## 1 Introduction
Furthermore, monitoring land-use/cover change has been another important task of remote sensing [38]. LUCC reflects the human activities that change the natural land environment, which can profoundly affect climate, biogeochemistry, and biological diversity [19; 39]. Different anthropogenic activities, such as urban expansion and desertification control, have different impacts on vegetation types. Combined with LUCC and NPP, [PERSON] et al. [26] found that the increase of urban and construction land has led to the decrease of vegetation NPP in Anhui province in China. [PERSON] et al. [40] thought urban expansion significantly exacerbates the decline of the total NPP, especially in areas of rapid urban expansion. However, some scholars also point out that human environmental policies are beneficial to the ecological environment. According to the land use transfer matrix result, a large area of cultivated land and deserts turned back to grassland, with better growth, after several policies were vigorously implemented in Inner Mongolia [22; 41]. [PERSON] et al. [28] pointed that the conversion to grassland or forest increased the total NPP in Northern China, which could increase the carbon sequestration capacity. [PERSON] et al. [29] found the total NPP in the Yangtze River basin in China increased 53887.51 Ggc from 2001 to 2018 because of the positive combination effect of LUCC and climate change. With the advent of cloud computing technology, Google Earth Engine (GEE) has become one of the most popular platforms for geospatial and big data analysis. GEE provides massive computational capabilities for free, which makes it easy to analyze the temporal and spatial variations at a large scale [42; 43; 44]. Many studies about land-use and land-cover have been carried out through GEE [45; 46; 47]. [PERSON] et al. [46] compared the object-based and pixel-based methods for Landsat 8 image classification in Maiella National Park and found the object -based method was better. On a larger scale, [PERSON] et al. [47] applied a local adaptive random forest model to perform a fine classification of surface types at the global scale based on the Landsat 8 surface reflectance images on the GEE platform, achieving an overall accuracy of 82.5%. Moreover, some studies applied the GEE platform to monitor the spatial and temporal variation of NPP or gross primary production (GPP). [PERSON] et al. [48] developed a method to analyze the spatiotemporal change of global forest NPP and its response to the El Nino-Southern Oscillation with the GEE platform. [PERSON] et al. [49] used the Coupled Carbon and Water model to estimate GPP in the United States from 2001 to 2016 and found LUCC had a significantly greater negative impact on GPP than climate change. In addition, remote sensing research based on the GEE platform has been widely conducted, including evapotranspiration (ET) modeling [50; 51; 52], shorelines extraction [53], flood monitoring [54], vegetation monitoring [55], urban remote sensing [56; 57], snow fluctuations monitoring [58], etc. Although these studies have obtained valuable results through the use of remote sensing, there is still a lack of systemic analyses of the quantity and quality change of the grassland ecosystem in Inner Mongolia. Meanwhile, there is still a lack of NPP estimation cases based on the CASA model on the GEE platform. Since the grassland ecosystem is sensitive and changeable in Inner Mongolia, it is of great significance to quantitatively analyze the grassland NPP and LUCC using long time-series data, and to assess the influence of LUCC and meteorological factors on NPP. The findings of such an analysis could provide data support for government to formulate or change the sustainable development policies. Therefore, the specific objectives of this study were:  
1. to analyze the LUCC spatiotemporal process in Inner Mongolia every 11 years from 2000 to 2020, and especially the change of grassland. 2. to estimate the Inner Mongolia grassland NPP and its spatiotemporal change every year for 2000-2020.