A brief review has been conducted on the progress of typical spaceborne and airborne polarimetric Synthetic Aperture Radar（SAR） systems at home and abroad， for which the implementation of radiometric and polarimetric calibration accuracies has been focused and surveyed. First the general requirements for the polarimetric SAR data calibration accuracy have been drawing from literature research， and then the status quo of representative polarimetric SAR systems in the word and the system data calibration accuracy achievements have been systematically presented， including the relative radiometric calibration accuracy， the absolute radiometric calibration accuracy， the polarization channel crosstalk accuracy， the polarization channel amplitude imbalance accuracy， and the polarization channel phase imbalance accuracy， etc. Finally， the key factors affecting the calibration accuracy of polarimetric SAR data have been analyzed， and the future calibration tasks meeting the new polarimetric SAR system design has been briefly discussed. This paper comprehensively describes the calibration accuracy information index of polarimetric SAR systems and their development status， and provides relevant researchers with timely， comprehensive and systematic information on the development requirements of polarimetric SAR systems and the research progress of calibration accuracy achievements.

The estimation of rice leaf nitrogen content is important to achieve the goals of high rice yield and efficient fertilization in the field. In this paper， we propose a Particle Swarm Optimization-Deep Forest （PSO-DF） model-based method for estimating the nitrogen content of rice leaves， which determines the number of estimation layers in the optimal cascade and the optimal estimator in the Deep Forest （DF） model parameters by a particle swarm optimization algorithm. The number of trees in the optimal estimator is determined by the particle swarm optimization algorithm to improve the regression accuracy of the DF model on Rice datasets.To verify the effectiveness of PSO-DF， this paper used an unmanned aircraft with a hyperspectral image collector to obtain hyperspectral images of Ningxia japonica rice， and sampled， measured， and analyzed the rice leaves at the same period， and extracted the three feature bands with the highest correlation coefficients with rice leaf nitrogen content， which were used as spectral features for inversion with rice nitrogen content data， and compared PSO-DF， the original model DF， and six other common The rice nitrogen content estimation models constructed by machine learning algorithms were compared. The results show that the model constructed by the PSO-DF algorithm outperforms the other models， and its R2 and RMSE indexes are significantly better than those of the other models.

Leaf Area Index（LAI） is an important indicator to reflect the growth state of crops， which is usually estimated by vegetation index. Traditional inversion models are mostly based on multivariate regression models， while the potential of multivariate regression models based on bivariates in LAI inversion has not been fully explored. By extracting the spectral features and texture features of satellite images， the correlation between each remote sensing feature and winter wheat LAI was analyzed based on Pearson correlation coefficient. Using Simple Regression model （SR）， Multiple Linear Regression model （MLR） and Random Forest Regression model （RFR）， the relationship between remote sensing characteristics and LAI of winter wheat was studied. The inversion accuracy of each inversion model was determined by the accuracy index （determination coefficient R2， root mean square error RMSE， relative root mean square error rRMSE）. Based on the above evaluation indicators， the optimal inversion model was proposed. The results showed： （1） All vegetation indexes and some texture indexes have achieved good inversion results in LAI inversion （R2>0.6）. Among them， the Universal Normalized Vegetation Index performed the best among all vegetation indices （R2=0.754，RMSE=0.606，rRMSE=12.99%）. Except for the mean feature inversion accuracy of some bands that is comparable to vegetation index， the accuracy of most texture feature inversion for the winter wheat LAI is poor； （2） The bivariate multivariate linear regression model with the highest LAI inversion accuracy for winter wheat was obtained through bivariate combination （R2=0.780，RMSE=0.573，rRMSE=12.29%）； （3）In the case of multiple input variables （at least 3 feature variables）， RFR performed better than MLR. Compared to texture features， the inversion performance of texture indices was better. The research results can provide a new approach and method for monitoring large-scale crop LAI based on satellite imagery in the future.

Rice-fish co-culture， as a model of modern ecological cycle agricultural， with significant social， economic， and ecological benefits on ensuring stable food production， reducing pollution， improving soil fertility， and lowering CH4 emissions. Therefore， obtaining information on distribution and area of rice-fish fields by using remote sensing technology， is helpful in enhancing the level of agricultural digital management and improving the efficiency of resource utilization efficiency. In this study， we selected the typical rice-crayfish model in the Chengdu Plain for remote sensing identification. First， the time-series data of Sentinel-1 VH polarization backscatter coefficients from 2019~2021 were collected and preprocessed in the Google Earth Engine， to reduce the noise of SAR time-series data. Then the time-series characteristics of typical ground objects were analyzed， including rice-crayfish fields， paddy fields， lotus root fields， orchards， traditional aquaculture， etc， and the characteristic parameters statistical of the backscatter coefficients time-series were statistically analyzed. Finally， the information of rice- crayfish fields， rice fields and lotus root fields were extracted by the classification method of random forest. The results showed that the backscattering coefficients of rice-crayfish fields exhibited typical time-series variation characteristics. Specifically， the annual variation trend of backscattering coefficients began with a smooth transition at low value， then increased rapidly， and finally decreased sharply to low value， due to the state of rice-crayfish fields changed from water body to vegetation and then back to water body. Moreover， the range of coefficient variation and the time of curve peak were significantly different from paddy fields and lotus root fields， respectively. The overall accuracy and Kappa coefficient based on random forest classification were 94.32% and 0.91， respectively. This suggested that time-series data of Sentinel-1 can effectively identify rice-crayfish fields in cloudy regions. The results can provide a reference for remote sensing identification of rice-crayfish fields in cloudy areas.

Remote sensing technology has become an important way to obtain agricultural greenhouse coverage information quickly and effectively. But the spatial resolution size of remote sensing images has a dual influence on the extraction accuracy， and it is important to select suitable resolution images. Taking the southern agricultural plastic greenhouses as the research object， GF-1， GF-2 and Sentinel-2 are used to form six different spatial resolution image datasets between 1 and 16 m. Based on Object-Based Image Analysis （OBIA）， we use the Convolutional Neural Network （CNN） and Random Forest （RF） methods to extract the canopy and analyze the extraction accuracy and the difference between the methods. The results show that： （1） the extraction accuracy of agricultural greenhouses under CNN and RF methods generally decreases as the image resolution decreases， and agricultural sheds can be detected on images from 1m to 16 m； （2） the CNN method requires higher spatial resolution than the RF method， and the CNN method has fewer missed and false extractions at 1~2 m resolution， but at 4 m and lower resolutions， the RF method is more applicable； （3） the 2 m resolution image is the best spatial resolution for shed information extraction， which can realize shed monitoring economically and effectively.

The apple cultivation industry is a pivotal sector for elevating agriculture and enriching farmers in Shaanxi Province. The apple-producing regions on the Loess Plateau face annual demands for blossom period frost protection and yield assessment. Blossom period monitoring is particularly crucial， and conventional methods suffer from high monitoring costs and low accuracy. This study introduces a method based on reconstructing crop reference curves using remote sensing. It compares the accuracy of this method with others applied to common crops. Subsequently， a set of NDVI time series is extracted from MODIS pure pixels identified by geographic national survey land cover classification data. These time series are then used to fit observations similar to Sentinel-2. The method is applied to apple crops in the Loess Plateau region. Based on the spatial phenological differences between the Normalized Difference Vegetation Index （NDVI） sequences of apple sample points （crop reference curve） and the reconstructed apple NDVI sequences， historical high-resolution onset blossom period monitoring results at 10 meters are generated， achieving the transformation of blossom period monitoring from point to area. The research results indicate that the detection results for the onset blossom period of apples in the validation region for the years 2019~2021 have an average absolute error within 1~3 days compared to ground data. The average absolute error is 0.926 days， and the root mean square error is 1.503 days. This method can be used for high-precision monitoring of the apple blossom period， and the predicted results can serve as a reference for frost protection and yield assessment in actual apple production. Additionally， this method can be applied to study the phenological characteristics of other crops such as wheat and maize， promising more accurate decision support for agricultural production.

The extreme drought disaster in Jiangxi Province in 2022 severely affected the growth and yield of citrus. It is of great significance to use remote sensing technology to assess the degree of drought damage quickly and accurately for reducing losses of citrus planting and subsequent stabilize yield. The citrus planting areas of Jiangxi Province were identified and extracted by using the 2022 Landsat remote sensing image data， the land use type data of Jiangxi Province， and the Google Earth citrus supervised classification sample point data. On this basis， the Normalized Difference Vegetation Index （NDVI） and Land Surface Temperature（LST） of the key growth period （June-October） of citrus in Jiangxi Province were calculated by using the MODIS data products of 2021 and 2022， which used for joint inversion of Temperature-Vegetation Drought Index（TVDI）. Combined with the citrus planting area data of Jiangxi Province Statistical Yearbook and field survey data， the economic losses caused by extreme drought in 2022 in Jiangxi Province were quantitatively assessed under four cases. The results showed as follows： （1） The average TVDI of citrus planting areas in Jiangxi Province from June to October in 2021 and 2022 were 0.83 and 0.62， respectively， and drought stress increased significantly in 2022； （2） Severe drought accounted for 66.1% and moderate drought accounted for 33.7% in the citrus planting area of Jiangxi Province from June to October 2022， and the spatial distribution of drought was more severe in northern Jiangxi Province than in southern Jiangxi Province. （3） From early July to early November 2022， TVDI of citrus planting areas in Jiangxi Province remained above 0.8 for a long time， which was characterized by severe drought. This period coincided with the key growth period of citrus and had a great impact on citrus growth. （4） In 2022， the average reduction rate of citrus yield in Jiangxi Province reached 58.2%， and the economic loss of citrus planting showed an increasing trend from north to south. The direct economic loss of southern， central， and northern Jiangxi Province were 4.964 billion yuan， 4.517 billion yuan， and 1.984 billion yuan， respectively. The research results are helpful for the government to quickly find out the disaster situation of citrus farmers in Jiangxi Province， and provide a certain basis for the decision of citrus planting drought relief， loss reduction， and yield guarantee of citrus planting in the future.

Using remote sensing phenology extraction method has great potential in understanding the sensitivity of farmland phenology to climate change.Used a satellite-derived Normalized Difference Vegetation Index （NDVI） to obtain the spatio-temporal patterns of the farmland phenology in NEC from 2005 to 2020 and validated the results using ground phenology observations. Then explored the relationships among farmland phenology， temperature， precipitation and sunshine hours for relevant periods. The results showed that the spatial distribution pattern of farmland phenology was consistent in Northeast China from 2005 to 2020， and only some areas were different due to the distribution of crop varieties. The temporal variation of farmland phenology was significant.In most regions of NEC， the start date of farmland phenology had advanced by approximately 1 d/a， and the length of vegetation phenology had been prolonged by approximately 1 d/a due to the warm conditions. Farmland phenology was significantly affected by temperature and precipitation， but not by sunshine hours， and was also affected by the change of planting structure to a certain extent.. Moreover， farmland phenology is largely affected by human factors， so some regional climatic factors show opposite trends.

Rapid and accurate extraction of crop type， spatial and temporal distribution is of great significance for agricultural structure adjustment and national food security. However， there are few optical remote sensing image of cloudy areas， thus crop monitoring is limited. To make up this shortage， spectral signature of winter crops and SAR time series characteristics of summer crops were proposed based on the Sentinel-2 and Sentinel-1 data for high-accuracy crop mapping. The Guanghan County， an important grain-producing region in southwest China， was studied. The object-oriented decision tree classification method was explored for spatial and temporal distribution extraction of crops in study area， and the classification accuracy was verified. The results shows that： （1） the main crops in Guanghan County are grain and oil crops， and the major crop rotation patterns are wheat-rice， rape-rice， potato-soybean and potato-corn； （2）the SAR time series characteristics of rice， soybean， corn show clear differences， extracting the types and distribution of winter-summer crops based on the optical-SAR remote sensing images provides a new idea for crops monitoring by remote sensing images in cloudy areas. （3） The overall accuracy and Kappa coefficient of object-oriented method reach 85.49% and 0.81， which can maintain the integrity of large area crops， and avoid salt and pepper noise.

As an important part of the intertidal ecosystem， tidal flats have unique environmental regulation service functions and ecological benefits such as maintaining coastline stability， accelerating material exchange and promoting carbon cycle. Accurate and timely assessment of the status of intertidal wetlands is essential to achieving sustainable management goals. With the help of Google Earth Engine （GEE） cloud computing platform， this paper uses the 2020 Sentinel-2 dense time series remote sensing images， integrates the Maximum Spectral Index Composite algorithm （MSIC） and the Otsu algorithm （Otsu） to construct a multi-layer decision tree classification model， so as to realize the rapid and automatic extraction of Australian intertidal tidal tidal flats. After vectorization， the spatial distribution dataset of high-resolution intertidal flats in Australia in 2020 was obtained， and the extracted tidal flats area was 10 708.22 km2， with an overall accuracy of 95.32% and a Kappa coefficient of 0.94. The dataset is stored in.shp format， with a temporal resolution of years， a spatial resolution of 10 m， and a data volume of 154 m. This data is suitable for coastline management， marine ecological research， environmental protection and monitoring， etc. The data can promote and manage coastal ecosystems， such as mangrove afforestation and control of alien species invasion such as Spartina alterniflora， and can also be used as basic data for scientific research， such as biodiversity， carbon storage estimation and sea level rise caused by sea level erosion etc.

Data-driven modeling is to discover the spatio-temporal evolution of state variables from data. Data-driven data assimilation is a scientific method to optimize the fusion of observation information and model by using data-driven model instead of traditional （physics-based） model. In this work， a data-driven support vector machine regression prediction model is applied to the ensemble Kalman filtering process，and the dynamic system is reconstructed from the sample set by non-parametric sampling of the dynamic system trajectory using the simulation prediction method. A data driven data assimilation method based on support vector machine regression machine learning simulation prediction strategy is proposed and applied to classical pattern driven data assimilation system. The Lorenz-63 and Lorenz-96 model are used for numerical experiments. The data assimilation performance is compared by changing the sensitivity parameters such as sample sizes，noise variance and observation step sizes. The results show that the proposed method is superior to the general sequential data assimilation method for large sample sets，which proves the effectiveness of the new method.

Aiming at the problem of poor image classification accuracy caused by low signal-to-noise ratio of hyperspectral images， a hyperspectral image classification method that combines multi-scale low-rank representation and two way recursive filtering is proposed. First， perform superpixel segmentation algorithm on hyperspectral images at different scales to obtain the spatial neighborhood information and segmented images. Next， low-rank representation and PCA（Principal Component Analysis） dimensionality reduction are performed in the segmented regions of each scale， the low-rank representation can impose low-rank constraints on the high correlation between spectra in the segmented regions and remove mixed noise. Then， two way recursive filtering is used to further eliminate noise in the image. Last， according to the classification results of the feature images of each scale by the Support Vector Machine， the final classification is obtained by the majority voting method. The results showed that： Compared with the classification methods using only spectral information （Support Vector Machine and PCA）， the overall accuracy of the proposed method is improved by 32.03%， 28.04% and 16.80% on average. Compared with the deep learning classification method of spatial-spectral residual network and vertex component analysis network， the average improvement is 10.99%， 8.45% and 7.08%. Compared with other spatial-spectral classification methods， the average improvement is 8.28%， 18.77% and 10.19%， it is proved that the proposed method can achieve better overall classification accuracy with fewer training samples.

As one of the main targets in cities， the extraction of buildings is of great importance， and using segmentation methods to separate buildings from the background is the basis for subsequent information extraction. However， traditional Markov Random Field （MRF） model only uses gray information when segmenting buildings in Synthetic Aperture Radar （SAR） images， so the segmentation integrity of gray inhomogeneous targets is poor， and the interrelationship between the two parts of random field energy is not considered， which leads to the results cannot balance regional consistency and edge detail. In order to solve these problems， an improved MRF model for building segmentation in SAR images is proposed. On the one hand， by introducing texture features weighted by the Bhattacharyya Distance into the observed random field， a complete extraction of gray inhomogeneous buildings is achieved； on the other hand， by introducing weights that vary with the number of iterations in the two parts of random field energy， a better noise suppression is achieved while keeping the edges smooth in the dense region， and finally more accurate building segmentation results are obtained. In order to verify the effectiveness and practicability of the proposed algorithm， real SAR images are selected for testing， and the results show that compared with current algorithms， the proposed algorithm has better classification accuracy and Dice coefficient.

The rapid development of Unmanned Aerial Vehicle （UAV） technology provides new methods for geographical research and promotes geographical research into a new stage of development. To understand the application trend of drones in geographical research， 3 911 papers from 2002~2021 were analyzed by bibliometric methods using the relevant literature on the application of drones in geography in the Web of Science core collection as the data source. The results show that the literature on UAV applications in geography has shown a rapid growth trend since 2012， with an annual average growth rate of 54.7%. China and USA dominate the applications of UAV in geographical research. The keyword clustering results reflect the research systems of surveying and cartography， geomorphology， GIS， ecogeography and natural disaster science. Surveying and cartography is currently the most used field for UAV， while GIS and ecogeography have the fastest growth rate. The number of articles issued in the fields of geographic information system and ecological geography has the fastest growth rate， with 73% and 69% growth rate respectively. At present， "UAV+Deep learning" is becoming a major trend in the combination of UAV and geographical research. As scholars strengthen cooperation and communication， more technical and theoretical innovations will emerge from UAV remote sensing to further promote its application in the field of geography.

Coal resources play a very important role in the national economy， and coal mining will cause many environmental problems， restricting the green and sustainable development of mining areas.The spatio-temporal dynamic monitoring of landscape pattern and ecological environment in mining area can provide decision support for ecological environment governance in mining area， alleviate the contradiction between man and nature， and guide the coordinated development of ecological environment in mining areas.This paper takes Yineng Mining area in Wenshang County， Jining City， Shandong Province as the study area. Based on Landsat images from 2000 to 2021， landscape classification methods， landscape pattern index method， Remote Sensing Ecological Index method （RSEI） and migration of the center of gravity model， the landscape pattern and ecological environment change of Yineng mining area were comprehensively analyzed.The results showed that from 2000 to 2021， the maximum patch index decreased， while the landscape fragmentation and shape index increased. Coal mining had a great impact on the original landscape pattern in Yineng mining area.The ecological environment quality showed a downward trend， upward trend and downward trend.The indexes of greenness and dryness have great influence on ecological environment. The difference grade and excellent grade of Yineng mining area have a large migration range.

Atmospheric PM2.5 is one of the primary pollutants affecting air quality. Therefore， how to effectively monitor and manage PM2.5 concentrations is of great significance to the sustainable development of ecological quality in China. Based on a series of auxiliary parameters， i.e.， Top-of-Atmospheric reflectance （derived from remote sensing imageries）， meteorology， and land use， a Random Forest （RF） model was developed to estimate ground-level PM2.5 concentrations in the contiguous Yangtze River Delta-Fujian （YRD-FJ） region located in East China in 2016， 2018 and 2020. The correlation between the spatial distribution of PM2.5 concentrations and landscape patterns in YRD-FJ region using 3-period land classification data was carried out. The results show that （1） the R2 between the PM2.5 concentrations estimated by the RF model and the ground-level measured values in YRD-FJ region in 2016， 2018， and 2020 are 0.91， 0.89， and 0.90， respectively； the RMSE are 9.07、10.19 and 8.03 μg/m3， respectively. （2） The annual average PM2.5 concentrations in YRD-FJ region showed a trend of year-on-year decrease from 2016 to 2020， and its spatial distribution was generally in the pattern of "Jiangsu > Shanghai > Zhejiang > Fujian". （3） Reasonable control of the landscape proportion of cropland， built-up land and water bodies， and reduction of their landscape dominance and edge density are conducive to alleviating the annual average PM2.5 concentrations. Additionally， appropriate increase in forest occupancy， edge density， and shape complexity are beneficial to reducing PM2.5 concentrations. Our results could provide the scientific basis and decision-making reference for the control of regional air pollution and landscape pattern planning.

Rivers play an important part of the earth's water cycle， and it is significant to extract mountain river information accurately for water resource evaluation and ecological restoration. According to Sentinel-2 images from 2019 to 2021， and the multi-spectral index method was used to distinguish rivers， lakes and glaciers automatically by combining with the Random Forest （RF）， and MERIT DEM was used as the terrain condition to extract the multi-temporal and high-resolution river automatically. Combined with the second glacier inventory dataset of China， the catchment of different recharge types were divided， the percentage change in river area and width was further calculated to describe the seasonal changing rate. The results showed that the average area of the river in the wet season and the dry season has reached 7 161.64 ± 22.73 km2 and 4 066.02 ± 35.19 km2 during 2019~2021， respectively， and both the Kappa coefficient and the overall accuracy are larger than 0.8. The average seasonal changing rates of river areas of glacier-fed types and non-glacier-fed were 0.34 and 0.23， respectively. The seasonal variation of the average width of glacial-fed rivers was mostly larger than that of non-glacial-fed rivers. The results showed that the changes in width and area of glacier-fed rivers are much larger than that of non-glacier-fed rivers， suggested that glacial meltwater has an important impact on the changes of river runoff.

Coupled with the physical environment of the city and human social activities， the study of the spatial and temporal evolution characteristics of the city's spatial structure can clarify the current needs of urban development and provide references for the layout of territorial spatial planning. Based on multi-source geographic big data， the spatio-temporal evolution characteristics of urban spatial structure are portrayed in terms of both hierarchical structure and circle distribution from the perspective of coordination of urban static-dynamic system， taking the main urban area of Wuhan city as an example， using night light remote sensing data， POI data， land use data and road data， and drawing on the concept of coupled coordination degree. The results show that： （1） in terms of temporal changes， the spatial structure tends to be perfect in the main urban area of Wuhan between 2010 and 2020， and the scope and number of urban centers also appeared to extend and increase. （2） The distribution of advantages and disadvantages of spatial structure is unbalanced in the main city state for ten years， forming an overall pattern with the city center and gradually decreasing along the circle gradient towards the periphery from the perspective of space. （3） The portrayal of characteristics of urban spatial structure coulping urban static-dynamic system based on multi-source geographic big data is highly consistent with the actual development of the city， which is helpful to deepen the understanding of urban spatial structure and provides a reference for urban planning.

Road ecosystem monitoring is the foundation and key link for understanding the quality status and causes of changes in road ecosystems， and is of great significance for road ecological environment protection and sustainable development. Due to its advantages such as large-scale， multi-temporal， high-precision， and comprehensiveness， remote sensing technology has gradually become the main means of road ecological monitoring. This article reviews the domestic and international research progress from three aspects of remote sensing monitoring with landscape pattern， vegetation status and species diversity monitoring， summarizes research trends， points out the main problems and challenges faced by current research， and proposes future development prospects. Research has shown that （1） remote sensing monitoring of road ecosystems covers a wide range of aspects， with rich research results. Different remote sensing monitoring methods have their own advantages and disadvantages in application. （2） At present， remote sensing monitoring of road ecosystems still faces many challenges， such as insufficient monitoring during road construction， weak targeting of monitoring and evaluation indicators， limited remote sensing monitoring based on road characteristics， and unclear delineation of road boundaries. Therefore， future research should establish multiple indicators from a multidimensional perspective to evaluate the ecological status of the road construction period. Based on the linear engineering characteristics of land transportation facilities， targeted monitoring technologies should be developed to improve monitoring efficiency and real-time performance.

Shanxi Province is one of the most important ecologically fragile areas in China. Scientific assessment of ecological vulnerability and its driving forces is an important basis for formulating ecological protection and improving ecological environment. However， previous studies on ecological vulnerability in Shanxi Province were often based on administrative boundaries， and there was nearly no grid scale to study the different characteristics and driving forces of ecological vulnerability of Shanxi Province. In this paper， remote sensing and GIS technique were used to evaluate the different characteristics and driving forces of ecological vulnerability in Shanxi Province from 2000 to 2019， combined with PSR model， Spatial Principal Component Analysis method and Geographically Weighted Regression method. The results show that the main ecological vulnerability of Shanxi Province is moderate， the ecological vulnerability of the central basin and the western loess Plateau of Shanxi is very poor， and the ecological vulnerability of the "Duo-shape" mountains is better. Considering the distribution of ecological vulnerability of different land cover， grassland， water area and cultivated land are dominated by moderate ecological vulnerability， forest land is mainly covered by mild ecological vulnerability， and construction land and unused land are most influenced by severe ecological vulnerability. The overall migration of ecological vulnerability gravity center is going to the south. The order of influencing factors on ecological vulnerability are population density>GDP>biodiversity abundance>NDVI>SHDI> aspect， respectively. According to the distribution and change characteristics of ecological vulnerability， Shanxi Province is divided into Core areas of ecological protection， ecological comprehensive concern areas， ecological optimal-concern areas， ecological restoration management areas， and ecological potential management areas， and the corresponding strategies are optimized for protection ecological vulnerability.

Aiming at the problem that dark feature information such as water bodies affects the accuracy of terrain shadow extraction in mountainous areas， this paper proposes a terrain shadow extraction method based on the first principal component features and spectral features of ground objects. Firstly， the spectral features and the first principal component features of four typical ground features including topographic shadows were analyzed， and the shadow component （PCA1） and the water component （NDMBWI） were established to construct the Normalized Shadow Index （NSI）. Then， the dynamic threshold was constructed by analyzing the two-dimensional spatial distribution between NSI and NDVI. Finally， the image information is segmented to obtain the terrain shadow area. The test results show that： （1） Compared with other methods， the dynamic threshold method based on NSI has the highest overall accuracy and Kappa coefficient （about 0.893 and 0.759）. The three statistics （Range， Standard Deviation， and Coefficient of Variation） of the reflectance in the shadow area are the lower， indicating that the method can effectively remove the influence of water and other dark ground objects， and accurately extract the shadow； （2） The dynamic threshold method based on NSI can extract topographic shadows in different phases and different study areas with good results. The topographic shadows are highly distinguishable from water bodies， dark features and buildings， and can suppress the influence of cloud shadows to a certain extent. The algorithm has good stability and applicability.

The high spatial and temporal resolution Sentinel-2 images are increasingly becoming the primary remote sensing data source for surface water extraction.A comparative study of the extraction effects of various water index methods based on this satellite image is a significant reference value for improving surface water’s remote sensing monitoring capability. In this study， the seven water indexes （NDWI， MNDWI， AWEInsh， AWEIsh， WI2015， CDWI and MNDWI_VIs） are used to extract surface water from four sample areas with different combinations of surface water types in North China， Northeast China， the middle and lower reaches of the Yangtze River and Northwest China.The water indexes’ accuracy is quantified using Sentinel-2 MSI images on the GEE （Google Earth Engine） platform. The results show that， all seven water indexes generally can identify surface water well， but there are some differences in performance when extracting different types of surface water bodies； the NDWI index underestimate the distribution of surface water in transient water bodies （e.g.， paddy fields， floodplains， etc.） and have a high miss-score speed； while the AWEInsh， AWEIsh and WI2015 indexes have an overall tendency to overestimate and have a high miss-score rate； the MNDWI_VIs water index maintains the highest extraction accuracy in areas with complex water index； in the field of monitoring water changes in long time series， the performance of the seven water bodies is generally consistent with the conclusions obtained based on single-view imagery. This study provides an essential scientific basis for carrying out surface water monitoring in different water bodies.

The distribution of local surface heat flux in mountainous cities is quite different from that in plain cities because of complex terrain and changing climate. In order to explore the spatio-temporal evolution law of surface heat flux during the urbanization process in new mountainous development city， the spatio-temporal evolution law of heat balance process of different land use types and heat flux process before， during and after urbanization in Yuelai New City were analyzed by satellite remote sensing image data， LUMPS and SEBAL model， the effect of land use/vegetation cover on surface heat flux was also discussed. The results show that， （1） the net radiation flux and difference of various land use types in Yuelai New City reached the maximum in July and the minimum in January， vegetation coverage was one of the factors affectied sensible heat flux of different land use types. the order of latent heat flux was forest land > farmland > unused land > residential land， and the order of soil heat flux was unused land > residential land > forest land > farmland. （2）The urbanization process increased the area of low net radiation value in Yuelai New City， and sensible heat flux showed an increasing trend and accounted for the largest proportion in the energy output. The low value area of latent heat flux gradually expanded to the north and south. Soil heat flux and sensible heat flux were higher in the area with low latent heat flux. The distribution rules of soil heat flux and sensible heat were basically consistent， and both showed an increasing trend. （3）The correlation between soil heat flux and land use area was the best among the energy output factors. FVC had a greater impact on heat fluxes than land use area. Residential combined land was most closely related to FVC， and latent heat flux was most affected by FVC.