The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China's river basins, the surface water in the Huai River Basin has significant impacts on agricultural plants, ecological balance, and socioeconomic development. However, it is unclear how water areas responded to climate change and anthropogenic water exploitation in the past decades. To understand the changes in water surface areas in the Huai River Basin, this study used the available 16,760 scenes Landsat TM, ETM+, and OLI images in this region from 1989 to 2017 and processed the data on the Google Earth Engine (GEE) platform. The vegetation index and water index were used to quantify the spatiotemporal variability of the surface water area changes over the years. The major results include: (1) The maximum area, the average area, and the seasonal variation of surface water in the Huai River Basin showed a downward trend in the past 29 years, and the year-long surface water areas showed a slight upward trend; (2) the surface water area was positively correlated with precipitation (p < 0.05), but was negatively correlated with the temperature and evapotranspiration; (3) the changes of the total area of water bodies were mainly determined by the 216 larger water bodies (>10 km 2 ). Understanding the variations in water body areas and the controlling factors could support the designation and implementation of sustainable water management practices in agricultural, industrial, and domestic usages. ranks the first among the major river basins in China, and it plays an important role in China's economic and social development. According to the 2016 Huai River Water Resources Bulletin, the surface water resource supply in the Huai River Basin accounts for 74.6% of the total water supply of various water supply projects. Therefore, the temporal and spatial variation characteristics of surface waters need to be accurately mapped to ensure the sustainable economic and social development of the river basin and the stability of the ecosystem.Previous studies mapped the surface water using different data, algorithms and produced different spatial scale production. Satellite-based methods have advantages compared to the traditional methods in surface water mapping due to the low cost, high frequency, and repeatable observations. In recent decades, regional, continental, and global-scales surface water areas have been investigated using the advanced very high resolution radiometer (AVHRR) [5,6], the moderate-resolution imaging spectro-radiometer (MODIS) [7], Landsat [8][9][10][11][12][13][14][15][16][17][18], Sentinel satellite images and so on. Meanwhile, many satellite-based approaches have been developed to detect surface water. The surface water detection algorithms can be roughly divided into general feature classification methods and thematic water surface extraction algorithms [17,19]. General feature cl...

Temperatures over the past three decades have exhibited an asymmetric warming pattern between night and day throughout the Tibetan Plateau. However, the implications of such diurnally heterogeneous warming on vegetation growth is still poorly understood. In this paper, we evaluate how vegetation growth has responded to daytime and night-time warming at the regional, biome, and pixel scales based on normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2015. We found a persistent increase in the growing seasonal minimum temperature (T min) and maximum temperature (T max) over the Tibetan Plateau between 1982-2015, whereas the rate of increase of T min was 1.7 times that of T max. After removing the correlations between T min , precipitation, and solar radiation, we found that the partial correlation between T max and NDVI was positive in wetter and colder areas and negative in semi-arid and arid regions. In contrast, the partial correlation between T min and NDVI was positive in high-cold steppe and meadow steppe and negative in montane steppe or wet forest. We also found diverse responses of vegetation type to daytime and night-time warming across the Tibetan Plateau. Our results provide a demonstration for studying regional responses of vegetation to climate extremes under global climate change.

Urbanization is a global problem with demographic trends. The urban heat island plays a dominant role in local climate systems. Despite existing efforts to understand the impacts of multiple urbanization factors on the urban heat island globally, very little is known about the attribution of urban heat island magnitude to urbanization in different locations or developmental phases. In this study, based on global land surface temperature data, urban spatial domain data, gross domestic product (GDP), and population data, we analyzed the influence of multiple urbanization factors on global surface urban heat island intensity (SUHII). We also tentatively compared the abovementioned factors between different regions across the globe, especially between China and the USA, the largest countries that are experiencing or have experienced rapid urbanization in recent decades. The results showed that global SUHII had remarkable spatial heterogeneity due to the geographical and socioeconomic variation between cities. There was a significant correlation between SUHII and population as well as GDP in global cities. Moreover, this study suggested that the impacts of population on SUHII might be stronger in the early stages of urbanization, and the GDP factor would become a critical factor at a certain development level. The urban area also had non-ignorable impacts on SUHII, while the correlation between SUHII and urban shape was relatively weak. All these may imply that the best approach to slow down SUHII is to find other solutions, e.g., optimize the spatial configuration of urban internal landscapes, when the urbanization reaches a high level.

The gross primary production (GPP) of vegetation in urban areas plays an important role in the study of urban ecology. It is difficult however, to accurately estimate GPP in urban areas, mostly due to the complexity of impervious land surfaces, buildings, vegetation, and management. Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and satellite images to estimate the GPP of terrestrial ecosystems including urban areas. Here, we report VPM-based GPP (GPPvpm) estimates for the world’s ten most populous megacities during 2000–2014. The seasonal dynamics of GPPvpm during 2007–2014 in the ten megacities track well that of the solar-induced chlorophyll fluorescence (SIF) data from GOME-2 at 0.5° × 0.5° resolution. Annual GPPvpm during 2000–2014 also shows substantial variation among the ten megacities, and year-to-year trends show increases, no change, and decreases. Urban expansion and vegetation collectively impact GPP variations in these megacities. The results of this study demonstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies of GPP and the terrestrial carbon cycle in urban areas.

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Regional land use change is the main cause of the ecosystem carbon storage changes by affecting emission and sink process. However, there has been little research on the influence of land use changes for ecosystem carbon storage at both temporal and spatial scales. For this study, the Qihe catchment in the southern part of the Taihang Mountains was taken as an example; its land use change from 2005 to 2015 was analyzed, the Markov-CLUES composite model was used to predict land use patterns in 2025 under natural growth, cultivated land protection and ecological conservation scenario, and the land use data were used to evaluate ecosystem carbon storage under different scenarios for the recent 10-year interval and the future based on the carbon storage module of the InVEST model. The results show the following: (1) the ecosystem carbon storage and average carbon density of Qihe catchment were 3.16×10 7 t and 141.9 t/ha, respectively, and decreased by 0.07×10 7 t and 2.89 t/ha in the decade evaluated. (2) During 2005-2015, carbon density mainly decreased in low altitude areas. For high altitude area, regions with increased carbon density comprised a similar percentage to regions with decreased carbon density. The significant increase of the construction areas in the middle and lower reaches of Qihe and the degradation of upper reach woodland were core reasons for carbon density decrease. (3) For 2015-2025, under natural growth scenario, carbon storage and carbon density also significantly decrease, mainly due to the decrease of carbon sequestration capacity in low altitude areas; under cultivated land protection scenario, the decrease of carbon storage and carbon density will slow down, mainly due to the increase of carbon sequestration capacity in low altitude areas; under ecological conservation scenario, carbon storage and carbon density significantly increase and reach 3.19×10 7 t and 143.26 t/ha, respectively, mainly in regions above 1100 m in altitude. Ecological conservation scenario can enhance carbon sequestration capacity but cannot effectively control the reduction of cultivated land areas. Thus, land use planning of research areas should consider both ecological conservation and cultivated land protection scenarios to increase carbon sink and ensure the cultivated land quality and food safety.

The estimation of optimum temperature of vegetation growth is very useful for a wide range of applications such as agriculture and climate change studies. Thermal conditions substantially affect vegetation growth. In this study, the normalized difference vegetation index (NDVI) and daily temperature data set from 1982 to 2006 for China were used to examine optimum temperature of vegetation growth. Based on a simple analysis of ecological amplitude and Shelford's law of tolerance, a scientific framework for calculating the optimum temperature was constructed. The optimum temperature range and referenced optimum temperature (ROT) of terrestrial vegetation were obtained and explored over different eco-geographical regions of China. The results showed that the relationship between NDVI and air temperature was significant over almost all of China, indicating that terrestrial vegetation growth was closely related to thermal conditions. ROTs were different in various regions. The lowest ROT, about 7.0 °C, occurred in the Qinghai-Tibet Plateau, while the highest ROT, more than 22.0 °C, occurred in the middle and lower reaches of the Yangtze River and the Southern China region.