## 1 Introduction  
Net primary productivity (NPP) is the net flux of carbon from the atmosphere into green plants per unit time. It is an essential flux of the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is a key component of the carbon cycle [1] and an important indicator of the ecosystem [2]. Regional estimates of NPP are useful in modeling the regional and global carbon cycle. Quantitative estimates of NPP at regional to global scales are significant for understanding changes in ecosystem structure and function, predicting terrestrial carbon cycle trends [3; 4; 5], determining sustainable use of natural resources, and making policy decisions [6]. Three main methods have been used to estimate NPP for regional areas: (1) the establishment of a model that combined climatic and environmental factors, was established to evaluate the regional distribution of carbon budgets [7; 8]; (2) the application of process-based, biogeochemical models that scale-up and extrapolate site-specific measurements [9; 10]; and (3) models that use remote sensing data, numerous efforts have been made to develop various NPP models and to estimate global NPP. The accuracy of satellite-derived NPP estimates have been recently highlighted as a matter of concern because of poor satellite data quality and lack of accurate ecosystem representation in satellite-based models [11; 12; 13]. Numerous remote sensing-based models for estimating NPP, such as Carnegie Ames Stanford Approach (CASA) model [14], Simulation model of Carbon Cycle in Land Ecosystems (MOD-Sim-Cycle) model [15], and GLOBAL Production Efficiency Model (GLOPEM) model [16], have been developed in recent decades to study the dynamics of vegetation productivity. Examples of ecological process models that simulate basic ecological physiology and the process of plant growth include Boreal Ecosystem Productivity Simulator (BEPS) model, Terrestrial Ecosystem Model (TEM) model, CENTURY model, and BioGeochemical Cycles (Biome-BGC) model [17]. The production efficiency model based on satellite remote sensing encounters difficulty in explaining the response of ecosystem internal mechanism to environmental change. Affected by the method and the data quality, it will produce deviation in analysis of large scale ecosystem changes. Thus, an integrated strategy was developed that combined the remote sensing-based GLOBAL Production Efficiency Model (GLOPEM) and an ecosystem process-based model, the Carbon Exchange between Vegetation, Soil and Atmosphere model (CEVSA), enhancing the possibility and feasibility of NPP model estimation. The GLOPEM-CEVSA model [18] makes full use of the advantages of remote sensing and considers the spatial heterogeneity of ecosystems. The model is suitable for estimating the spatial-temporal distribution of NPP for terrestrial ecosystems. As the remote sensing data have the timeliness and space, for the model, the time and space scale can be changed, this paper selected 2001-2011, a total of 11 years of remote sensing products. However, few available models integrate moderate-resolution imaging spectroradiometer (MODIS) atmosphere and land products to estimate fraction of photosynthetically active radiation (fPAR). A main component of the GLOPEM-CEVSA modeling framework is the satellite-derived FPAR absorbed by vegetation, which can be derived from satellite normalized difference vegetation index (NDVI) data that come from MODIS products, with a 1000 m resolution. The LUE model is an index of the photosynthesis. Research of LUE is beneficial for the energy cycle of the biosphere and of great significance for energy use and transformation, and the mechanism of vegetation physiological and ecological processes. Considering the atmosphere, radiation, moisture, carbon dioxide and other stress factors as a model-driven parameter, the model is used to simulate the autotrophic respiration of plants and calculate the dynamic NPP. Furthermore, soil moisture data were obtained by inversion of precipitation data and evapotranspiration models.