## 1 Introduction  
Forests cover approximately 4.03 billion hectares worldwide, constituting 49% of the terrestrial gross primary production [1]. They can play a significant role in the carbon cycle because they store a large amount of carbon in the vegetation and soil. Moreover, the annual gross carbon uptake of the global forests equates to approximately half of the carbon emitted from fossil fuels [2]. In general, the NPP (Net Primary Productivity) is an indicator of plant growth and represents the capacity of vegetation to sequester and convert the products of photosynthesis [3]. The estimation of NPP hasprovided not only important variables for characterizing vegetation activities but has also reflected the considerable impact on the dynamics of regional and global carbon cycles, as well as climate change [4]. An accurate quantitative knowledge of the forest NPP, on a regional and global basis, is of basic importance in many branches of Earth and life sciences. However, it has been difficult to accurately estimate the large-scale forest NPP due to the complex effects of various environmental and biological factors. With the development of satellite remote sensing technology, the high-resolution remote sensing satellite images has been widely used to estimate the forest NPP. For example, [PERSON] [5] used the moderate resolution imaging spectroradiometer (MODIS) data for NPP to evaluate the impact of climate on the productivity of forest in Anatolia, Turkey during 2000-2010. Additionally, they found that forests in the Eastern Marmara Transition Subregion had the most significant reduction in NPP, whereas forests located in the northeastern Anatolian Subregion experienced an increase in production. Based on the terrestrial NPP model driven by satellite remote sensing observations, [PERSON] et al. [6] revealed the general decadal trend of increasing NPP for the pan-Arctic basin and Alaska from 1982 to 2000 of approximately 2.7%. However, the rise of satellite sensing technology has occurred in very short a time. It has been difficult for us to study the change of NPP within a long period by using the remote sensing data. [PERSON] et al. [7] analysed the influence of climate changes on NPP for the arid and semiarid areas in China with Climatic Research Unit (CRU) climatic data and MODIS satellite remote data during 2000-2010 and indicated that precipitation was more positively correlated with NPP than temperature. [PERSON] et al. [8] used satellite observations with climate data to evaluate above-ground biomass (AGB) across the Amazon Basin in order to reconstruct annual maps of potential AGB during 1993-2012. Additionally, they estimated a 0.7 Pg C reduction in potential AGB and discovered that climate change has led to a decline of ~1/3 in the capacity of these disturbed forests. For assessing NPP and its spatial and temporal trends, quite often a combination of models and observational proxies (e.g., NDVI) has been used to ensure the reliability of the results [9]. In recent years, the estimation models have been considered as an essential tool to improve the NPP estimation capability. Estimation models could be divided into three categories: statistic model, parameter model and process model [10]. The CASA (Carnegie-Ames-Stanford Approach) model is a typical static parameter model. [PERSON] et al. [11] reported that urbanization could result in a reduction of NPP by using the CASA model in the Pearl River Delta, China. [PERSON] et al. [12] used the simulation model of carbon cycle in land ecosystems (Sim-CYCLE), which was a process model, and pointed out that the estimated NPP was higher than the measured value in the Changbai Mountains from 1982 to 2003 under present climate conditions. In addition, the dynamic global vegetation model (DGVM), such as the Lund-Potsdam-Jena (LPJ) Dynamic Global Vegetation Model, could reveal the NPP dynamics under different climate conditions/scenarios as well [13].