The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation’s ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change has become an important part of current global change research. Since existing studies lack detailed descriptions of the response of vegetation to different climatic factors using the method of grey correlation analysis based on pixel, the temporal and spatial patterns and trends of enhanced vegetation index (EVI) are analyzed in the growing season in IMAR from 2000 to 2015 based on moderate resolution imaging spectroradiometer (MODIS) EVI data. Combined with the data of air temperature, relative humidity, and precipitation in the study area, the grey relational analysis (GRA) method is used to study the time lag of EVI to climate change, and the study area is finally zoned into different parts according to the driving climatic factors for EVI on the basis of lag analysis. The driving zones quantitatively show the characteristics of temporal and spatial differences in response to different climatic factors for EVI. The results show that: (1) The value of EVI generally features in spatial distribution, increasing from the west to the east and the south to the north. The rate of change is 0.22/10°E from the west to the east, 0.28/10°N from the south to the north; (2) During 2000–2015, the EVI in IMAR showed a slightly upward trend with a growth rate of 0.021/10a. Among them, the areas with slight and significant improvement accounted for 21.1% and 7.5% of the total area respectively, ones with slight and significant degradation being 24.6% and 4.3%; (3) The time lag analysis of climatic factors for EVI indicates that vegetation growth in the study area lags behind air temperature by 1–2 months, relative humidity by 1–2 months, and precipitation by one month respectively; (4) During the growing season, the EVI of precipitation driving zone (21.8%) in IMAR is much larger than that in the air temperature driving zone (8%) and the relative humidity driving zone (11.6%). The growth of vegetation in IMAR generally has the closest relationship with precipitation. The growth of vegetation does not depend on the change of a single climatic factor. Instead, it is the result of the combined action of multiple climatic factors and human activities.

Sichuan Province, China, is a typical ecologically fragile area that is sensitive to global climate change. Studies regarding the spatial-temporal variations and driving factors of FVC (Fractional Vegetation Coverage) in Sichuan Province’s vegetation ecosystem are of important theoretical and practical significance for revealing the relationship between global climate change and vegetation ecosystems. These studies are also important theoretical and practical significance for the evaluation of environmental quality and service function adjustment of terrestrial ecosystems. In existing studies, there is a lack of detailed depictions of the FVC response to climatic factors in the context of different vegetation types and different landform features in Sichuan Province. In this study, the spatial-temporal patterns and change trends for the FVC of the growing seasons during the 2000–2017 period for Sichuan Province were analysed based on the FVCs that were inversely determined from MODIS (MODerate-resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) remote sensing data, and they were combined with air temperature, relative humidity and precipitation data. Moreover, the GRA (Grey Relational Analysis) method was used to study the response of the FVC to climate changes. Based on the results of the GRA, the zoning of climatic factors as driving forces for the FVC was performed, and the differences in the spatial-temporal characteristics of the FVC response to different climatic factors were presented in quantitative form. Here, we found that the vegetation coverage in Sichuan province showed a slight degradation trend, and that the medium to low altitude woody plants were significantly degraded. The proportion of regions in which relative humidity (17.3%) and precipitation (17.4) were strong drivers for FVC changes, was much greater than the regions in which air temperature (1.8%) and other co-drivers were the force.

Aerosol is an essential parameter for assessing the atmospheric environmental quality, and accurate monitoring of the aerosol optical depth (AOD) is of great significance in climate research and environmental protection. Based on Landsat 8 Operational Land Imager (OLI) images and MODIS09A1 surface reflectance products under clear skies with limited cloud cover, we retrieved the AODs in Nanjing City from 2017 to 2018 using the combined Dark Target (DT) and Deep Blue (DB) methods. The retrieval accuracy was validated by in-situ CE-318 measurements and MOD04_3K aerosol products. Furthermore, we analyzed the spatiotemporal distribution of the AODs and discussed a case of high AOD distribution. The results showed that: (1) Validated by CE-318 and MOD04_3K data, the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the retrieved AODs were 0.874 and 0.802, 0.134 and 0.188, and 0.099 and 0.138, respectively. Hence, the combined DT and DB algorithms used in this study exhibited a higher performance than the MOD04_3K-obtained aerosol products. (2) Under static and stable meteorological conditions, the average annual AOD in Nanjing was 0.47. At the spatial scale, the AODs showed relatively high values in the north and west, low in the south, and the lowest in the center. At the seasonal scale, the AODs were highest in the summer, followed by spring, winter, and autumn. Moreover, changes were significantly higher in the summer than in the other three seasons, with little differences among spring, autumn, and winter. (3) Based on the spatial and seasonal characteristics of the AOD distribution in Nanjing, a case of high AOD distribution caused by a large area of external pollution and local meteorological conditions was discussed, indicating that it could provide extra details of the AOD distribution to analyze air pollution sources using fine spatial resolution like in the Landsat 8 OLI.

Inner Mongolia Autonomous Region (IMAR) is related to China’s ecological security and the improvement of ecological environment; thus, the vegetation’s response to climate changes in IMAR has become an important part of current global change research. As existing achievements have certain deficiencies in data preprocessing, technical methods and research scales, we correct the incomplete data pre-processing and low verification accuracy; use grey relational analysis (GRA) to study the response of Enhanced Vegetation Index (EVI) in the growing season to climate factors on the pixel scale; explore the factors that affect the response speed and response degree from multiple perspectives, including vegetation type, longitude, latitude, elevation and local climate type; and solve the problems of excessive ignorance of details and severe distortion of response results due to using average values of the wide area or statistical data. The results show the following. 1. The vegetation status of IMAR in 2000-2018 was mainly improved. The change rates were 0.23/10° N and 0.25/10° E, respectively. 2. The response speed and response degree of forests to climatic factors are higher than that of grasslands. 3. The lag time of response for vegetation growth to precipitation, air temperature and relative humidity in IMAR is mainly within 2 months. The speed of vegetation‘s response to climate change in IMAR is mainly affected by four major factors: vegetation type, altitude gradient, local climate type and latitude. 4. Vegetation types and altitude gradients are the two most important factors affecting the degree of vegetation’s response to climate factors. It is worth noting that when the altitude rises to 2500 m, the dominant factor for the vegetation growth changes from precipitation to air temperature in terms of hydrothermal combination in the environment. Vegetation growth in areas with relatively high altitudes is more dependent on air temperature.

We show an improvement in extraction of remotely sensed phenology in the Qinghai–Tibet Plateau (QTP). The improvement includes multiple preprocessing, newly proposed absolute growth rate and relative growth rate models based on botany and phenology, and selection of the appropriate growth rate model at different growing stages. We refer to this model as the tempo-differentially selected growth rate model (TDSGM). The newly developed TDSGM is a comprehensive and accurate remote sensing phenological extraction model without manual intervention, which is better than the current mainstream methods. Results show that: (1) influence of elevation and longitude on the QTP phenology has been more effective than that of latitude and vegetation types; (2) length of the growing season (LOS) was in a shortening trend in the long term, even it showed a slight extension in recent years in the short term; (3) the shortest LOS was identified in the area with an altitude of 3000 to 5500 m, which accounts for 90% of the total area of QTP. High instability of LOS was concentrated in meadows at 4000 to 5000 m; and (4) most previous studies on remotely sensed phenology showed that LOS was more related to the start of the growing season. However, we show that the LOSs of four out of seven QTP vegetation types were closely related to the end of the growth season, consistent with 1455 ground observations.