With the impact of global warming and socio-economic development, eutrophication has been observed frequently in Chinese lakes. Therefore, there is an urgent need to monitor the phytoplankton biomass of the lakes. In this paper, 756 lakes with an area more than 10 km² were selected as research objects. With the help of Google Earth Engine platform, we retrieved the chlorophyll-a (chl-a) concentration from 2003 to 2018, revealed the seasonal and annual nutritional status, and examined the spatio-temporal changes of the lakes. The relationship between spatio-temporal characteristics of lake trophic status and meteorological phenomena, socio-economy and lake features wers analyzed. The main conclusions are as follows: (1) The change of lake trophic states in China has obvious seasonality and regionality. About 90% of lakes were mesotrophic within 15 years of the study. In spring, most lakes in the plain areas of eastern China, the Northeast China Plain and mountain regions were oligotrophic, while in summer, many lakes turned into eutrophication. In comparison, most lakes on the Qinghai-Tibet Plateau and the Yunnan-Guizhou Plateau were eutrophic in spring. The interannual variations in chl-a concentration show that 82% of lakes in China had slight changes in chl-a concentration (the absolute annual rate is <0.5), and the rest showed dramatic variations. (2) The lake surface temperature and precipitation had strong influences on chl-a concentration. For more than 70% of the lakes, the concentration of chl-a had a positive correlation with the lake surface temperature and precipitation, most of which are located in the eastern and northern China. The population in buffer zone, altitude, and geographical location of the lakes also exert influence on the biomass of the phytoplankton.

Climate change risk has been a challenge for the development of society. As the ecosystems in the arid/humid transition zone (AHTZ) in northern China are characterized by sensitive and complex response to climatic fluctuations, assessing the climate change risk for ecosystems in the AHTZ is of scientific significance to regional climate governance and risk management. This study utilized the net primary productivity (NPP) as the indice for risk assessment. Based on the climate data of five global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and four Representative Concentration Pathways (RCPs), the NPP was simulated using an improved LPJ (Lund-Potsdam-Jena). A climate change risk assessment method was built to identify the climate change risk levels and their spatio-temporal distributions in the AHTZ at different periods in the future. Results show that NPP loss would gradually extend and aggravate the risk in the AHTZ from mid-term period to long-term period of the 21st century. The risk under the high-emission scenario would be more serious, mainly manifested as a negative anomaly and a downward trend of NPP. In particular, under RCP8.5, 81.85% of the area may face climate change risk, and 54.71% will reach a high risk. During 2071-2099, under RCP8.5, the NPP anomaly in the high-risk area will reach (-96.00±46.95) gC m^-2 a^-1, and the changing rate of NPP will reach (-3.56±3.40) gC m^-2 a^-1. The eastern plain of AHTZ and the eastern grasslands of Inner Mongolia are expected to become the main risk concentration areas. The future vegetation growth in these areas may be adversely affected by climate change. Increasing warming and intensifying drying may be important causes for future climate change risks.

Climate change plays a significant role in the evolution of human civilization. The West Liaohe Basin of northeast (NE) China, an area sensitive to monsoon climate change, has a prolonged history of agriculture-based civilizations, making it an ideal place to study human-landscape interactions in the Holocene. Here, analyses of multi-proxies were applied to a sediment profile (XLW) obtained near the Xinglongwa archaeological site, with a reliable chronology that covered mainly the past 5000 years. The result showed that from 5.0 to 3.7 cal. ka BP, the climate turned cooler/drier as indicated by the low magnetic susceptibility and loss on ignition (LOI), coeval with the decline of the East Asian summer monsoon (EASM). This change in climate condition coincided with the demise of agriculture-based Hongshan Culture, which was later replaced by the Xiaoheyan Culture featured by fishing and gathering livelihood. During this period, the number of Neolithic sites in NE China decreased, with a significant southward migration, possibly related to climate deterioration. After 3.7 cal. ka BP, high values of magnetic susceptibility and LOI indicated enhanced terrestrial input, which may result from the strengthening of EASM under a warming climate condition. This could have promoted the recovery of agriculture and boosted the development of the Lower Xiajiadian Culture, during which a demographic expansion was indicated by a significant increase in site numbers. After 2.8 cal. ka BP, a decreasing trend in magnetic susceptibility and LOI hinted the deterioration of EASM with a cool/dry setting, which might have caused a southward shift of settlements in the Upper Xiajiadian Culture when farming was partially replaced by pastoralism.

Phenology is an important indicator of climate change. Studying spatiotemporal variations in remote sensing phenology of vegetation can provide a basis for further analysis of global climate change. Based on time series data of MODIS-NDVI from 2000 to 2017, we extracted and analyzed four remote sensing phenological parameters of vegetation, including the Start of Season (SOS), the End of Season (EOS), the Middle of Season (MOS) and the Length of Season (LOS), in tundra-taiga transitional zone in the East Siberia, using asymmetric Gaussian function and dynamic threshold methods. Meanwhile, we analyzed the responses of the four phenological parameters to the temperature change based on the temperature change data from Climate Research Unit (CRU). The results show that: in regions south of 64°N, with the rise of temperature in April and May, the SOS in the corresponding area was 5-15 days ahead of schedule; in the area between 64°N and 72°N, with the rise of temperature in May and June, the SOS in the corresponding area was 10-25 days ahead of schedule; in the northernmost of the study area on the coast of the Arctic Ocean, with the drop of temperature in May and June, the SOS in the corresponding area was 15-25 days behind schedule; in the northwest of the study area in August and the southwest in September, with the drop of temperature, the EOS in the corresponding areas was 15-30 days ahead of schedule; in regions south of 67°N, with the rise of temperature in September and October, the EOS in the corresponding area was 5-30 days behind schedule; the change of the EOS in autumn was more sensitive to the change of the SOS in spring, because the smaller temperature fluctuation can cause the larger change of the EOS; the growth season of vegetation in the study area was generally moving forward, and the LOS in the northwest was shortened, while the LOS in the middle and south of the study area was prolonged.

In arid and semi-arid environments, desert vegetation plays an important role in preventing soil erosion by wind and maintaining the stability of the desert and oasis ecosystem. Four types of typical desert vegetation, namely, Populus euphratica, Haloxylon ammodendron, Nitraria sibirica and Halostachs caspica, in different habitats (i.e., banks, sand dunes, desert, and salt marshes) were chosen as the model vegetation in this research. The δ²H and δ¹⁸O of rain water, soil water and plant water were applied to identify the water sources and quantify the proportions of water used during the whole growth period (from March to October). The results showed that the precipitation δ²H and δ¹⁸O in the Ebinur Lake basin varied from -142.5‰ to -0.6‰ and from -20.16‰ to 1.20‰, respectively. The largest values of δ²H and δ¹⁸O were found in summer and the smallest ones in winter. The soil water δ²H and δ¹⁸O of the four habitats decreased gradually with the increase of the depth. The δ²H and δ¹⁸O values of water extracted from the four plant stems had similar variation trend, i.e., the maximum was observed in spring, and the minimum was in summer. Among the four plants, Halostachs caspica had the highest stable isotopic values in the stem water, followed by Nitraria sibirica, Haloxylon ammodendron and Populus euphratica. The water sources and utilization ratios of desert vegetation varied in different growth stages. In the whole growing period, Haloxylon ammodendron mainly used groundwater. The proportion of water used by Nitraria sibirica varied greatly throughout the growing season. In spring, plants mainly relied on surface soil water, with a contribution rate of 80%-94%; in summer, the proportion of deep soil water use was 31%-36%; and in autumn, the proportion of middle soil water use was 33%-36%. Halostachs caspica mainly relied on topsoil water in spring and autumn, and the proportion of soil water in the middle layer slightly increased in summer, which was 20%-36%. Populus euphratica mainly used the intermediate soil water in spring, with a utilization rate of 53%-54%; in summer, groundwater was the main source, with a utilization rate of 72%-88%, and only 2%-5% from river water; in autumn, the utilization rate of river water rose to 11%-21%. The results indicated that there were significant differences in water use sources during the growing season of desert vegetation in arid areas. This research provides a theoretical basis for understanding the water use mechanism, water adaptation strategies, and vegetation restoration and management of desert vegetation in arid areas.

Exploring the influence of urban spatial morphology layout on the urban heat island (UHI) at the urban scale is of great significance for the improvement of ventilation environment and the ecological and livable urban planning. Taking Beijing, China as an example, this study analyzed the UHI spatial characteristics using the hourly temperature data of high-density automatic weather stations in 2009-2018 and the 2018 NPP/VIRRS night-light satellite data. Using 1:2000 basic geographic information data and Landsat8 satellite remote sensing data in 2017, based on remote sensing and GIS technology and morphological models, we extracted eight morphological parameters in the main urban area of Beijing, namely, building height (BH), building density (BD), building standard deviation (BSD), floor area ratio (FAR), frontal area index (FAI), roughness length (RL), sky view factor (SVF), fractal dimension (FD) and three land surface parameters consisting of vegetation coverage (VC), impervious cover (IC), albedo (AB). The relationship between these morphological parameters and UHI was further examined at the urban scale using the spatial statistical method. Results show that the downtown area of central Beijing has presented a relatively fixed distribution pattern of UHI at annual scale, four seasons, and 02:00 at nighttime in the past 10 years. The UHI of the annual, spring, summer, autumn, winter, 14:00, and 02:00 are 1.81℃, 1.50℃, 1.43℃, 2.16℃, 2.17℃, 0.48℃, and 2.77℃, respectively. The eight spatial morphological parameters have obvious spatial correlations with UHIs for most of the year, and the correlations are stronger in winter than in other seasons, and stronger at 02:00 am than at 14:00 pm. The top three parameters are SVF, FAR, and BD. There are spatiotemporal changes in the impact of different spatial morphological parameters and land surface parameters on UHI. Spatial morphological parameters have become important drivers of UHI change and the individual contributions of the eleven parameters to UHI changes are 13.7% to 62.2%. The spatial morphological parameters that contribute the most in summer, winter, and the whole year are BD (43.7%), SVF (62.2%), and SVF (43.0%), respectively; and the corresponding largest land surface parameters are VC (42.6%), AB (57.1%), and VC (45.4%), respectively. The comprehensive contribution of multiple parameters to UHI changes in summer, winter, and the whole year are 51.4%, 69.1%, and 55.3%, respectively; and the dominant influencing factors are BD, SVF, and BD.

Coastal wetlands are special land-sea transitional ecosystems with important functions. It is of great significance to obtain the spatiotemporal distribution data of coastal wetland vegetation accurately. Previous wetland mapping studies focusing mainly on high spatial and spectral resolution images often have difficulties such as high data acquisition costs and limited coverage, so these methods are only suitable for small regions. Sentinel-2A/B satellite images with high spatial and temporal resolution and free sharing, make it possible for us to dynamically monitor large-area coastal wetlands. Based on Sentinel-2 images in 2018, this study proposed the pixel-level SAVI time series and double logistic vegetation phenological feature fitting reconstruction model, used a random forest algorithm to classify Yancheng coastal wetland vegetation in Jiangsu, East China, and then discussed the applicability of vegetation phenological characteristics (VPC) classification method. The results show that the overall accuracy of mapping based on VPC was 87.61%, which was 19.57% higher than that of the conventional single image classification, and the results were consistent with the actual distribution of wetlands. The vegetation discriminant phenological parameters can provide the basis for differentiating various types of vegetation, which can be applied to coastal wetland classification in the case of missing or insufficient image data. The phenological parameters have improved the method based on VPC, which can be applied to the rapid and accurate extraction of coastal vegetation and also provides new ideas to solve the problem of insufficient data in coastal wetland classification research. The method based on VPC in the pixel-level time series can achieve the accurate classification of the mixed zone of vegetation communities and the effective differentiation of "the same spectrum with different objects", which is applicable to the coastal wetland classification in large regions and improves the mapping accuracy of coastal wetland vegetation effectively.