Sentinel-2 is a high-resolution optical Earth observation mission within the GMES （Global Monitoring for Environment and Security） programme， which is renamed Copernicus in 2012， jointly implemented by the EC （European Commission） and ESA （European Space Agency） for global land observation with high revisit capability to provide enhanced continuity of data so far provided by SPOT and Landsat. Copernicus is the most ambitious Earth Observation programme to date. It provides accurate， timely and easily accessible information to improve the management of the environment， understand and mitigate the effects of climate change and ensure civil security. At present， Sentinel-2 is one of the most important data source for remote sensing monitoring and application research， and has been widely used in monitoring natural disasters such as floods，forest fires， landslides， volcanic eruptions， and emergency response and humanitarian crises around the globe，and there are also great potentials in detecting glacier and ice and supporting relief efforts for cryospheric disaster.In this study， the glacier and ice avalanches occurred in Arutso Lake basin in northwestern Tibet and Sedongpu basin in southeastern Tibet in 2016 and 2018 were investigated using Sentinel-2 images and field surveys， and the evolution process of two events were reproduced， which has important reference significance for monitoring cryospheric hazard， emergency relief and management in other mountain regions on the world.Study shows that Arutso No. 53 glacier avalanche completely melted away in July 2018 after lasting for two years from occurrence to final disappearance， while the area of Arutso No. 50 glacier avalanche is 0.58 km2 left on June 22，2021 because of more thickness compared to Arutso glacier No. 53. Four large-scale ice-rock ava lanche and debris flow events in the Sedongpu basin in 2017 and 2018 not only had significant impacts on the river flow， landscape and landform in the basin， but also caused great disasters in the basin and downstream.Two glacier and ice avalanche events were caused by climate warming and local heavy precipitation， acting on specific topographic and geomorphic structure of glacier properties in high mountains. Specifically， Arutso glacier avalanche was caused by climate- and weather-driven external forcing， acting on specific polythermal and soft-bed glacier properties and is an unprecedented large catastrophic instability of low angle mountain glaciers. Glacier and snow melting caused by climate warming and heavy rainfall are main triggering factors for ice and rock avalanche in the Sedongpu basin， which is a typical hazard cascades originating from cryosphere， followed by rock fall， debris flow， dammed lake， and lake outburst flood disaster. It often occurs in the Sedongpu basin and will continue to occur for a long time in the future， and the high mountain ridge covered with ice and snow in the right side of back of the basin is still a high-risk area for ice and rock avalanches in the future.
The Tian Shan region hosts a large number of surge-type glacier， Detailed surge process and control mechanism analysis are still unclear for surge glaciers in Tian Shan. In this paper， the surface velocity and digital elevation models of the Mushketov Glacier in the central Tian Shan are obtained by feature-tracking of Sentinel-1A SAR data and differential Interferometry of TerraSAR-X/TanDEM-X， respectively. Geodetic method was employed to calculate the glacier surface elevation change. The results show that the surface velocity of the main stream of the glacier has increased significantly since the end of summer in 2017， reached its peak in winter， up to 4.4 m d-1 and decreased sharply at the end of summer in 2018. The middle and upper reaches of glacier from 2000 to 2012 are accumulated， with an average thickening of 9.23±4.62 m， and the ice tongue thinned dramatically； From 2012 to 2014， the ice tongue continued to thin， the average thickness of reservoir area increased by 1.23 ± 0.91 m； From 2014 to 2018， the glacier reservoir area was significantly thinned， with the maximum decrease of 42.6 ± 1.82 m， the elevation of the receiving area increased significantly， and the highest uplift was 75.6 ± 1.82 m. According to the change of elevation and the characteristics of flow velocity and analysis of glacier surge mechanism using glacier flow law， we concluded that the Mushkotov Glacier surged from 2017 to 2018， and the surge is mainly controlled by hydrological conditions. Combined with the available history data， it is inferred that the glacial surge interval is about 60 years.
Sea ice is one of the most important indicators in the global climate system， which has a significant impact on regional heat regulation and climate change. Sea ice thickness is an extremely important parameter in the research of sea ice change. The existing altimeter has some disadvantages in sea ice monitoring， such as low spatial resolution and narrow observation range. However， the new three-dimensional imaging radar altimeter can achieve wide-range and high-precision sea surface measurement with small incident angle and short baseline interferometry technology， which has great potential in sea ice monitoring. In this paper， based on the principle of interference altimetry， the influence of the slant range measurement error， baseline inclination error， baseline length error and interference phase error on the altimetry accuracy of three-dimensional imaging radar altimeter is analyzed， and the total altimetry error composed of the above errors is simulated. Combined with the sea ice distribution image and the sea ice thickness estimation principle， the factors to improve the estimation accuracy of sea ice thickness are explored. Experiments show that the accuracy of sea ice thickness estimation by 3D imaging radar altimeter can be improved by correcting the baseline inclination error by using the correlation between interference phase and baseline inclination angle， and the estimation error of sea ice thickness can be reduced from 85.47 cm to 70.23 cm， and the accuracy can be improved by 17.83%.
Glacier movement can cause debris flow， landslide and other geological disasters to a certain extent， so it is very important to master process of glacier movement. Glacier velocity reveals the process of glacier movement， but some existing methods of constructing glacier velocity do not consider direction of glacier flow， and mechanism of glacier movement revealed is not precise enough. Based on Sentinel-1A ascending orbit data from 2018 to 2020， this paper uses Pixel Offset Tracking （POT） technology to obtain azimuth and range displacement fields of the South Inylchek Glacier in Central Tianshan Mountains， introduces the glacier flow direction to construct the axial two-dimensional velocity of glacial mainstream line， and analyzes the mechanism of glacier movement. The results show that pixel migration velocity in stable region is far less than the axial two-dimensional velocity of glacier mainstream line. The axial two-dimensional velocity model constructed by POT technology is good for monitoring the glacier movement process. In 2018， 2019 and 2020， the axial two-dimensional average velocities of the South Inylchek Glacier in Central Tianshan Mountains are 62.28 cm/d， 49.41 cm/d and 61.89 cm/d， respectively. The axial two-dimensional velocity of ablation area （ice tongue） decreases slowly at first， then increases gradually， and last decreases rapidly with the decrease of elevation， and the glacier velocity decreases from axis to edge of both sides. With the increase of temperature， the speed of glacier movement increases gradually. The increase of temperature may be the main reason for the acceleration of glacier velocity.
For space-based earth observation imaging missions， the cloud cover contained in an remote sensing image often determines whether the data is available. However， the spectral characteristics of cloud and snow are similar， which makes it difficult to distinguish them. The purpose of the research on cloud and snow recognition is to improve the ability to judge the validity data. So an novel method is designed to solve it. Firstly， an improved lightweight convolutional neural network， which is built based on the proposed DCP （Double Convolution Parallel） structure， is used as the backbone to classify the quasi-cloud （cloud， snow and highly reflective ground objects） and other ground objects. Secondly， the textures and gray features of cloud and snow and ground objects are analyzed by a binary tree network formed by fractal dimension and angular second moment for fine recognition in further. The network weight layers are only six （four convolutional layers and two full connection layers）. The proposed method is trained on the data sets containing cloud， snow and cloud-snow with different ground sample resolutions from Tianzhi-1 and SPOT4/5/6 and Pleiades. When compared on the accuracy with reference methods， such as random forest， SVM， traditional methods and binary tree methods， our method provide an increasing accuracy to 89.08%. The experimental results shows： （1） The comparative experiment between network structures shows that the DCP could effectively improve the model ability of feature information extraction and promote faster convergence； （2） Texture features analysis of remote sensing images makes the recognition process not completely dependent on convolutional neural network， so as to reduce the network depth and weight parameters； （3） The combination of traditional remote sensing analysis method and neural network is better than one of them alone， which can improve the accuracy of cloud and snow recognition. The proposed method is suitable for cloud and snow recognition on panchromatic， multispectral and hyperspectral remote sensing imagery.
The passive microwave snow-depth retrieval algorithm is an important method to obtain the surface snow depth information of the Tibetan Plateau on a large scale. In order to evaluate the applicability of the current passive microwave snow-depth retrieval algorithms in the Selin Co and Nam Co regions of the Tibetan Plateau， AMSR2 brightness temperature data and snow depth data of ground stations are used， while R， Bias and RMSE are used as evaluation indicators. Five algorithms including Chang2 algorithm， Che algorithm， SPD algorithm， AMSR2 algorithm and Jiang algorithm are chosen. The results show that the Jiang algorithm has the best overall performance， with the highest R value of 0.68 at Nam Co station. The Che algorithm has a good retrieval effect on shallow snow， and its Bias at Bangor Station is -0.66 cm. The Chang2 algorithm performed well for the deep snow of Nam Co station and Selin Co station， with R values of 0.63 and 0.50 in the two places respectively. The retrieval effect of SPD algorithm is the most unsatisfactory， and the snow depth is overestimated obviously， among which shallow snow is overestimated by nearly 20 cm. The performance of AMSR2 algorithm differs greatly between regions， and the retrieved results at Namco Station are better than those at Selin Co Station and Bangor Station. Except for the SPD algorithm， all other algorithms underestimate snow depth in the study area， which is consistent with previous research results.
Due to the lack of studies on phenological evolution and driving factors of snow cover in the Chinese Tianshan Mountainous Region （CTMR）， this study calculated the number of Snow Cover Days （SCD）， Snow Onset Date （SOD） and Snow End Date （SED） in the CTMR in each hydrological year on a pixel-by-pixel basis based on the daily cloud-free snow area products of MODIS from 2002 to 2017. Then combined the temperature and precipitation data to analyze the temporal and spatial characteristics of snow phenology and its response to topography and climate change. The results were followed： The spatial distribution of snow phenology in the CTMR was different. SCD presented a distribution pattern of high in the west and low in the east， high in the north and low in the south. In high-altitude areas， SOD was earlier and SED was later. SOD in the central and western regions showed an advance trend， in which the advance trend in Bayinbulak prairie was obvious. The delayed SOD happened in southwest slope， north slope and eastern region. And the delayed SED occurred in the middle and ridgeline areas. Below 5 000 m asl， the average gradients of SCD， SOD and SED with altitude were 4.93 d/100 m， -1.64 d/100 m and 2.94 d-1.64 d/100 m， respectively. The growth trend of SCD reached the maximum at 2 500-3 000 m， and that of SED gradually decreased with the increase of altitude. The response of SED to topographic change was similar to that of SCD， but the impact of altitude on SED was weaker than that of SCD. The warming and wetting in autumn were the main reason for the postponement of SOD in the CTMR. And the warming in spring can promote the advance of SED， while wetting in spring can contribute to the postponement of SED. This study can effectively monitor the SOD and SED， reveal the climate change， and provide significant information support for the prediction of river runoff and the early warning of natural disasters such as flood and debris flow.
Flights onboard L-band radiometer are carried out in the Shandian River Basin in the “Soil Moisture Experiment in the Luan River （SMELR）”， airborne brightness temperature （TB） observations are acquired. In this paper， studies on the comparison of airborne TB and that of SMOS and SMAP are performed. The data of airborne and satellites are selected according to their observation periods， incidence angles and polarization mode， and three spatial matching schemes are proposed according to the difference of their scales. By taking the direct numerical bias （satellites minus airborne）， correlation coefficient （R） and unbiased square root error （ubRMSE） as indicators， the quantitative difference between airborne TB and that of satellites are explored， results show that： the variation of the TB difference between airborne and satellite with the incidence angle are consistent with the theory， and the reliability of the airborne TB observations are initially verified； the total average TB difference varies greatly under difference spatial matching schemes， and the quantitative influence of spatial matching methods in the validation and comparison of multi-source and multi-scale data are confirmed； the TB difference between airborne and SMAP is smaller than that of SMOS， which reflects the similarities and differences in sensor design， observation mechanism and spatial mapping methods.
The Qinghai-Tibet Plateau has a special geographical location and remarkable environmental characteristics， and it is a key participant and decision-maker in the role of the Earth system. Using large-scale satellite microwave remote sensing data to study soil moisture can not only provide theoretical support for understanding the quantitative impact of typical regions on the global water， air， energy and heat interaction mechanism， but also provide practical basis for confirming the reliability of remote sensing data. Based on SMOS （2011—2020） and SMAP （2016—2020） satellite soil moisture data， supplemented by ISMN data， GPCP precipitation data， MOD16A2 evapotranspiration data and C3S surface landcover data， this paper studied the temporal and spatial variability of soil moisture over the Tibetan Plateau during the monsoon and vegetation growing season. Based on the annual mean value of soil moisture（
Soil moisture plays a very important role in the energy exchange and water cycle between land and atmosphere. At present，microwave remote sensing satellites represented by SMOS are the main way to obtain global soil moisture information， and the brightness temperature simulation is a crucial link in the SMOS satellite retrieval algorithm. Based on the L-MEB model， this paper investigates the influence of key auxiliary parameters on brightness temperature simulation and the feasibility of using rich and reliable measured data to simulate brightness temperature using ISMN measured data and SoilGrids soil texture data. The results show that soil moisture and soil temperature are transient in time and have a stochastic effect on the brightness temperature simulation， while sand and clay content are stable in time and belong to the slowly varying background parameters， which have a systematic effect on the brightness temperature simulation. The correlation coefficients between the simulated H- and V- polarized brightness temperatures and SMOS simulated brightness temperatures in this paper reached 0.59 and 0.65， respectively， which proved that it is feasible and effective to use ISMN measured data and SoilGrids data as auxiliary data for brightness temperature simulation.
The level 2 （L2） soil moisture data of SMAP satellite is a direct retrieval result， which can reflect its comprehensive ability of soil moisture retrieval from models， algorithms， parameters and other aspects. At this level， SMAP designed soil moisture data at multiple scales including L2_SM_P（36 km）、L2_SM_P_E（9 km） and L2_SM_SP（3 km and 1 km），the soil moisture data can meet different experimental and application requirements. In this paper， the difference characteristics between SMAP L2 soil moisture data and ISMN measured data are studied and analyzed by using the ISMN ground measured soil moisture data as reference， Bias， root mean square error （RMSE）， unbiased root mean square error （ubRMSE） and correlation coefficient （R） as analysis indicators. The results show that under different static conditions （climate type， soil property and vegetation type）， vegetation has the largest impact on the difference， while soil property has the smallest impact； Under different dynamic conditions （surface soil moisture， vegetation optical depth and surface temperature）， vegetation optical depth and surface soil moisture have a greater impact on the difference， while surface temperature has a smaller impact； Among the four SMAP L2 soil moisture data with different spatial scales， the difference between the 9km data and the ISMN ground measured data is the smallest， followed by the 36km data， 3km data and 1km data scales； According to the static and dynamic conditions， the differences between the 36km and 9km scale data and the ISMN ground measured data are similar， and the differences between the 3km and 1km data are similar.
Soil moisture is a key parameter in the study of hydrological cycle， ecological environment， climate change， etc.， The acquisition of high-resolution long time series soil moisture information is of great significance for agricultural management and crop growth monitoring， and remote sensing monitoring is also a difficult problem in research. Based on the Sentinel-1 radar data and Sentinel-2 optical data of the time series（2019—2020）， this paper constructs a synergistic retrieval model of surface soil moisture， that is， a method for detecting changes in surface soil moisture under bare soil conditions， And the normalized vegetation index was used to correct the vegetation impact. The proposed method has achieved soil moisture mapping with a spatial resolution of 100 meters in the permafrost region （Wudaoliang） of the Qinghai-Tibet Plateau. The comparison and validation with the in-situ measured soil moisture observed show that the correlation coefficient between the soil moisture estimates and the ground measurements is 0.672≤R≤0.941， and the unbiased root mean square error （ubRMSE） is between 0.031 m3/m3 and 0.073 m3/m3. Soil moisture changes are closely related to regional precipitation events and characteristics， verifying that the change detection method proposed in this study has high applicability in the flat terrain and sparse vegetation areas on the Qinghai-Tibet Plateau.
The comprehensive assessment of multiple Soil Moisture （SM） products is helpful to understand the characteristics and differences of products， and is of great significance to the algorithm improvement and rational application of products. The differences and applicability of three remote sensing SM products （SMOS_L3， AMSR-E_LPRM and ESACCI v04.5） and three model-based SM products （ECMWF_ERA5， GLDAS_Noah v2.1 and GLDAS_CLSM v2.2） in typical regions of North China from 2010 to 2011 were analyzed from the aspects of spatial distribution， in-situ evaluation， land cover type and dry and wet classification. The possible reasons affecting the accuracy of soil moisture products were discussed from multi-angle. Results show that： （1） On the annual scale， all products can effectively characterize the distribution of soil moisture in the arid region of the West. On the seasonal scale， ESACCI product and three model-based SM products had high soil moisture and similar spatial distribution in summer and autumn； （2） In terms of in-situ evaluation， ERA5 product outperformed other products with the highest average Pearson correlation coefficient （0.582） and the lowest unbiased root mean square error （0.045 m3/m3）. The model-based SM products were superior to remote sensing SM products in terms of ubRMSE and R and can effectively represent the dynamic characteristics of in-situ observations. However， the time variations range of model-based SM products was low， which may lead to dry or wet bias. ESACCI product had the highest accuracy among remote sensing SM products. AMSR-E product performed well in Bias （-0.015 m3/m3）， but the correlation with in-situ observations was low due to the influence of weather. SMOS product was affected by Radio-frequency Interference， and its overall performance was average； （3） SMOS and AMSR-E products were sensitive to farmland and forest respectively. The soil moisture distribution of other products under different land types was consistent with the actual situation， and can show dry and wet distribution.
Topography is one of the important factors affecting the characteristics of surface microwave radiation. Based on the experiment of observing artificial undulating surface with ground-based microwave radiometer， and the improved mountain surface microwave radiation model， the influence of terrain undulating and surface heterogeneity on the brightness temperature of surface microwave radiation was studied， and the microwave radiation model was verified according to the empirical data. The better results show that the effect of terrain occlusion on surface microwave radiation is consistent with the geometric optics hypothesis in the model. The simulation effect of this model is good， the measured data and the simulated data are consistent with the change trend of terrain， the error between the measured value and the simulated value is smaller after considering the surface roughness. The coupling of surface heterogeneity and topographic relief results in obvious difference of brightness temperature observed at different azimuth angles of H polarization and V polarization. These results provide a reference for the topographic correction model of microwave brightness temperature on mountain surface.
Reasonable and effective soil moisture observation network can better monitor regional soil moisture based on in-situ data and provide high-precision soil moisture information. Based on the study of spatial and temporal variability of soil moisture in the region from year 2010 to 2019， and superimposed with different types of auxiliary data， the study area was divided twice， and an optimal layout method of soil moisture observation network was designed. On the basis of the existing 24 stations， 79 new stations were added to the designed observation network， which reduced the monitoring area of the existing single point to 381—792 km2， and the monitoring efficiency increased by 71.57%. This method follows the idea of "partition before laying out"， first utilizing the relative continuous satellite remote sensing data to acquire regional soil moisture geography law， and then deduce the layout plan of the ground station network， which can provide a new reference for the optimization of the layout of the related station network.
Soil moisture is one of the important parameters of ground - atmosphere energy exchange and global water cycle， and also a key parameter in the research of hydrology， meteorology， agriculture， and other researches. Soil moisture with high spatial resolution is of great significance in discussing regional hydrological process， ecological environment protection and agricultural water resources management. Based on Sentinel-1 radar data， this paper developed a high spatial resolution soil moisture retrieval algorithm in the Tibetan Plateau， and obtained soil moisture with a regional scale spatial resolution of 20 m. firstly， the algorithm optimized the parameters of the water cloud model based on filed data， Sentinel-1 radar data and MODIS NDVI. Secondly， the simulation database was constructed using the optimized water cloud model， and the artificial neural network algorithm was used to train the simulation data to build a soil moisture retrieval algorithm based on neural network. In order to test the algorithm， the regional soil moisture values of the Tibetan Plateau site were retrieved using Sentinel-1 radar data， and verified with the measured soil moisture. The validation results showed that there is a high correlation between the estimated and measured soil moisture， and the correlation coefficients was 0.784—0.82， the root mean square error was 0.052 m3/m3—0.064 m3/ m3. The estimated soil moisture could capture the change trend of the measured soil moisture in the time series. This study can provide a certain reference for soil moisture monitoring with high spatial resolution in the Tibetan Plateau.
Surface soil moisture is a key parameter of terrestrial systems in terms of ground-air energy exchange， and the surface soil moisture in karst areas is an important factor to promote karst action， affect the soil erosion， and cause karst rocky desertification. Therefore， it is of great significance to accurately determine the distribution of surface soil moisture and its dynamic change in karst areas to the safety of ecological and geological environment and regional climate change. Taking the typical karst area of Guangxi as the study area the LST-VI feature space was constructed using MODIS surface temperature data and vegetation index data. Firstly， the applicability of the four different vegetation indices （NDVI， EVI， SAVI， and FVC） in karst areas was compared， and FVC is selected as the optimal vegetation index for the LST-VI feature space in the study area. The soil moisture index M0 based on the uniform normalized T*-FVC feature space is derived. The track and regularity of M0 with time under different underlying surface types such as forest， farmland and karst areas are studied in detail，as well as the spatial distribution changes of M0 and the causes. The results show that the track and regularity of M0 with time is similar under the same underlying surface type in the T*-FVC feature space， indicating that the underlying surface type is an important factor affecting the change of soil moisture. In terms of spatial distribution， the distribution of M0 in Guangxi has the characteristics of being smaller in summer than in winter， smaller in southwest than in northeast， smaller in karst than in non-karst areas， and the seasonal variation of M0 in farmland is evident. Overall， the spatio-temporal dynamic change of soil moisture in karst areas has been achieved using the normalized T * -FVC feature space.
Winter dead grass is a blank field of remote sensing monitoring services， by revealing the unique spectral characteristics of dead grass， establish alpine dead grass monitoring technology and a series of research， can promote alpine region dead grass remote sensing monitoring new services and the development of service time， for the Qinghai-Tibetan plateau ecological environment protection and management to provide innovative technical support. Based on two field observation tests in August and November 2016 in area of Sanjiangyuan in the hinterland of the Qinghai-Tibet Plateau， 72 ground hyperspectry data were obtained including the samples of fresh grass and dead grass. A significant linear pattern of the reflect spectrums for dead grass showed during from 1.5% at 350nm to about 38% near 1 350 nm at range of visible and near-infrared spectrum band. There are evidently differences between dead grass and fresh grass in spectral reflectivity， dead grass completely lost spectral characteristics which shown in the green vegetation with a strong absorption in the red band and a weak absorption in the green band， also shown a high reflection in the 760—1 300 nm （near-infrared band）. The red light band reflectance is about 4.9 times that of fresh grass， while the green light band reflectivity is nearly 1.4 times. In this study， we provided a normalized Dead Grass Vegetation Index （DGVI） using the band 5 and band 3 according to the MODIS satellite data. It was found that the DGVI can effectively identify dead grass in winter， the correlation coefficient between the measured and estimated data by DGVI reaches 0.68 （P <0.05）， and DGVI is significantly better than the general vegetation index. Our study indicated that the DGVI can be used to monitoring for alpine dead grass in winter.
The central urban area of Tianjin is taken as the research object. Based on the abundant OSM road network data and POI big data， functional area identification is carried out at the fine scale. The road space generated by OSM road network data is used to divide the central urban area of Tianjin into 1960 research units. The density distribution and functional area distribution characteristics are analyzed by combining the POI data with weight assignment. The research results show that： （1） In the distribution of urban function density， except for the concentrated distribution of industrial functions in the periphery of the central city， the distribution of other urban functions shows the characteristics of gradual dispersion from the center to the periphery； （2） In a single functional area， commercial areas and public management and public service areas account for a relatively large proportion， while the other four single functional areas account for a small proportion； （3） Among the mixed functional areas， the mixed functional area mainly composed of business-public management and public services has the largest proportion； （4） Comparing the recognition results of functional areas with the Amap， it is found that the accuracy of the recognition results of urban functional areas is relatively high.
To explore the gradient and difference of the influence of different urban building forms on LST， Xi 'an， Zhengzhou， Jinan as the research area， based on Landsat 8 TIRS images and urban 3D building data. Based on the multiple linear regression model， the influences of building form on LST in different seasons in the three cities were analyzed and the differences were compared ：（1） the cities with the largest influence on LST in summer and winter were Xi 'an （R2=0.414） and Jinan （R2=0.300）. The building coverage rate and average building height have the greatest impact on LST in summer and winter， respectively， with positive and negative impacts. （2） After the gradient classification of building coverage， it is found that when the coverage rate is less than 20%， the building volume density has a strong cooling effect on the three cities； When the coverage rate is 20%—40%， the average building height significantly reduces the surface temperature of the three cities. When the coverage rate is 40%—60%， the sky visible factor has a certain warming effect on the three cities， when the coverage rate is greater than 60%， the average building height greatly reduces the surface temperature of Jinan. （3） The average surface temperature of low-rise buildings in Xi 'an， Jinan and Zhengzhou is 9.5 ℃， 7.7 ℃ and 6.1 ℃， respectively. The surface temperature of the three cities shows a downward trend from low-rise to high-rise， and the surface temperature of Xi 'an is higher than that of Zhengzhou and Jinan in each gradient. The research shows that rational planning of urban building form is beneficial to alleviate the phenomenon of high surface temperature in central cities.
Vegetation change characteristics are the important contents of watershed ecological monitoring and the most critical information for environmental protection. In this study， MODIS EVI data products and Hurst index were used to analyze the temporal and spatial variation trend of vegetation in Huangshui River Basin from 2000 to 2019 and its continuity analysis. Combined with the meteorological observation data of temperature and precipitation， this paper analyzed the influencing factors of vegetation change in 9 counties and districts in Huangshui River Basin. The results show that from 2000 to 2019， the maximum annual EVI increase of vegetation in the Huangshui River Basin is 0.0063， and different counties and districts in the upper， middle and lower reaches show different change characteristics under the influence of temperature， precipitation， land use and other factors. For the annual EVI maximum， the increasing trend of the downstream is the most significant， and the change intensity of the river channel area is more obvious. Based on Hurst index analysis， the trend has a certain continuity in the short term. This study revealed the importance of vegetation monitoring trends in the plateau watershed by monitoring the temporal changes of vegetation， and provided a certain data support and scientific basis for watershed management and sustainable development.
Based on the principle of water balance， the spatiotemporal changes and the influencing factors of the water conservation capacity in the Shannxi Qinba Mountains were quantified by using MODIS， climate data， the DEM and soil texture data， etc. during the period of 2000—2020. The results showed that ① the water conservation capacity， which ranged from -142.84—419.41 mm， was higher in south and lower in north. The water conservation capacity of each city followed an order of Hanzhong>Ankang>Baoji>Xi’an>Shangluo>Weinan. ② The water conservation capacity decreased at a rate about 13.07 mm/a in recent 21 years. The regions that significant weakened accounted for about 48.36 percent. ③ The changes of the water conservation capacity was mainly closely related to its direct influencing factors， the precipitation and the evapotranspiration and finally the temperature among its indirect influencing factors.
As the head of the canal of the middle route of the South-to-North Water Transfer Project and the location of Danjiangkou Reservoir in Henan province， analyzing the impact of the expansion of surface water in Xichuan County on the production-living-ecological space and ecological migration is of great significance to regional ecological protection and development.This study uses the Google Earth Engine cloud platform and uses 1631 Landsat remote sensing images to dynamically analyze the evolution of Danjiangkou Reservoir from 2000 to 2020. By constructing an production-living-ecological space classification system， it analyzes the impact of Danjiangkou Reservoir expansion on Production-living-ecological space of Xichuan County. It also has a influence of the evolution of the pattern and the impact of ecological migration. The results show that： （1） From 2000 to 2020， the ecological space area of Xichuan County fluctuates up， the production space area gradually decreases， and the living space area continues to expand. （2） The surface water area of Danjiangkou Reservoir in Xichuan County has fluctuated and increased from 2000 to 2020. The expansion of Danjiangkou Reservoir has increased the submerged area of Xichuan County by 181.67 km2. （3） The expansion of the ecological space in Xichuan County， especially the expansion of the Danjiangkou Reservoir water body， took up 181.67 km2 of ecological and living space， reduced the carrying land area and increased the pressure on the carrying population， resulting in 162 000 ecological immigrants in Xichuan County.
