Snow cover， snow depth/snow water equivalent， surface soil frozen/thaw state and soil moisture are the key variables in the three cycles including energy， water and carbon cycles. In order to better understand the remote sensing techniques of above parameters， this paper presents a comprehensive review of the progress in remote sensing of snow， soil frozen/thaw state and soil moisture， including the methods and theories of snow cover， snow depth / snow water equivalent， surface soil frozen/thaw and soil moisture remote sensing monitoring from visible， microwave techniques and the integration of multi-sources of remote sensing. The research progress of these parameters is summarized， and the prospects of these parameters are also discussed. The capability of snow， surface soil frozen/thaw state and soil moisture with remote sensing has been demonstrated to be improved greatly due to the retrieval algorithms development based on from single-sensor to multi-sensor combination， single-band to multi-band integration， especially on the virtual satellites constellation. Long time series data set of these surface parameters about 40~50 years were generated， then these products provide our better understanding on surface response to global climate change， and accelerating the application into the research of hydrology， climate and carbon cycles. This review will be helpful for the application of key parameters retrieval in water cycle with remote sensing.

Deep learning has significantly advanced remote sensing image processing technology， demonstrating notable improvements in both accuracy and speed. However， deep learning models typically require large amounts of manually labeled training samples in practical applications， and their generalization performance is relatively weak. In recent years， the development of visual foundation models and large language models has introduced a new paradigm for research on large models in remote sensing image processing. Remote sensing large models， also known as remote sensing foundation models， have garnered attention for their outstanding transfer performance in downstream tasks. These models are first pretrained on large datasets unrelated to specific tasks and are then fine-tuned to adapt to various downstream applications. Foundation models have already been widely applied in language， vision， and other fields， and their potential in the field of remote sensing is increasingly gaining attention from the academic community. However， there is still a lack of comprehensive surveys and performance comparisons of these models in remote sensing tasks. Due to the inherent differences between natural images and remote sensing images， these differences limit the direct application of foundation models. Against this backdrop， this paper provides a comprehensive review of common foundation models and large models specifically designed for the field of remote sensing from multiple perspectives. It outlines the latest advancements， highlights the challenges faced， and explores potential future directions for development.

Taking Shanghai region as the study areas,by using thermal infrared data of multi-sources polar orbit meteorological satellite,the research selected 264 preprocessed images to couple with the synchronous air temperature data gathered from 52 weather stations and then built two kinds of air temperature retrieval models changed with seasons and times.Results showed that the stable models for retrieving the air temperature could be constructed based on large numbers of samples from multi-sources remote sensed data,and furthermore the retrieval precision could be improved by using a field-measured data to modified the model.

In recent years， in order to improve the classification accuracy of ground objects， break through the technical system of single sensor， and make up for the limitations of single data source application， multi-source remote sensing data fusion has become a research hotspot concerned by many scholars in the field of remote sensing. The fusion technology of optical image and LiDAR point cloud data of hyperspectral remote sensing technology provides a feasible scheme to improve the accuracy of ground object recognition and classification at the technical level， breaks the technical upper limit of single sensor， and provides a new solution for the integrated acquisition of target three-dimensional space-spectral information. At the same time， it lays a foundation for the research of hyperspectral LiDAR imaging technology. This paper reviews the development history of LiDAR and hyperspectral imaging data fusion， discusses the main fusion methods and research progress at the feature level and decision level， introduces the commonly used feature level fusion and decision level fusion methods in detail， summarizes the latest research algorithms and discusses their challenges and future development and application prospects. Finally， the future development of LiDAR and hyperspectral imaging data fusion is prospected systematically.

Mineral resources are important production materials for human survival and development， and the monitoring of mine environment is crucial for mineral resources exploitation and protection. Due to the advantages including large-scale， multi temporal and comprehensive， remote sensing technology has become the main means of mine monitoring. Aiming to the requirements of mine development and utilization， geological disasters， ecological environment monitoring and quality evaluation， we systematically summarized data sources， methods and models used in remote sensing monitoring of mine environment. Especially， data sources adopted in remote sensing monitoring of mine have tended to diversify and involve in all aspects of mine monitoring. Along with the rapid development of cloud computing platform and artificial intelligence technology， methods such as big data analysis and deep learning have gradually played an important role in remote sensing monitoring of mine environment， while multi-source data fusion， intelligent extraction of features， three-dimensional deformation monitoring and quantitative inversion are the main problems and challenges.

As an important complementarity of aerial remote sensing,the UAVRSS(Unmanned Aviation Vehicle Remote Sensing System) has many advantages in specific field.Firstly,the paper  described 4 times aviation experimentations,then based on the experiment images and aviation data of UAV,all images were  mosaicked to a panorama image.And the mosaicked image was analyzed from many factors for evaluating the image quality.Finally,some improvements to the UAV image quality were discussed.

There are kinds of methods for ortho-rectification in application of remote sensing images，including Collinearity Equation Model，Strict Geometric Model based on Affine Transformation，Improved Polynomial Model，Rational Function Model，Method based on Neural Network，and so on.But there is lack of system comparison among these methods.On the basis of introducing the principle of the methods above，advatanges and drawbacks about these algorithms are summarized in this paper.Specific emphasis is the mathematical derivation and algorithm design of FM.Tikhonov method is taken to the progress of computation  of RFM.Two kinds of algorithm based on neural network was taken in application of ortho-rectification.To compare accuracy and effectiveness between the above methods，we make some experiments.The result shows that: on the condition of the same GCPs distribution，Rational Function Model that can reach sub pixel accuracy is the best of all from the viewpoint of precision and can be used in practice in spite of its relatively slower speed.

Air aerosol pollution has become increasingly serious and become the focus of atmospheric research with the development of urban industrialization.Remote sensing technology is widely used in atmospheric research as a means of scientific，rapid and large-scale monitoring.The main content of remote sensing for atmospheric aerosol retrieval including Aerosol Optical Depth (AOD) of aerosol，aerosol concentration aerosol particle size distribution and air pollution are analyzed based on the atmospheric radiation transmission theory.Main progress around of the world in areas of atmospheric aerosol research by remote sensing techniques are introduced，especially the advantages and disadvantages of the different aerosol retrieval algorithms.At last，some existing problems and the trend of remote sensing for atmospheric aerosol retrieval are discussed.

Vegetation phenology information is a key indicator for evaluating climate-vegetation interaction， land coverage， and interannual productivity changes in ecosystems. Traditional phenological monitoring methods are based on visual observation， the monitoring range is limited and requires a lot of manpower and resources. As a new monitoring method in recent years， remote sensing technology has the characteristics of large monitoring range， convenient information acquisition and saving manpower and material resources. Its application has promoted the development of vegetation phenology dynamic monitoring research. Firstly， this paper combs the process of vegetation phenology remote sensing monitoring in recent years， and clarifies the existing remote sensing phenology monitoring system； The remote sensing data sources that can be used to establish vegetation growth curve are summarized， and the application scenarios of different data sources are discussed； The existing curve noise reduction algorithms and application processes are summarized， and the causes of errors in different methods are analyzed； The main vegetation phenology extraction methods are summarized； Finally， the remaining uncertainties in remote sensing monitoring of vegetation phenology， such as data resolution， vegetation phenology stage definition， and monitoring timeliness， were discussed， and the main directions for future research on remote sensing monitoring of vegetation phenology were prospected.

Airborne LIDAR is a new technology that can obtain high\|precision three-dimensional geographical data quickly.Filtering process for LIDAR points cloud data is to separate the non\|ground points from the ground points.Several important useful filtering algorithms for LIDAR points cloud data\|\|mathematical morphology based filtering algorithm,slope based filtering algorithm,TIN based filtering algorithm,pseudo scanning lines based filtering algorithm,etc.have been introduced,discussed and contrasted.their advantages and disadvantages,the improving advice to each filtering algorithms also have been presented in the paper.

Three-dimensional （3D） radiative transfer model can accurately describe the interactions between solar radiation and heterogeneous land surfaces. Recently， it has become an important tool for quantitative remote sensing studies. LESS is a ray-tracing based 3D radiative transfer model， which take full advantage of the forward ray-tracing techniques for simulating radiative budget and backward ray-tracing for simulating large-scale images， which makes it possible to simulate various remote sensing data in a single model. Currently， LESS can simulate multi-angle Bidirectional Reflectance Factor （BRF）， multi-spectral/high-spectral images， fish-eye cameras， upwelling/downwelling shortwave radiation in rugged terrains and layered FPAR， etc. This simulated dataset can be used for validating physical modes， developing parameterized models， as well as training neural networks. This paper presents the fundamentals of LESS and its applications. LESS can be downloaded from www.lessrt.org.

The signal-to-noise ratio (SNR) is one of the most important indices which can be used to evaluate the data quality obtained by a remote sensor.To a great extent,the SNR of an image reflects the SNR of the remote sensor.Several typical methods to estimate the SNR of optical remote sensing imagery are summarized in this paper,and their merit and restrictions are presented.And this paper also performs the comparison and analysis between these methods based on their own principles,from six aspects including the automatic computation,the computing time,the stability,the applicability,the suitable sensor category,and the uniformity of estimating areas.In addition,the paper points out that the comparison and analysis between methods in various specific applications should be done in the future.The study will help to choose a reasonable SNR estimating method aiming at different remote sensors and different types of remote sensing images.

Evapotranspiation retrievals in conditions of rugged terrain and arid/semiarid sparse vegetation are always the hotspot in estimation of regional evapotranspiration from remote sensing images.Concerning with the complex characteristics of the loess hilly-gully region,such as undulating topography,un-unique land use/covers and less vegetated land surface,etc,land surface characteristic parameters and flux was retrieved,and the instantaneous vegetation transpiration and soil evaporation was estimated with the TSEB parallel model by using the Landsat TM data for an experimental study site located on the conjuncture area of Shaanxi and Gansu where the terrain surface is very undulating with less vegetation developed,heavy soil and water loss loess plateau.And then the calculated result was merged into daily evapotranspiration,also the spatial pattern of the daily evapotranspiration was analyzed.Actual evapotranspiration of the study area was calculated with the extra resistance method and Penman-Monteith equation to compare with the TSEB parallel model estimated,which suggested that the TSEB parallel model can be used for the accurate estimation of evapotranspiration in loess hilly-gully region.

To take Wuhu in Anhui province for example.Firstly,the author analysed the water body in Wuhu and the spectral characters of the earth's surface,then,summarized every class separability of the water information and the earth's surface in every band of ASTER Remote Sensing Image,after repeated experiments and analysis constructed the extracting model of water body which based on threshold of bands and relation of spectrum:B2>B3,B1+B6<127,B3+B4<54 and B3<24.In the end,the author compared and evaluated the conclusion of extracting model of water body with the conclusion of unsupervised classification,supervised classification and NDVI.The result of experiment indicated that this method is feasible and more simple,but higher demands were needed in the selected samples of the analysing process of spectral characters.

In recent years,airborne laser bathymetry technology has got a rapid development.It plays an important role in monitoring of offshore area,measuring water depth and surveying underwater topographic and geomorphic features.This paper analyzed the development history of airborne laser bathymetry in the world and discussed the research progress of data processing and applications.This paper studied the problems of airborne laser bathymetry technology in the development and application,and pointed out future development trend and direction in the end.This research will provide detailed reference information for researchers and users of airborne laser bathymetry technology.

The global environment change has become one of the three global environmental issues with the greatest global impact.Lake is of great ecological significance in regional ecosystem study.Dynamic change as one of three main lake environmental problems of lakes has became the key point of the research area.Remote sensing techniques,as scientific tools of rapid investigation and monitoring,have been widely applied in the research of dynamic change in lakes.Main progress around of the world in areas of lake dynamic change research by means of remote sensing techniques are introduced in the aspects of basic principles and remote sensing data sources,remote sensing classification and dynamic change monitoring methods of lake information | especially,the advantages and disadvantages of remote sensing data source and dynamic analysis methods are analyzed deeply.In addition,some existing problems and the development trends of the recent lake dynamic change research are discussed.

 

Chlorophyll-a concentration is an important proxy for defining the tropic status of various bodies of water. Using remote sensing technology to retrieve chlorophyll-a concentration is an effective method for water eutrophication monitoring and a great number of algorithms for chlorophyll-a concentration retrieval are developed. These algorithms have different advantages and ranges of application. Because the optical characteristics vary in different bodies of water， it is hard to achieve desired results if blindly applying algorithms. In order to promote the further development of water quality remote sensing， the theory and data sources of remote sensing inversion are introduced.Then，domestic and foreign algorithms of retrieving chlorophyll-a concentration in water by remote sensing are summarized.The algorithms studied are categorized into six types by their architectural designs， namely： fluorescence peak and maximum peak algorithms， band algorithms， chlorophyll-a index algorithms，artificial intelligence algorithms，algorithm systems based on optical water types and analytical algori-thms.Each algorithm is presented systematically and its characteristics are analyzed.Then，all the aforementioned algorithms are compared regarding their applicable range of chlorophyll-a concentrations as well as water types.The applicability， merits and demerits of each category of algorithms are analyzed and concluded in order to provide reference for environmental and remote sensing researchers.The main conclusions are as follows：①the algorithm applicability for Case II waters is limited. More in-situ observations should be conducted to establish and supplement the database. Similarities and difference of various optical water types should be further studied to establish global algorithm systems based on optical water typologies； ②The combination of UAVs and hyperspectral sensors could provide new thoughts in monitoring inland water quality； ③Machine learning algorithms and mechanism models should be integrated to develop physical constrained models with high accuracy.

The Remote Sensing based Ecological Index （RSEI） has been widely used since its publication and was modified recently. In this paper， the differences between RSEI and the Modified Remote Sensing Ecological Index （MRSEI） are analyzed and compared based on the principle of the principal component analysis and an application case. The results show that the MRSEI index unreasonably adds the second principal component （PC2） and the third principal component （PC3） into the first principal component （PC1）， as PC2 and PC3 have no clear ecological meanings. The addition also reduces the weight of PC1. Therefore， the MRSEI does not improve the original RSEI， but reduces the value of RSEI as the added principal component components can cancel each other. Therefore， the modification made in MRSEI lacks rationality. This paper also analyzes and discusses some issues that users encountered in calculating and applying the RSEI index. The RSEI should be calculated using surface reflectance data rather than the top of atmospheric reflectance data or Digital Numbers （DNs）. Also， the imagery should be acquired in plant growing seasons. When there is large-area open water in study images， the water must be masked in advance. The "1–PC1" procedure can only be performed when the loadings of the greenness and wetness indicators in PC1 have negative signs.

Object detection has always been a hot topic in the field of remote sensing images information extraction， and has a wide range of application prospect in many fields. The development of deep learning in the field of computer vision provides a strong technical support for the extraction of massive remote sensing images， and greatly improves the accuracy and efficiency of object detection in remote sensing images. However， objects in remote sensing images have the characteristics of multiple scales， multiple rotation angles and complex scenes， deep learning technique still faces great difficulties in the application of remote sensing images object detection with limited high-quality labeled samples. According to five aspects of scale invariance， rotation invariance， complex background interference， limited training samples and detection of multi-band data， the existing algorithms of object detection based on deep learning in the field of remote sensing images in recent years are introduced and summarized. In addition， the difficulties and methods of detecting typical objects in remote sensing images are analyzed and summarized， and the common datasets of remote sensing images object detection including optical images and SAR images are also given general introduction. Finally， the future trends of object detection in remote sensing images are analyzed.

Building extraction is one of the most challenging research topics in remote sensing image understanding.It is of great significance in practice to exploit automatic,intelligent,accurate building extraction approaches.This paper firstly outlines the history and recent development of building extraction from remote sensing imagery,and then provides a comprehensive survey of state-of-the-art approaches,to divide them into the bottom\|up (data-driven) methods and top-down (model-driven) methods.Finally,the remaining problems and future development trends are provided for building extraction from high resolution remote sensing imagery.

The surface Evapotranspiration （ET） is an important controlling factor to water cycle and energy transmission in the biosphere， atmosphere and hydrosphere. Satellite provides an unprecedented spatial distribution of ET in the past decades. In this paper，the estimation methods of evapotranspiration using remotely sensed data were summarized，and the existing issues that should be further studied were discussed. In the future research，we should strengthen the improvement of the evapotranspiration regarding scale effect， nighttime ET， the general validation method of different ET products， remotely sensed data in China， the ET products with higher spatial-temporal resolution， and the new ET model using the machine learning methods.

The interaction between vegetation and climate is a complicated process.Many vegetation models were developed to understand the mechanism of interacting between vegetation and climate and assess the effects of climate change on vegetation.Furthermore,vegetation models have progressed from Static Vegetation Models (SVMs) to Dynamic Global Vegetation Models (DGVMs).DGVMs include the dynamic biogeochemical models and dynamic biogeophysical models,which mainly simulate the vegetation physiological processes,the vegetation dynamics,the vegetation phenophase and the nutrient cycling.The widely used DGVMs in the world include LPJ,IBIS,VECODE,TRIFFID and so on.The present research on DGVMs mainly focuses on four points:① the improvement of the accuracy of DGVMs; ② the comparison among different models; ③ the integrated model of DGVMs and Climate Models; ④ the research on the Carbon Cycle Data Assimilation System (CCDAS).

Ulan Buh Desert is one of Chinas major deserts.In recent years its rapid expansion has seriously affected the local ecological security.Desert vegetation is the most important ecological protection barrier in this region.Gaining the knowledge accurately of the distribution of vegetation is important.Calculated NDVI,and integrated principal component analysis combined with Gray Level Co\|occurrence Matrix texture analysis to analysis the ALOS image in the reserch area.Using NDVI and mean texture as the classification indices,the article determined the appropriate threshold range,and abstracted the vegetation information by using the decision tree method.The result shows that the decision tree method could use texture and other auxiliary information effectively,and achieve better classification results compared with traditional classification method.

With complicated natural conditions, multiplicity of crop structure, small and dispersive distribution of parcel, the accuracy of images with moderate and lower resolution can't meet the acquisition of crop yield forecasting. With improvement of new sensors of high resolution, remote sensing imagery of high resolution can provide more abundant information such as texture, hue and so on. However, the current object-oriented classification approaches are not mature, which have too much thresholds to be set and more complicated and difficult to be used commonly. Therefore, combining QuickBird high spatial resolution satellite imagery with the field investigation data as mainly auxiliary information as well as using the pixel-oriented maximum likelihood method, crop planting area was obtained step by step, applying the principle of multi-scale information extraction,a test was set in Mianyang, Sichuan province.The result shows that the accuracy of crop classification is fairly exciting.

Earth observation system and technology realized the global real-time observation and played an increasingly important role in acquiring space-time information of global surface and deep earth which provides condition for environment monitoring and earth system research. Development trend of earth observation system was discussed based on three aspects as high resolution development direction of earth observation satellites and sensors, networking earth observation satellites as well as integrated and coordinative development of earth observing system. At the same time, technology scopes and requirements were analyzed from the aspect of country investment, capabilities of equipments development and their potential application value in other fields, cost of development and operational needs of the time and so on.

Remote sensing has profound implication in ecosystem investigation and research.However,traditional multispectral remote sensing data usually focused on the mass screening.It was difficult to be used to inverse the complicated properties and biochemical parameters of ecosystem for the limitation of spectral resolution.Hyperspectral remote sensing,which measure large numbers of narrow spectral bands,could greatly improve the accuracy and types of input parameters of ecosystem models.In present study,we systemically reviewed the implication of hyperspectral remote sensing in ecosystem processes and properties based on expounding the principle and characteristics of hyperspectral remote sensing,and proposed the potential implication of hyperspectral remote sensing in ecology.

Night\|light data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) has been widely utilized to derive the urban built\|up areas.Four major methods,including Empirical Thresholding,Sudden Change Detection,Statistics\|assisted Thresholding,and TM\|assisted Thresholding,have been proposed to determine the threshold value for separating the illuminated urban areas from the dark background of rural areas in DMSP/OLS night\|light images.This paper makes a comprehensive assessment of those methods through a case study of Shanghai,China.The methods are implemented to extract the urban built\|up area for Shanghai using DMSP/OLS stable light data acquired in 2003.Then,the same threshold values obtained from 2003 DMSP/OLS night\|light data are applied respectively to the 2000 and 2006 DMSP/OLS data,resulting in a significant error in urban built\|up area detection.This analysis result suggests that the threshold value determined for a specific year cannot be extended and transferred to other years.The failure in temporal extensibility of threshold value means that an appropriate threshold value has to be determined for every year when a time series of DMSP/OLS nigh\|light data need to be processed.Therefore,the method that determines threshold value independent of reference data is more suitable for processing time series DMSP/OLS data.The Sudden Change Detection method does not require ancillary reference data and is the best choice of those methods considering the convenience,accuracy,and automated data processing,and it is then adopted to derive the urban built\|up areas of Shanghai from 2000 to 2006.

Applying remote sensing to estimate vegetation biomass has been paid more attention.When using the remote sensing information parameters and other parameters to construct models,researchers often need choose some parameters as independent variables which have obvious influence on biomass.Therefore,identifying the number of independent variables and choosing independent variables play an important role in the reliability and precision of estimation models.From single-variable models,multi\|variables models and nonparametric models,the article reviews on correlation analysis of independent variables in estimation models of vegetation biomass based on remote sensing,which offers some referenced value for choosing the independent variables in estimation models of vegetation biomass.

With the rapid development and large integration of global informatization and industrialization since the 21st century，the Internet of things and cloud\|computing have emerged.The world has entered an era of big data.There are a huge amount geographical and remote sensing data generated every day in the field of geoscience，environmental science and related disciplines.However，the traditional approaches for storing，managing and analyzing massive data on the local platform，which take up lots of resources，time and energy，have been unable to meet the needs of the current researches.Google Earth Engine(GEE) cloud platform is powered by Google’s cloud infrastructure，and it combines a large number of geospatial datasets and satellite imagery，in which the datasets could be processing，analyzing as well as visualizing on a global scale.Meanwhile，it uses Google’s powerful computational capabilities to analyze and process a variety of environmental and social issues including climate change，vegetation degradation，food security and water resource shortages.Firstly，an introduction of GEE cloud platform has been given.Secondly，recent researches that using GEE cloud platform were reviewed.Thirdly，GEE cloud platform and MODIS land cover type data were used to analyze spatio\|temporal changes patterns of major land use and land cover type in Three Gorges Reservoir in the period of 2002~2013.The results indicate the largest changes occurring in forest lands，shrub grasslands and croplands.Finally，after a rough calculation，GEE cloud platform is superior to the traditional approaches in terms of both cost and economic efficiency，improving the overall efficiency by more than 90%.GEE cloud platform could not only provide powerful support to experts in the field of geosciences and remote sensing，but also offer valuable help to researchers in related disciplines.GEE cloud platform is an excellent tool for scientific research in geosciences，environment sciences and related disciplines.

Landslides are powerfully explosive and destructive， and are one of the natural disasters with high frequency in the world， causing serious damage to people's lives and properties. Accurate and rapid extraction of landslides and obtaining the distribution range of landslides after a disaster are extremely important for landslide disaster investigation and hazard assessment. The method of landslide monitoring based on high-resolution satellite remote sensing images is investigated. Firstly， the decoding characteristics of landslides on high-resolution satellite remote sensing images are introduced， while the research progress of landslide extraction methods and accuracy evaluation and analysis methods are discussed， and finally the advantages and shortcomings of current methods are summarized， as well as the development direction of future research. The results show that the deep learning method has greater potential， and the combination of deep learning and other automated interpretation methods should be strengthened in landslide monitoring in the future to solve the influence of sample size on the model results， realize the migrability of the model， and improve its automation.

Earth observing technology was used in civilian fields since the initial part of 1970s.Satellites ground stations are the basic establishments of earth observation system and basic source of space data,and they play the role of public data flat in national economic construction activities,and their development got wide attentions of governments.There 21 ground stations in different countries were investigated in this paper,including America,the Europe,Japan,Canada and Australia,etc.The properties,distribution characteristics,management system,personnel size and data types received of overseas satellites ground stations were analyzed in this paper.The results showed these overseas satellites ground stations had automatization,commercial,marke-oriented,civilian,internationalization,systematization,net-oriented operation characteristics.Future development direction of satellites ground stations was put forward in this paper.

Google Earth Engine （GEE） is a comprehensive application platform that integrates remote sensing image storage and analysis. It can conveniently and quickly call remote sensing images and information extraction. Therefore， GEE has attracted more and more scientific researchers' attention. With the continuous expansion and upgrade of GEE， the system platform has become more and more complex. For ordinary users， it is becoming more and more difficult to quickly understand its architecture and functional algorithms. In response to this problem， this article systematically introduces the technical architecture， data resources， model algorithms and computing resources of GEE， and summarizes the application results of GEE in various fields， hoping to provide GEE users with a quick understanding of the platform Window to help them make better use of the GEE platform to carry out their own application research.

Forest height estimation is one of the hottest research areas of InSAR/PolInSAR technology within its 30 years’ development.Estimation algorithms play an important role in the forest height assessment by InSAR/PolInSAR technologies.This paper systematically reviewed the basic theories，model assumptions and then summarized the limitation and potential of these algorithms applied in forest height estimation，especially performed in regional or global scale.It also deliberated the intrinsic characteristics of these algorithms like DEM difference method，three\|stage inversion process，coherence amplitude method and so on.Analysis showed that the estimation results of DEM difference method had higher accuracy and less influence from forest types and structure.So it had great potential for global and regional forest height assessment，however，it was limited by the requirement of high accuracy DEM data in those area.The result accuracy of algorithms based on PolInSAR depended more on forest types，structures and also the robust of forest scattering models.It had no restriction of DEM and could perform in global and regional scale，but for the forest area with great heterogeneous，model and algorithm suitability and robust need to further studying.Besides，for the poor penetration of single\|baseline InSAR/PolInSAR，we should focus more on multi\|dimension，multi\|baseline technique for InSAR/PolInSAR application development in the future.

New method for automatic geometric rectification based on image matching in remote sensing image is proposed,which can solve the problem of lower-precision and time-consumption in traditional manual geometric rectification.First,the pictures contained ground control points (GCPs) are gained in the remote sensing image which has been processed with basic geometric correction method.Secondly,the accurate matching between remote sensing image and GCPs pictures are found.Then,the outliers of GCPs are detected.Finally,the remote sensing image is precisely corrected by the GCPs.Also in this paper,a new multicriterion image matching algorithm is proposed to detect precise matching.The experimental results demonstrate the robustness and effectiveness of the proposed automatic geometric rectification method.

To generate 3D information from the image sequence of the normal UAV(Unmanned Aerial Vehicle) loading camera,a reconstruction method is proposed.At the first step,the key frame selecting from the sequence,the robust features extracting and corresponding of multi-images of sequence are accomplished.At the second step,two views which are far away enough and sharing lots of correspondences are selected.With the correspondences the RANSAC algorithm estimates the fundamental matrix robust and then the more accurate correspondences with less outliers are gotten,which are called as inliers.Basing on the calibrated camera parameters,the motions R and T of the view is computed by decomposition of the fundamental matrix,and then it is optimized with the correct correspondences.At last,3D reconstruction is realized by the method of triangle communicating.Experiment results show that the algorithm is efficient and it improves the robustness and accuracy of the reconstruction.

Remote sensing technology has been widely used in disaster reduction and response works in earthquakes,floods and typhoons.It plays an important role in stage of warning,monitoring,rescue,assessment and brings us a new way to know the disaster better.Combined with its own characteristics in accessing information,this paper analyzes the advantages of remote sensing technology in disaster reduction.With the detailed description of their data parameters,it introduces the common remote sensing data used in disaster information access and summarizes the application status of them.This paper also discusses the major role of remote sensing in different disaster management phases,such as warning,monitoring,relief,assessment and recovery.After analyzing the data characteristics and its processing methods,it introduces some practical application in different type of disaster.At the end,the application and development trend of remote sensing technology in disaster reduction and response has been discussed.

Tropical forest plays important role in keeping carbon balance by its biomass,which occupies 40% of the world.In order to provide basic data for tropical forest change monitoring,object-oriented classification methods was applied to study tropical forest mapping technology using Ortho-rectified PALSAR images.The study includes multi-scales segmentation,feature extraction and rules establishment and so on.The study results show that the ratio of polarization features (HV/HH) is sensitive for forest and the object-oriented classification methods can identify forest primely,meanwhile the different classification results reflect the changes of forest between 2007 and 2009 clearly. 

The spatiotemporal variation of vegetation coverage is one of the main research fields in Global and Regional eco-environment. Based on the Google Earth Engine cloud platform, using the MODIS-EVI long-term series data of 250 m resolution from 2000 to 2017. The model of dimidiate pixel was applied in estimating the spatiotemporal variations of Fractional vegetation coverage in China since 2000. The spatiotemporal variation characteristics of China's vegetation coverage for nearly 18 years and future trends from the provincial scale also be analyzed. Trend analysis, Detrended Standard Deviation and Hurst index were employed. The results showed that: (1) The rate of variation of vegetation coverage in China since 2000 is 0.09%/a (P<0.01), the average vegetation coverage is 44.63%. The overall spatial distribution pattern shows the characteristics of “south-high and low-lying northwest”, but there is space Heterogeneity; (2) Hainan Province has the highest average vegetation coverage (79%), the lowest in Xinjiang Uygur Autonomous Region (13%), the most significant improvement in vegetation coverage in Shanxi Province (0.4%/a). Tianjin has the largest inter-annual volatility (DSD=0.039), Xinjiang, Tibet and Qinhai province which located in the westernmost of China have the least annual fluctuations in vegetation coverage; (3) The Hurst Index of Vegetation Coverage at National Scale is 0.72, China Future vegetation coverage will continue to improve. The provinces with improved sustainability are basically “T”-type distribution, and the provinces on both sides of the east and west should focus on strengthening the ecological restoration and protection of vegetation to guarantee the sustainability of regional ecological civilization construction.

In July,2008,during the large\|scale synchronous remote sensing experiment of “WATER:Watershed Airborne Telemetry Experiment Research” in Zhangye district,Gansu province,we did the adjacency effect measurement synchronous with satellite at Lanzhou University grass site,with the device designed by ourselves.The adjacency effect correction coefficient was computed using backward least square algorithm.Then it was imported into the actual reflectance transformation equation to correct the adjacency effect of the synchronous ASTER image.The quality of the result image was improved.Moreover,the result image was analyzed compared with that the SHDOM equation.The result showed that,both the reflectance and normalized difference vegetation index of the result image corrected with the adjacency effect correction coefficient had greater differences,meanwhile the spatial autocorrelation between pixels decreased obviously.〖JP〗

Rapid urbanization has changed land surface properties and land-atmosphere interaction,which further may impact on the climate and environment.It is important for urban sustainability to timely and accurate monitor regional or global urbanization process.Currently,urban area in large scale could be derived from satellite images,and DMSP/OLS has already been widely used to detect global urbanization due to its time series.However,its saturation in urban area largely limited its further application.Therefore,an improved urban index (VANUI) was used to detect urbanization process in JING-JIN-JI area from 2001-2012 to examine its fitness in mega-city area,further provide reasonable information for urban planning and urban sustainability.Compared with the traditional night lights index,VANUI is indeed to improve the monitoring ability for urban center and reduce the influence of signal saturation on parameters derivation.The results showed that VANUI could catch the urbanization process that happened in JING-JIN-JI area during last decades.Spatial pattern of urban expansion from 2001-2012 indicated that the urban area of JING-JIN-JI was doubled from 2001-2012,and the fasted region about urbanization happened in Tangshan,Hebei Province.Linear trend was also detected in urban development by VANUI index from 2001-2012,and the most important increasing period showed in 2004-2007 and 2009-2012.