Fractional vegetation cover (FVC) has been one of the most widely utilized indicators to assess the vegetation growth status for the better ecological environment. The spatiotemporal evolution of FVC and its relationship with the climate change can also be crucial to promote the ecological civilization and the sustainable development in agriculture and animal husbandry. However, the large-scale and long-term FVC research is often required to deal with the massive image data, leading to the significant challenges on research advancement. The Google Earth Engine (GEE) platform can be expected to offer the new and effective solutions. In this study, a perfect tradeoff was gained between the rich data sources and powerful processing using GEE platform. A dimidiate pixel model (DPM) was employed to produce the FVC under the different types of land cover in Inner Mongolia, northern China. The Sen's slope estimator and Pearson correlation coefficient were then used to identify the temporal and spatial change of FVC and its correlation with the climate factors. Initially, the FVC annual time series were collected from 1982 to 2021 using the NOAA AVHRR NDVI data within GEE. Subsequently, Sen's slope estimation and the Mann-Kendall test were conducted to analyze the significant trends of FVC over the four decades. Finally, Pearson correlation coefficients were calculated between FVC and climate factors. The significance tests were carried out to reveal the relationship between FVC and climate change. Additionally, the spatial distribution maps were obtained to depict the spatial pattern of the correlations in the significant correlated regions. The results showed that: 1) There was the significant band pattern in the spatial distribution of the average FVC, with the gradual increase from the west to the northeast. A large difference of FVC was also found in different types of land cover. Generally, the FVC was >0.1-0.2 in 18.7% of the region. The proportions of FVC >0.5-0.6 and >0.7-0.8 were both close to 15%. There were the relatively small proportions of 0-0.1 and >0.8-0.9, particularly without the proportions of >0.9-1.0. The rest intervals were about 10%. 2) The FVC decreased significantly in the desert area of western Inner Mongolia, accounting for 13.1%. There was no significant trend of FVC in the mountains and grasslands of the middle, where the area was accounted for 38.2%. A very significant upward trend was found in the FVC of croplands, forests and part of grasslands in the south, east and northeast, accounting for 38.3% of the total. 3) There were the significantly different correlations between FVC and meteorological factors in the different types of land cover. The FVC of grassland was significantly or extremely significantly positive correlated with the annual precipitation, while the FVC of croplands was significantly or extremely significantly positive correlated with annual average temperature, and the FVC in some areas of forests was significantly negative correlated with annual precipitation, as well as the FVC in some barren was significantly negative correlated with annual average temperature. A 40-year-long time series of annual FVC were successfully constructed using NOAA AVHRR NDVI data on the GEE platform. The robust processing and analysis capabilities of platform were better balanced to identify the trends of FVC in the various land cover types, together with the correlation with climate change. These findings can provide some significant implications to guide the ecological environment protection for the sustainable development in Inner Mongolia of China.

The increasing frequency of extreme climate events has not only directly affected vegetation growth but also posed serious threats to agricultural-pastoral production and ecological sustainability. To facilitate global and regional studies on climate extremes, the Expert Team on Climate Change Detection and Indices (ETCCDI) defined 27 core climate indices. Among these, temperature-related indices are critical for understanding the temporal and spatial characteristics of extreme temperature events and their driving mechanisms, which are essential for regional ecological conservation and agricultural security. However, previous studies mostly focused on station-scale data and lacked systematic investigations at the regional level over longer timescales. Moreover, the causes of extreme climate involve multiple factors and the driving mechanisms remain poorly understood, although recent findings have indicated a strong relationship between their variability and atmospheric circulation patterns. In this study, we investigated the spatiotemporal variation of extreme temperature using 16 temperature-related ETCCDI indices in Inner Mongolia, China. We constructed the annual time series of extreme temperature indices based on ERA5-Land reanalysis dataset on Google Earth Engine (GEE) platform, and utilized Sen’s slope estimator, Mann-Kendall test and Pearson correlation coefficient to comprehensively analyze the spatiotemporal changes in extreme temperatures and the causes of atmospheric circulation in Inner Mongolia from 1951 to 2020. First, meteorological station data were used to validate the daily average temperature, daily maximum temperature, and daily minimum temperature in ERA5-Land reanalysis data to verify the consistency of the ERA5-Land reanalysis data and the meteorological station temperature data. Second, the annual time series of the extreme temperature indices were calculated using ERA5-Land data within the GEE platform, and the Sen slope estimation and Mann-Kendall test methods were used to analyze the spatial distribution and changing trends of extreme temperatures. As the final step, the Pearson correlation coefficient method was employed to analyze the correlation between the extreme temperature indices and the atmospheric circulation indices, thereby identifying the dominant drivers of extreme temperature variability in Inner Mongolia.The results showed that: 1) extreme temperature indices exhibited clear spatial heterogeneity. Frost days and ice days were most frequent in the central Yinshan Mountains and northeastern Greater Khingan Range, whereas summer days and tropical nights were more common in the western desert regions. Maximum values of daily maximum and minimum temperatures were highest in desert areas, while mountainous regions had the lowest. Other indices, such as cold/warm spell durations, diurnal temperature range, and growing season length, also displayed distinct regional patterns, reflecting Inner Mongolia’s complex climatic and geographic features. 2) Most extreme temperature indices demonstrated significant warming trends, with cold indices decreasing and warm indices increasing, indicating a reduction in extreme cold events and an intensification of extreme heat events. 3) Among the four atmospheric circulation indices examined, the Arctic Oscillation (AO) had the most significant impact, contributing to winter warming in northeastern Inner Mongolia. The Pacific Decadal Oscillation (PDO) mainly influenced the western deserts, while the North Atlantic Oscillation (NAO) and Southern Oscillation Index (SOI) showed minimal effects. These findings offer valuable insights for regional ecological management and the sustainable development of agriculture and animal husbandry in Inner Mongolia.