change and improving our predictions of possible future climates requires accurate quantifications of carbon uptake by terrestrial vegetation. Gross Primary Production (GPP), the amount of carbon assimilated by vegetation through photosynthesis over time, fundamentally influences the carbon cycle and human habitability of our planet [1]. While the mechanisms regulating GPP are well understood and modeled at the leaf and canopy level, regional and global GPP models still need to address large uncertainties in the estimates [2]. Historically, scientists have tried to explain the spatial and temporal patterns of GPP by exploiting its relation with vegetation biophysical metrics such as Leaf Area Index (LAI) [3], foliar standing crop [4], the Fraction of Photosynthetically Active Radiation absorbed by plant canopies (FPAR) [5], or with vegetation indices like the integrated Normalized Differential Vegetation Index (NDVI) [6]. Following the recognition that drought conditions suppress summer photosynthesis [7], process-oriented models were developed to estimate GPP with a combination of meteorological data and satellite-observed biophysical parameters, such as FPAR [8] and LAI [9]. These process-oriented models, while allowing simulations of ecosystems under future climate scenarios, present large uncertainty when used for current estimations of GPP because the required input parameters are not available with adequate accuracy at the global scale. The possibility of estimating GPP based on meteorological data only (e.g., [PERSON] _et al._[10]), combinations of satellite-observed biophysical parameters (e.g., [PERSON] _et al._[11]; [PERSON] _et al._[12]), or combinations of meteorological data and simple biophysical processes (e.g., temperature \(\times\) (precipitation - evapotranspiration) [13]), has also been explored. For global-scale studies of GPP, satellite data have the advantage of providing continuous, repetitive, and consistent observations of vegetation dynamics. The Advanced Very High Resolution Radiometer (AVHRR, available since 1981 and originally designed for weather monitoring) has been widely used for long-term analysis of global vegetation dynamics [14, 15] but suffers from orbital drift, water vapor contamination and lack of onboard calibration [16]. In 1999 NASA launched the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the TERRA platform with the specific objective of monitoring Earth's surface and atmosphere environment. MODIS data have now been used to generate a decade's worth of standard land data products including vegetation indices, biophysical properties of vegetation, and carbon fluxes [17]. Concurrent with MODIS, participants in the global FLUXNET network of micrometeorological towers with eddy covariance instrumentation have recorded land-atmosphere exchanges of carbon dioxide, water vapor, and energy at hundreds of diverse sites [18, 19]. Although uncertainties exist in the FLUXNET observations, the network has provided unprecedented estimates of GPP at high temporal resolution for almost a decade. The development of these two data streams--MODIS and FLUXNET--has led to extensive tests of the ability of remote sensing to approximate GPP, especially at local scales. Among the MODIS products, the Enhanced Vegetation Index (EVI)--an optimized vegetation index developed from NDVI [20]--has shown a strong seasonal correlation with GPP [21, 22, 23, 24]. [PERSON] _et al._[23] found a high correlation between GPP and EVI in deciduous forests but not in evergreen forests. [PERSON] _et al._[24] extended the relationship between GPP and EVI to the monsoon forests of Southeast Asia. [PERSON] _et al._[21] indicated that EVI could track FPAR by measuring photosynthetically active vegetation while excluding the senescent parts of vegetation. Some biophysical models have already tried to use EVI in place of NDVI to estimate vegetation carbon production [21, 25, 26, 27].