## 1 Introduction  
Gross primary production (GPP), defined as the total amount of carbon dioxide fixed by plants in photosynthesis, is the first step in the input of atmospheric CO\({}_{2}\) to terrestrial ecosystems [1; 2; 3]. GPP is a key component of the carbon biogeochemical cycle that links atmospheric CO\({}_{2}\) and terrestrial ecosystems. Therefore, accurate and synoptic GPP quantification at regional and global scales can improve our understanding about the mechanisms of carbon budgets and global climate change.  
The eddy covariance (EC) technique measures net CO\({}_{2}\) exchange, which can provide valuable information for developing and validating GPP models at ecosystem scale [4; 5]. Satellite remote sensing can provide continuous, repetitive, and consistent observations of dynamic changes in terrestrial ecosystem structure and function over large areas; it has become a more and more important tool for monitoring land surface properties. A number of remote sensing-based GPP models have been proposed, including the MOD17 algorithm [6], the vegetation photosynthesis model (VPM) [7], the temperature and greenness (TG) model [8], the physiological principles for predicting growth (3-PG) [9] model, the eddy covariance light use efficiency model (EC-LUE) [10], the vegetation index (VI) model [11], the alpine vegetation model (AVM) [12], and the greenness and radiation model (GR) [13; 14], _etc._ These GPP models can be generally classified into two categories. The first category includes models that employ the maximum light use efficiency (LUE)--VPM, MOD17, and EC-LUE--which require many ground meteorological observations (and LOOKUP tables based on vegetation types) as input variables. The demand of these variables is a bottleneck for the model application. The second category of GPP models, such as TG, VI, and GR, are those entirely based on the relationships between remote sensing data and key factors affecting photosynthesis. Though the second category of models reduces or avoids dependence on ground meteorological observations, the ecological meaning of the model parameters is not so clear; model results may not be as good as with the first category. Substantial variations and uncertainties exist in the GPP estimations from different models. Therefore, it is essential to analyze the dependence of GPP models on climate variables and compare the model performances in different eco-regions with diverse canopy structures and climate characteristics.  
Though there are lots of studies focusing on GPP estimations in China using different models--for example, [PERSON] _et al._[15] estimated GPP in the Tibetan Plateau using MODIS images and climate data; [PERSON] _et al._[12] estimated GPP of alpine grasslands using AVM model; and [PERSON] _et al._[5] reported a MODIS-based Photosynthetic Capacity Model to estimate GPP in Northern China and the Tibetan Plateau--there are very few studies focusing on comparing and evaluating these models in different land cover types in China. The most suitable GPP models should be chosen and calibrated for application in different ecosystems.  
In this paper, five GPP models, _i.e._, the VPM, TG, AVM, GR, and MODIS GPP algorithm (MOD_ALG), will be tested and calibrated at five land cover types (eight sites) in China using MODIS image, climate variables, and CO\({}_{2}\) flux measurements during 2003-2005. The objectives of this study are: (1) to calibrate different GPP models in different eco-regions in China; (2) to apply the ground flux measured data and MOD17 to give a full evaluation of different GPP models; and (3) to compare the model accuracies and examine the parameters (key control factors) and sensitivities during photosynthesis. These analyses will improve quantification of the temporal changes in GPP in typical ecosystems in China.