## 1 Introduction
[PERSON] analyzed the temporal and spatial changes in vegetation phenology and their impact on GPP in temperate regions of China by using MODIS data [27]. [PERSON] established the estimation equation of the daytime dark respiration of different tree species according to the characteristics of dark respiration and light inhibition intensity of a single leaf from each tree species, and revised the GPP estimation results of a broad-leaved Korean pine forest on Changbai Mountain in combination with the measurement results of the canopy leaf biomass [28]. The increase intemperature increases the NPP of the cold zone or subalpine forest ecosystem, and increases the decomposition rate and reduces the NEP of the forest ecosystem [29; 30; 31]. [PERSON] et al. showed that the productivity of China's terrestrial ecosystem was highly sensitive to climate change from 1981 to 2000, and the interannual changes in NPP were significantly positively correlated with the temperature [32]. Due to the unique characteristics of the alpine climate, arid-semi-arid environment, and distinct vertical zonality, the vegetation productivity in forest ecosystems in the Altay Mountains are very sensitive to climate change and human disturbances. Therefore, an accurate assessment of the long-term dynamics of GPP in forest ecosystems in the context of global change and its mechanistic analysis of interannual variability would be helpful to estimate and predict the variation of Chinese forest ecosystems, and provide important guidance for policy formulation on the protection of forest resources. The variation of vegetation GPP in forest ecosystems is affected by complex interactions between climate, topography, forest structure, and soil fertilities. Among them, precipitation and temperature are determining factors affecting the vegetation GPP. However, detailed studies that take into account the decisive role of these factors are scarce. This study, therefore, was conducted to partly fill in this information gap. The MODIS-GPP product is a global GPP product using remote sensing data, developed according to the light-use efficiency principle. The MODIS data can effectively reflect the landscape information on a regional scale. To our knowledge, this study is the first research that analyzes the relationship between vegetation GPP and meteorological factors in the Altay Mountains area. In view of the significant decline in the ecological quality of the Altay Mountains in recent years and the unclear impact of climate change on the ecosystem, this study uses the MODIS GPP model, moving average trend analysis, linear regression analysis, and climate tendency rate method to reveal the spatial-temporal variation of vegetation productivity in the Altay Mountains and its response to meteorological factors, such as temperature and precipitation, by the means of field monitoring, computer simulation, and remote sensing technology. By combining the study with meteorological data and field observation data, we are able to accurately evaluate the variation trends of vegetation GPP in relation to meteorological factors by using the MODIS-GPP model. The methods used in this study to evaluate the variation trends of vegetation GPP can be used to assess ecosystem degradation, design the key restoration areas, as well as mitigate the impact of various meteorological disasters on mountain forest ecosystems. Due to the unavailability of systematic data on the impact of natural disasters and human disturbance, we are not able to carry out systematic research on the reasons of degradation of the forest ecosystem in the Altay Mountains. Further research can be conducted by combining the study with field investigations and plant physiology experiments on the basis of a longer period of remote sensing data.