In the context of achieving peak carbon dioxide emissions and carbon neutrality, conducting a remote sensing-based ecological assessment and monitoring analysis is greatly significant for ascertaining the ecological condition in time and formulating scientific and reasonable ecological protection policies. The early remote sensing-based ecological assessment indices, simple and involving complex processes, are difficult to find wide applications. In contrast, the remote sensing ecological index (RSEI), contributing to elevated assessment efficiency, has been extensively used. To gain a deeper understanding of RSEI, this study describes its background, calculation method, and research status and provides a summary of the current issues and regional adjustments. Furthermore, it analyzes the main application directions of RSEI, namely the in-depth analyses of regional ecological assessment and change monitoring. Finally, the study proposes that despite a broad space for RSEI development, it is necessary to conduct research into the spatiotemporal scales of images, storage and batch processing capabilities, model adaption, and intelligentization.

Inland surface water bodies, including rivers, lakes, and reservoirs, are significant freshwater resources for human beings and ecology, and their monitoring and control are greatly significant. Optical remote sensing provides great convenience for the monitoring of surface water resources, proving to be an important means for the information extraction and dynamic monitoring of inland surface water bodies. This study reviews the basic principles, remote sensing data sources, methods, existing issues, and prospects of the information extraction of water bodies. Owing to the unique characteristics of the remote sensing images of inland surface water bodies, their information can be extracted in an accurate, scientific, and effective manner using remote sensing. Multiple remote sensing data resources can be applied to the information extraction, and the optical remote sensing-based extraction methods include the threshold value method, classifier method, object orientation method, and deep learning method. Given that different methods have unique advantages, disadvantages, and applicable conditions, selecting appropriate multi-source data and varying methods based on the conditions of study areas tend to improve the information extraction accuracy. Nevertheless, there still exist some issues in the optical remote sensing-based water body information extraction, such as the balance of spatiotemporal resolution of remote sensing data, the information mining of water body characteristics, the generalization ability of water body models, and the uniformity of criteria for accuracy evaluation.

The Pearl River Delta urban group is a region with rapid economic development; nevertheless, with the economic development, the heat island effect becomes increasingly obvious. Contrast with traditional point surveillance, the thermal infrared remote sensing method can make us understand the spatial distribution of urban heat island more intuitively. In this paper, the authors retrieved the land surface temperature(LST)of the four seasons of the Pearl River Delta region using the split window algorithm based on MODIS images, and divided the urban core and suburban region based on impermeable surface area(ISA), and finally obtained the surface urban heat island intensity. The results show that the Pearl River Delta region has a serious heat island phenomenon, with the most severe season being summer and the weakest season being winter. There is a tendency that the connection of cities has led to the formation of large urban heat island, especially in the two most serious cities, Foshan and Guangzhou. The heat island intensity is negatively correlated with NDVI value and positively correlated with the degree of the city’s economic development. The research results would provide some ecological instructions for urban development planning of the Pearl River Delta region.

Algorithms for identifying convexity-concavity of a simple polygon has a very important application in many fields. The authors analyzed the present popular algorithms for identifying convexity-concavity of a simple polygon such as angling method, left-right-point method, vector-area method, vector-product method, raying method, slopping method and extremity-vertices-order method. A detailed derivation of these algorithms has revealed that these algorithms can all use the formula b=p*m as the expression, and are equivalent to each other in nature; nevertheless, the pole-order method still have some problems to be further studied. Based on an analysis of the computation, the authors hold that theoretically the vector-product method, the slopping method and the raying method could be used effectively in programming.

This paper discuses the necessity of combining remote sensing images with professional maps in respects of the remote sensing imases's application potentialities and its developmental trend as well as the limitations of traditional professional maps, and expounds the guiding ideology and basic demand of making remote sensing professional images, at last, it takes "the satellite imase of trourism plan of Hainan provence"for example to introduce the methods of how to make professional images.

The vegetation optical depth (VOD) serves as a microwave-based method for estimating vegetation water content and biomass. Compared to optical remote sensing, the satellite-based VOD, exhibiting a lower sensitivity to atmospheric disturbances, can measure the characteristics and information of vegetation in various aspects, thus providing an independent and complementary data source for global vegetation monitoring. It has been extensively applied to investigate the effects of global climate and environmental changes on vegetation. Discerning the research advances of VOD application in the dynamic monitoring of global vegetation is critical for VOD’s further development and application. Hence, this study first presented the primary methods for obtaining the VOD through inversion of passive and active microwave data, comparatively analyzing the principal characteristics of various sensor VOD products. Then, this study generalized the current research advances of VOD in the dynamic monitoring of vegetation in terms of vegetation characteristic monitoring (like vegetation water content and biomass), carbon balance analysis, drought monitoring, and phenological analysis. Finally, this study expounded the advantages, limitations, and improvement approaches of VOD products, envisioning the application prospect of VOD in the dynamic monitoring of vegetation.

A comparison with traditional soil moisture monitoring methods shows that the remote sensing method has great superiority. This paper presents a review of the remote sensing methods currently used both in China and abroad for monitoring soil moisture, which include the reflectivity method, the vegetation index method, the surface temperature, temperature-vegetation index method, the crop water stress index method, the thermal inertia method and the microwave method, with a detailed comparative description of the advantages and disadvantages of these methods. Based on summarizing researches on remote sensing monitoring methods for soil water, this paper evaluated the focal points, difficulties and development trend of this research field. It is held that the thermal inertia method and the vegetation temperature index method are relatively mature methods for soil moisture monitoring. With the wide application of geographic information system, the microwave remote sensing will become the key research direction in this field because of its unique advantages.

Snow proves to be both an important factor in characterizing the surface cryosphere and a critical parameter for weather and hydrological phenomena. Employing remote sensing to conduct long-term and large-scale monitoring of snow morphologies and their changes plays a vital role in research into global climate change, investigations into hydrology and water resources, and geological disaster prevention. After decades of development, significant progress has been made in the field of remote sensing-based snow monitoring technology both in China and abroad. Accordingly, the products for remote sensing-based snow monitoring have become increasingly abundant, and the snow-orientated inversion algorithms have been continuously improved. This paper provides a summary of the existing, widely applied products after categorizing them into three types: snow-cover extent (SEC), snow coverage, and snow depth/snow water equivalent (SWE) products. Furthermore, this study organizes the commercialized remote sensing inversion algorithms used in existing, typical SEC and SWE products. The review of advances in the relevant scientific research reveals that, with the constant presence of sensors with high temporal and spatial resolutions in China and abroad and the support of both novel optical and microwave data sources and new technologies, researchers have gradually improved the accuracy of snow-orientated inversion algorithms by optimizing these algorithms based on regional characteristics. This will provide more support for continuously improving remote sensing-based snow monitoring products in the future.

Hyperspectral image is a new kind of remote sensing images with the feature of "combining mapping and spectra into one",thus better expressing the subtle differences on the surface of the material through the continuous spectral curve. Hyperspectral images have a wide range of applications in such aspects as classification,unmixing and target detection. With the continuous development of hyperspectral remote sensing technology,anomaly target detection has become one of the most active direction of research because it doesn't need a priori information. Many anomaly target detection algorithms have been proposed. Based on data available both in China and abroad,this paper summarized the research situation and new progress in anomaly detection algorithms. The author first expounded the essence of hyperspectral anomaly target detection and used the basic theory and then analyzed and summed up some representative anomaly detection algorithms in such aspects as the ideas of algorithm,key technology,advantages and disadvantages. On such a basis, the author summarized and described the evaluation method of anomaly detection and discussed the future development trend of anomaly target detection algorithm, with the purpose of finding new breakthroughs in the study of the algorithm of hyperspectral anomaly target detection.

The coastal terrain of Zhejiang is complex. It is impossible to use the traditional way to measure the tideland heights of many beaches or offshore islands. With UAV remote sensing technology and tidal observation, the tideland height can be measured, and the difficult problems of artificial measurement can be solved, thus greatly improving the efficiency. Firstly, in combination with existing data, the aerial region and tide information are determined.Secondly,the UAV aerial operations and tidal observations are implemented outside synchronization. Finally, the boundary between tide and coast are interpreted and drawn with high-precision remote sensing image data (DOM), the tidal observation data are interpolated to update tideland height. The results show that this technology can quantitatively describe the tideland height information and is hence a new method of tideland height measurement.

The energy of electromagnetic radiation which is accepted by most detectors using as Remote Sensing comes mainly from the reflection of the earth surface to solar radiation energy.This reflection is related to the relative positions between the sun and the earth. Then the solar angles which represent the relative positions is quite important in the research of Remote Sensing.This paper gives a detailed and whole method for calculating the solar angles, such as solar right ascension, solar declination, solar hour angle,solar vertical angle, solar zenith angle, and so on.The main idea for the method depends on the theory and method of celestial mechanics. At first, it is neccessary solving six elements of orbit argument of perigee of mean spring equinox (ω), mean anomaly (M), eccentricity (e), major axis of orbit (α), mean obliquity (i) ,and right ascension of ascending node(Ω), Then mean solar right ascension and mean solar declination are gained by solving Kepler eqduation on the basis of the six elements of orbit. True solar right ascension and true solar declination are computed by correcting nutations and precessions of mean solar right ascension and mean solar declination. The calculating of solar hour angle is that the sidereal time is firstly computed, and then the sidereal time minus solar right ascension is solar hour angle. The formula for computing solar vertical angle (h) is sin (h)=sin (δ)×sin+ cos (δ)×cos×cos (t) given solar declination (δ), solar hour angle (t), and the latitude of earth surface . Finally, ninety degrees minus solar vertical angles solar zenith angle.At last in the paper, there are some examples about the application of these solar angles on the research of Remote Sensing.

Since the availability of global runoff data decrease year by year, the inversion algorithms, as substitutes for the river discharge measured at hydrological stations, have become increasingly important. With the continuous development of satellite remote sensing technology, the methods for estimating river discharge have increased in number. This study systematically summarized the remote sensing-based inversion methods for river discharge, as well as the inversion methods for hydraulic remote sensing elements that are closely related to the estimation of river discharge and the progress made in them. Moreover, this study reviewed the methods, principles, and application status of two types of algorithms based on hydrological models and empirical regression equations and summarized the applicable conditions and shortcomings of different methods. Finally, this study predicted the worldwide development trends of the river discharge inversion based on the satellite remote sensing technology, including ① actively developing the advanced data assimilation technology for satellite remote sensing data; ② integrating new sensor products; ③ optimizing and innovating algorithms.

In this paper, Wuhan City was selected for case study. Land use information obtained from satellite remote sensing TM image in 2000 and 2005 was used as the main data source, and the GIS technology was employed as the data integration analysis platform. An ecological risk index was constructed based on the varieties of land use, and the systematic sampling method was utilized to make it a spatial variable. After the performance of sampling, the semivariagram analysis and block kriging were conducted to compile the map of ecological risk distribution. The results indicate that the spatial distribution of ecological risk became more uneven in the working area. The level of the ecological risk study area was divided into three levels: the majority of the vegetation and the waters belonged to the low ecological risk area, whereas the urban built-up area and its marginal areas belonged to moderate ecological risk and relatively high risk areas. Spatial distribution of areas of various levels experienced certain extent of changes in the five years.

A new approach to the fusion of multifocus images based on wavelet transform is proposed to solve the problem that some parts of the images are blurred because of the different focus points. The images are firstly decomposed by using wavelet transform, and then the low and high frequency coefficients are fused by using different fusion strategies: the low frequency coefficient is fused with a rule weighted average of energy, while the high frequency coefficient is processed with the regional grads. After that the fused image is obtained by inverse wavelet transform. Experiments prove that the fused image obtained by the method has a better subjective visual effect and objective evaluation criteria, thus attaining a better result than other traditional fusion methods.

Timely and accurate detection and statistical analysis of the spatial distributions and time-series variations of water bodies like rivers and lakes holds critical significance and application value. It has become a significant interest in current remote sensing surface observation research. Conventional water body extraction methods rely on empirically designed index models for threshold-based segmentation or classification of water bodies. They are susceptible to shadows of surface features like vegetation and buildings, and physicochemical characteristics like sediment content and saline-alkali concentration in water bodies, thus failing to maintain robustness under different spatio-temporal scales. With the rapid acquisition of massive multi-source and multi-resolution remote sensing images, deep learning algorithms have gradually exhibited prominent advantages in water body extraction, garnering considerable attention both domestically and internationally. Thanks to the powerful learning abilities and flexible convolutional structure design schemes of deep neural network models, researchers have successively proposed various models and learning strategies to enhance the robustness and accuracy of water body extraction. However, there lacks a comprehensive review and problem analysis of research advances in this regard. Therefore, this study summarized the relevant research results published domestically and internationally in recent years, especially the advantages, limitations, and existing problems of different algorithms in the water body extraction from remote sensing images. Moreover, this study proposed suggestions and prospects for the advancement of deep learning-based methods for extracting water bodies from remote sensing images.

Remote sensing is a main method for obtaining large-scale land surface evaportransporation (ET), and the direct result of ET is an instantaneous value estimated at the passing time of satellite. Therefore only the daily evapotranspiration has practical significance. Recently, many ET time scale extrapolation methods have been proposed, such as constant evaporative fraction method, time integration method, sinusoid method, crop coefficient method and canopy resistance method. In order to provide readers with clear outlines about the methods and tell readers what is the proper justification when these methods are used, this paper attempted to summarize and make a comparison of the above 5 common methods based on their principles and characteristics. The results obtained show that each method has its own advantages and disadvantages, and hence researchers should consider features of the study area and the data to assure the best selection. What's more, there is a summarization about the existing difficulties and the research hotspots.

Based on an analysis of remote sensing data sources, the authors classify the methods into five categories, i.e., visual interpretation, ratio of index analysis, correspondence analysis, spatial structure analysis, and object-oriented information extraction, and summarize their characteristics and application fields. The research direction and development trends are also predicted, which include such application fields as hyper-spectral remote sensing data, artificial intelligence, expert systems and non-linear theory.

Texture plays a very important role in image retrieval and classification, and texture feature extraction has been a research hotspot. Most present existing texture extraction algorithms can be only used to calculate texture features of gray image. Texture extraction algorithm for color image is very few. Referring to the analytical method of gray level co-occurrence matrix (GLCM),the authors analyzed the influence law of parameters (direction,distance,grayscale,window size)on GLCM texture features of color image. A color image texture feature extraction method(color GLCM,CGLCM)based on GLCM was realized. Through analyzing the influence law of these parameters on four texture features(ASM(angular second moment),Entropy,Contrast,Correlation),a proper parameter value range was given and the CGLCM method was optimized. The results of comparing CGLCM method with GLCM method show that the four texture features calculated with CGLCM method have better robustness and identification capability. These results can provide reference for image retrieval and classification based on texture information.

On the basis of a detailed discussion on the principle of GB InSAR, the main data processing and analysis stages for estimating deformations starting with the GB InSAR observations are described. This paper gives a review of the main types and development trend of ground-based radar system, the main application domain and some existent problems of GB InSAR, and then summarizes the pros and cons of ground-based and space-borne InSAR for deformation monitoring.

Cloud cover is the main factor affecting the quality of remote sensing image. Cloud detection for remote sensing images is one of the principal problems that must be solved in remote sensing data restoration processing. On the basis of extensive investigation of existing articles, the research status of cloud detection is analyzed, and then a classification and comprehensive overview of cloud detection methods is presented, the cloud detection methods for several kinds of commonly used satellite data are also given. By comparing the cloud detection methods, the existing problems and development trend of cloud detection method are discussed.

POS (Position and Orientation System) provides position and attitude information during aerial photography. There must be at least one reference GPS base station for traditional differential GPS (DGPS) positioning, and the establishment of a GPS station would be a very costly and difficult task in some areas. GPS Precise Point Positioning (PPP) has been advanced as a way to avoid the use of the GPS base station. This paper describes the approaches to the processing of an actual aerial photographic data by using both kinds of GPS positioning methods. The final results of the POS-supported aerial triangulation from PPP are compared with those from DGPS solution. The empirical results suggest that the accuracy of POS-supported aerial triangulation from PPP can satisfy the 1∶2 000 topographic map specifications for aerophotogrammetric office operation. It is feasible to process the POS data of ADS40 without a GPS Base Station by using Precise Point Positioning.

The accuracy of coastline extraction can't be guaranteed by applying a single algorithm,because different types of coasts have different characteristics. The existing researches are mostly focused on the extraction of instantaneous waterline,with the lacking of tidal correction and verification of accuracy. In this paper,the authors presented a method combining coastline extraction with coastal type and tidal correction. MNF rotation,MNDWI,morphology and edge detection were applied to SPOT4 data acquired in Qinhuangdao coastal zone to extract instantaneous waterline. Besides,the coastline was extracted accurately by integrating tidal data to calculate the slope of shoal. Moreover,the verification of the accuracy of coastline extraction was achieved by the GPS data obtained in the same period. The results show that the precision of coastline extraction by the method proposed in this paper is high.

Soil moisture is closely associated with global climate change, the carbon cycle, and the water cycle, as well as agricultural production and ecological conservation and restoration. The detection of soil moisture has shifted from ground survey to remote sensing detection, achieving global- and regional-scale survey and monitoring. Given differences in data spectrum segments, radiative transfer mechanisms, and inversion algorithms, it is necessary to comprehensively analyze the mechanisms, advantages, and limitations of algorithms, with the purpose of laying a foundation for accuracy and algorithm improvement. From the aspects of optical remote sensing, microwave remote sensing, and optic-microwave cooperation, this study systematically analyzed the features and challenges of the following inversion techniques: inversion based on the T_s-VI spatial and T_s-NSSR temporal characteristics of optical remote sensing data, inversion using passive and active microwave data, joint inversion using active and passive microwave data and remote sensing data, and optical-microwave cooperative inversion based on accuracy improvement and spatio-temporal transformation. At present, the joint inversion of soil moisture using multi-source remote sensing data faces the following challenges: ① The data suffer missing and spatio-temporal mismatching; ② Different data sources exhibit varying degrees of surface penetration; ③ The joint inversion model relies on empirical parameters and numerous auxiliary parameters. These challenges can be addressed with the improvement in the satellite monitoring network, the increase in the surface detection depths of data sources, the clarification of the physical mechanisms of joint inversion, and the establishment of spatio-temporal continuous datasets of auxiliary parameters.

Multi-label classification of remote sensing images plays a fundamental role in remote sensing analysis. Parsing given remote sensing images to identify semantic labels can provide a significant technical basis for downstream computer vision tasks. With the continuously improved spatial resolution of remote sensing images, many remote sensing objects with different scales, colors, and shapes are distributed in various zones of images, posing high challenges to the multi-label classification task of remote sensing images. This study focuses on the multi-label classification of images in the field of remote sensing, summarizing and analyzing the frontier research advances in this regard. First of all, this study expounded the problem definition for the multi-label classification task of remote sensing images while generalizing the commonly used multi-label image datasets and model evaluation indicators. Furthermore, by systematically presenting the frontier progress in this field, this study delved into two key tasks in the multi-label classification of remote sensing images: feature extraction of remote sensing images and label feature extraction. Finally, based on the characteristics of remote sensing images, this study analyzed the current challenges of multi-label classification as well as subsequent research orientation.

The target motion information extraction technology described in this paper uses satellite remote sensing to detect ground moving targets and estimate its motion parameters. It is one of the important application directions of remote sensing images and has been widely used in traffic monitoring and military remote sensing. As an excellent tool for the study of large-scale target motion characteristics, the high-resolution optical satellite image has more obvious texture features and richer information. After summarizing the research progress of moving targets in optical satellite imagery, this paper describes the methods of moving target detection and motion parameter estimation according to the process of target motion information extraction from high-resolution optical satellite image. Meanwhile, the principle and ideas of a novel method which is based on sequence panchromatic satellite images to detect moving target are introduced. In the end, based on analyzing the weaknesses of existing target motion information extraction research in data source and algorithm, it is pointed out that the target motion information extraction is developing towards automation, intellectualization and real-time.

In order to evaluate the white roof plan for quantitative alleviation of the urban heat island effect, the authors, adopting Shanghai as a study area and based on remote sensing, obtained the data of roof reflectance. By using Hottel model, the sun sunny hourly irradiance was simulated, and the city's rooftops in the absorption of solar radiation were employed to simulate the process under different values of reflectance. According to the "white roof plan", the authors estimated that the temperature of island intensity in the study area can reduce 1.32 ℃ during the lunch period in summer. In combination with the white roof heat transfer model, the authors also estimated that, in the house with white rooftop in summer, the air conditioning energy efficiency can be up to 12.60%.

Tailings ponds are considerable hazard sources with high potential energy. Ascertaining the number and distribution of tailings ponds in a timely manner through rapid identification and monitoring of their spatial extents is critical for the environmental supervision and governance of tailings ponds in China. Due to the lack of pertinence for potential targets, identifying tailings ponds based on solely remote sensing images is prone to produce confusion between tailings ponds and exposed surfaces, resulting in significant errors in practical applications. This study proposed an extraction method for tailings ponds, which integrated enterprise directory, multi-source geographic data (e.g., data from spatial distribution points, digital elevation model (DEM), and road networks), and high-resolution remote sensing images. The application of this method in Gejiu City, Yunnan Province indicates that integrating multi-source geographic data can effectively exclude the interferential areas without tailings ponds, with the precision and recall rates of the extraction results reaching 83.9% and 72.4%, respectively. The method proposed in this study boasts significant application prospects in high-frequency and automated monitoring of tailings ponds nationwide.

The accurate acquisition of land cover/use changes and their types is critical to territorial space planning, ecological environment monitoring, and disaster assessment. However, most current studies on the change detection focus on binary change detection. This study proposed a multi-class change detection method using a multi-task Siamese network of remote sensing images. First, an object-oriented unsupervised change detection method was employed to select areas that were most/least prone to change in the new and old temporal images. These areas were used as samples for the multi-task Siamese network. Subsequently, the multi-task Siamese network model was used to learn and predict the new and old temporal land-use maps and binary change maps. Finally, the final multi-class change detection results were derived from these maps. The multi-task Siamese network was tested based on the images from the Third National Land Survey and corresponding land-use maps. The results demonstrate that the method proposed in this study is applicable to the change detection cases where changed and unchanged samples lack but there are available historical thematic maps.

Differential SAR interferometry has proved to be of remarkable potential for large-scale deformation

monitoring. However, a full operational capability has not yet been achieved due to atmospheric disturbance and

phase decorrelation phenomena. This paper deals with the main techniques for the application of InSAR to surface

deformation monitoring. Such techniques as the conventional SAR interferometry, Stacking Interferograms, Permanent

Scatterers Inteferometry and Corner reflector InSAR are presented in this paper， with their advantages and

limitations analyzed separately. The method for differentiating the atmosphere disturbance phase from the

deformation phase and the algorithm for identifying coherent point and spatial phase unwrapping based on Dealaunay

triangle network are discussed. From the viewpoint of practical application, the accuracy, precision and

reliability of D-InSAR measurement are analyzed. The key problems in the spatial integration processing of

multiple SAR images and the phase regression in the temporal domain of large coverage deformation mapping are also

studied.

This study aims to provide a guide for the optimal management of land use and ecological environment in the Bayin River basin or similar areas by revealing the comprehensive evolution characteristics of the ecological environment in the Bayin River basin. Based on the 12 scenes of remote sensing image data from 2005 to 2020, this study quantitatively explored the critical factors influencing the ecological environment in the study area using a geographical detector. By combining the model for integrated valuation of ecosystem services and trade-offs (InVEST), this study established an ecological environment quality assessment model through statistical analysis, overlay analysis, and analytic hierarchy process, revealing the comprehensive evolutionary characteristics of the ecological environment in the basin. The results show that: ① The critical factors influencing the ecological environment quality in the basin included population size, GDP, elevation, and rainfall. The comprehensive assessment value of the ecological environmental quality in the basin increased from 0.455 to 0.533, suggesting an overall upward trend; ② The ecological environmental quality in the basin exhibited significant regional differences. Specifically, 14.9% of the basin manifested degraded ecological environmental quality, primarily distributed in the vicinity of the Bayin River basin and the surrounding area of Delingha City. In contrast, 33.6% displayed improved ecological environmental quality, spreading in areas to the south of lakes in the middle and lower reaches of the Bayin River basin. This study indicates that the future ecological environment protection and planning in the Bayin River basin should focus on the balance between agricultural land and other ecological and construction land during urbanization, thereby achieving coordinated development of economy and ecology through scientific planning of spatial framework.

Oil spill is one of the main sources of pollution to the marine environment. Early monitoring of oil spill is very important for marine environment protecting. In this paper, the calculation of radar backscattering based on the wave spectrum was carried out, and a review of the study of the damping ratio of wave spectrum in consideration of the films characteristics, water molecular tension, elastic model and surface tension was carried out. The problem of insufficient research on the damping of the oil spill remote sensing monitoring with the wave spectrum and the quantitative calculation of the damping was discussed. The research on the damping of the oil spill for remote sensing monitoring in the future may be based on the backscattering characteristics of the real ocean wave spectrum under the cover with oil slicks. The research on radar coefficient calculation can provide support for quantitative analysis of the damping characteristics of oil spills, thus improving the accuracy of oil spill remote sensing monitoring.

Forest fires are one of the most significant disturbance factors affecting forest ecosystems. Exploring their spatio-temporal variations and forest restoration holds certain sociological and ecological significance. The Great Xing’an Range, possessing the largest primitive area in China, is a key area suffering frequent forest fires. Hence, this study extracted the distribution information of burned zones in the Great Xing’an Range from 2002 to 2021 from the MODIS time series products involving burned zones, land cover, and gross primary productivity (GPP). Moreover, it statistically analyzed the post-fire forest restoration. The results show that: ① Fires in the forest area of the Great Xing’an Range showed an overall downward trend from 2002 to 2021, but the burned areas showed fluctuating changes. Both the burned area and fire frequency were the highest in 2003, followed by 2008, with the lowest burned area seen in 2019; ② Forest fires occurred primarily in spring and autumn, with the highest burned area and fire frequency in March and the second highest fire frequency in September; ③ Forest fires manifested an uneven spatial distribution from northeast to southwest, predominantly in the Great Xing’an Range within Heilongjiang and Hulunbuir City of Inner Mongolia. Moreover, the forest fire area in Inner Mongolia far exceeded that in Heilongjiang. The analysis of forest types in burned zones reveals that the burned areas decreased in the order of broad-leaved, mixed, and needle-leaved forests. According to the time series analysis of GPP in burned zones, GPP values recovered the fastest in the first year post-fire, but it took nearly seven years to recover to the pre-fire growth level. Different forest types manifested significantly distinct post-fire restoration rates, which decreased in the order of broad-leaved, needle-leaved, and mixed forests. Overall, ascertaining the spatio-temporal distribution of forest fires can provide data support for the arrangement and adjustment of fire prevention and control efforts, while investigating the post-fire forest restoration can provide a scientific basis for the rehabilitation and sustainable development of forests.

Atmospheric correction is an important preprocessing step for hyperspectral remote sensing images. The atmospheric correction quality determines the application degree of hyperspectral remote sensing to a certain extent. First, this study analyzed the influence of the atmosphere on radiative transfer and summarized the inversion methods of aerosol optical thickness and water vapor in the atmosphere, indicating the main atmospheric factors affecting the quality of hyperspectral remote sensing images. Then, the influence of the atmosphere was demonstrated theoretically by clarifying the derivation process of the radiative transfer equation and the action mechanism of relevant parameters, indicating the main aspects of hyperspectral atmospheric correction. Furthermore, this study summarized the hyperspectral atmospheric correction methods formed in recent years, including methods based on empirical statistics and radiative transfer, and analyzed the study advances and development trends of hyperspectral atmospheric correction. Finally, this study forecasted the development of atmospheric correction of hyperspectral remote sensing images. This study will provide a certain reference for the engineering application and study of hyperspectral remote sensing.

Taking the interaction between spatial and temporal resolution of remote sensing data into consideration, the authors hold that there is no satellite sensor that can produce images with both high spatial and temporal resolution, and spatiotemporal fusion of remote sensing data is an effective method to solve this problem. This paper introduces main research achievements of spatiotemporal fusion model obtained both in China and abroad. Based on the comparative analysis of the mainstream fusion models, these models can be divided into two categories, i.e., the transformation-based model and the pixel-reconstruction-based model. Furthermore, the authors divide the pixel-reconstruction-based model into mixed linear model and spatial and temporal adaptive reflectance model, and then introduce the basic principles, methods of these models. This paper makes a comparative analysis of the advantages and disadvantages of various aspects of the model. At last, the data, application and scale prospect of spatiotemporal fusion models are put forward.

Leave water reflectance is an important parameter in the study of water optical characteristics. To better interpret the effect of cyanophytes contamination on water optical characteristics, the authors conducted in situ measurement of spectral reflectance and water sampling in the Taihu Lake on 10 and 11, November 2008. Remarkable effects were observed in leave water reflectance of the cyanobacterial water, leading to an obvious absorption peak in the red region and an increase in the near-infrared region. Equivalent leave water reflectance based on FY-3A and MODIS band settings was derived by using the spectral response functions. Furthermore, the authors used the Ration Index (RI) model for the estimation of chlorophyll-a on 12, November 2008, and observed high determination coefficients R2=0.72, which were further used to map the chlorophyll-a distribution. The results obtained will be helpful to the further evaluation of optical characteristics and water quality.

 Characteristics of the main sensors used for the retrieval of the atmosphere and humidity profiles are

briefly discussed in this paper, with emphasis placed upon sensor characteristics and retrieval methods of ATOVS,

MODIS and AIRS. Advantages and disadvantages of these methods are analyzed, and the development trend of the

retrieval methods for atmosphere and humidity profiles is also discussed.

With the fast development and wide application of remote sensing technology, remote sensing images with higher quality are needed. However, it is difficult to directly acquire high-resolution, multispectral remote sensing images. To obtain high-quality images by integrating the information from different imaging sensors, pansharpening technology emerged. Pansharpening is an effective method used to obtain multispectral images with high spatial resolution. Many scholars have studied this method and obtained fruitful achievements. In recent years, deep learning theory has developed rapidly and has been widely applied in pansharpening. This study aims to systematically introduce the progress in pansharpening and promote its development. To this end, this study first introduced the traditional, classical pansharpening methods, followed by commonly used remote sensing satellites. Then, this study elaborated on the pansharpening methods based on deep learning from the perspective of supervised learning, unsupervised learning, and semi-supervised learning. After that, it described and analyzed loss functions. To demonstrate the superiority of the pansharpening methods based on deep learning and analyze the effects of loss functions, this study conducted remote sensing image fusion experiments. Finally, this study presented the future prospects of the pansharpening methods based on deep learning.

Automatic generation is a critical procedure in remote sensing image mosaicking and a key technique for producing digital orthophoto map (DOM). The seamline is closely related to the object integrity and color consistency in the image,so the automatic generation of seamline determines directly the quality and efficiency of mosaic processing, which thus becomes a hot research topic both in China and abroad. In this paper,the authors described the relative principle of the seamline generation and made a summary of four methods for automatic generation of seamline,which included methods based on overlap area image difference,identical points,auxiliary data and morphology. The prospect of further optimizing the seamline is forecasted on the basis of comparing and analyzing the advantages and disadvantages of these methods.

Smart city is the inevitable choice for the development of China’s new urbanization. As a product of informatization and urban integration, smart city is gradually realized as an efficient and fine tool for managing people, money, material and things intelligently. The study of influence of UAV remote sensing technology in the construction of intelligent city plays an important role in accelerating the construction of smart cities. In this paper, the authors first reviewed the definition and development status of smart city, and then introduced the applications of unmanned aerial vehicle (UAV) from urban planning, illegal construction supervision, engineering environmental management, waste management, intelligent transportation, and other aspects.Finally, the development tendency was discussed.

Water is a very important resource, and it is an important material basis for the survival and development of all human beings and organisms. Water extraction can result to easily understand the general situation of existing water resources, thus being conducive to the rational planning and management of water resources and having a significant impact on human life and social activities. Traditional artificial methods are time-consuming and laborious, and therefore satellite remote sensing data is now used to extract water parameters such as water position, area, shape and river width, which has become an effective method and means to obtain water parameters quickly. On the basis of extensive literature research, this paper illustrates the basic ideas of water extraction of remote sensing image and its development course as well as the basic method and current situation of water extraction performed by experts, and makes a comprehensive review and analysis of the advantages and disadvantages of various methods so as to explain the problems of water extraction and research prospect, make the readers understand the situation of this field and provide some ideas for the study in this field.

Aimed at solving the problem of crack detection on buildings, the author has designed a method of crack detection based on the temperature observation above buildings by using the infrared thermal imaging technology. The method utilizes Matlab and other software to conduct related processing of thermal images collected by external industry so as to enhance the display of cracks, thus achieving the purpose of detection and simplifying the image processing process. An analogue experiment on a chapped building in Hangzhou was carried out to examine the reliability of this method. The experimental results show that the theory and method put forward by this paper have some practical value.

Abstract This article introduced mainly a comprehensive method based on aerial remote sensing information,It was used firstly for allover investigation of Great wall in soil tamped structure,and barracks and the enclosing walls as well as dun-block in Ning Xia Hui Autonomous region of NW China.Multitemporal aerial image was used and early aerial image redeveloped.The distribution, the systom of construct,and the functions of Ning Xia Great wall were studied with historical documents.

Soil salinization is identified as a major cause of decreased soil fertility, productivity, vegetation coverage, and crop yield. Optical remote sensing monitoring enjoys advantages such as macro-scale, timeliness, dynamics, and low costs, rendering this technology significant for the dynamic monitoring of soil salinization. However, there is a lack of reviews of the systematic organization of multi-scale remote sensing data, multi-type remote sensing feature parameters, and inversion models. This study first organized the optical remote sensing data sources and summarized the remote sensing data sources and scale platforms utilized in current studies on saline soil monitoring. Accordingly, this study categorized multi-source remote sensing data into three different platforms: satellite, aerial, and ground. Second, this study organized the mainstream characteristic parameters for modeling and two typical inversion methods, i.e., statistical regression and machine learning, and analyzed the current status of research on both methods. Finally, this study explored the fusion of remote sensing data sources and compared the pros and cons of various modeling methods. Furthermore, in combination with current hot research topics, this study discussed the prospects for the application of data assimilation and deep learning to soil salinization monitoring.

Determining the present distribution of historically abandoned mines nationwide and carrying out orderly ecological rehabilitation of these mines are important parts in the preparation of mine ecological rehabilitation planning and serve as the main bases for the deployment of ecological rehabilitation engineering. This study proposed the technical process and method for determining the historically abandoned mines according to the definition of historically abandoned mines and the public management requirements. This technical method was proven effective through tests.

To obtain the fundamental data of mine environments objectively, this study monitored the damaged mining land and the ecological restoration land in abandoned open-pit mines in China by combining remote sensing data with multi-source data, computer automated information extraction with human-computer interactive interpretation, and comprehensive laboratory research with field investigation. The remote sensing monitoring in 2022 shows that the mining land of abandoned open-pit mines in China covered an area of 82.74×10⁴ hm², representing 0.86‰ of the national land area, primarily distributed in Inner Mongolia and Xinjiang Uygur autonomous regions as well as Hebei, Shandong, and Heilongjiang provinces. Among them, the damaged mining land and the ecological restoration land accounted for 50.74×10⁴ hm² and 32.00×10⁴ hm², respectively, with an ecological restoration rate of 38.68%. The mining land of abandoned open-pit mines occupied primary farmland of 2.63×10⁴ hm², representing 3.18% of the total mining area. The mining land of nationwide abandoned open-pit mines within the ecological red line accounted for 8.09×10⁴ hm², representing 9.77% of the total mining area. The mining land of nationwide abandoned open-pit mines, coinciding with the result of the third national land resource survey (mining land), totaled 30.13×10⁴ hm², representing 36.42% of the total mining area. This study preliminarily analyzed the present situation and existing problems of remote sensing work involving the mining land of nationwide abandoned open-pit mines, the occupation of primary farmland, the mining land of such mines within the ecological red line, and corresponding environmental restoration and governance. Finally, this study proposed countermeasures and suggestions in this regard.

Conventional remote sensing monitoring techniques, constrained by data availability and computational capacity, often fall short of the research requirements of extensive landslide disaster monitoring. This study established a dynamic assessment model for landslide hazards in the Three Gorges Reservoir area based on cloud computing platform Google Earth Engine (GEE), achieving dynamic assessment of landslide hazards in the area under the support of the massive data storage and robust computational capabilities of GEE. First, based on factors such as slope, slope aspect, normalized difference vegetation index (NDVI), normalized differential water index (NDWI), and geological structures, a landslide susceptibility zone map was established using a weighted gradient boosting decision tree (WGBDT) model. Then, the rainfall threshold inducing landslides in the Three Gorges Reservoir area was determined based on the Global Precipitation Measurement (GPM) data from the National Aeronautics and Space Administration (NASA). Subsequently, the rainfall classification criteria and a landslide hazard assessment model were established by combining rainfall and landslide susceptibility. Finally, focusing on the rainfall on August 31 in the Three Gorges Reservoir area, the daily distribution maps of landslide hazards in the Three Gorges Reservoir area were plotted, yielding the spatio-temporal variation trend of landslide hazards. In sum, the data processing and analysis tools of GEE allow for the analysis of landslide-related data of the Three Gorges Reservoir area, thus providing nearly real-time monitoring and early warning information for landslide hazards and offering a basis for the formulation of disaster prevention and mitigation policies.

 The methods for supervised and unsupervised classification of remote sensing images are reviewed in this paper. The main problems discussed include the merits, shortages and application fields and conditions of these methods. An integrated evaluation of these methods is also given. The future developing trends and the key points in the study of remote sensing image classification are dealt with at the end of this paper.

Inland water bodies, as irreplaceable resources in ecosystems, play a vital role in climate change and regional water circulation. Scientifically and accurately monitoring the distribution and dynamic changes of water bodies is critical for ecosystem balance maintenance, sustainable human development, and early warning of floods and droughts. However, current research primarily focuses on the static monitoring of inland water bodies, lacking high-resolution monitoring of dynamic changes in water bodies. Hence, relying on the Google Earth Engine (GEE) cloud computing platform, this study monitored the dynamic changes of water bodies at a spatial resolution of 10 m, with the Sentinel-2 surface reflectance data in 2020 as the data source. First, the optimal water body monitoring features were selected by examining the features of typical land cover types in Sentinel-2 spectral bands and water indices. Then, an automatic extraction method for water body training datasets was proposed in conjunction with priori water body products, obtaining high-confidence water body training samples. Furthermore, the spectral angle (SA) and Euclidean distance (ED) methods were integrated based on the Dempster-Shafer (D-S) evidence theory model, and a SA-ED dynamic monitoring model for water bodies was developed combined with the extracted optimal water body monitoring features. Finally, the stability of the SA-ED model was tested with Henan Province as a study area, demonstrating that the SA-ED model can effectively monitor the dynamic changes in water bodies. The SA-ED model yielded an overall monitoring accuracy of 97.03% for water bodies in Henan Province, with user accuracy of 95.85% and producer accuracy of 95.17% for permanent water bodies, user and producer accuracies of 96.21% and 93.82% for seasonal water bodies, respectively. The results of this study provide a novel approach for the fine-resolution monitoring of dynamic changes in water bodies.

This study aims to investigate the application of high-resolution remote sensing images in the supervision of river and lake sand mining in the Dongting Lake area. Based on the aerial and space high-resolution remote sensing images over the past 20 years, as well as human-computer interaction interpretation and field investigation verification, this study summarized the types and meanings of surface elements in river and lake sand mining, established the remote sensing interpretation symbols for river and lake sand mining, and analyzed representative typical cases. The results show that the interpretation symbols of remote sensing images for river and lake sand mining differ from those for onshore mining summarized previously. The river and lake sand mining was carried out using dredges as the mining equipment, sand carriers as the transport equipment, and sand yards and docks as transfer sites. The mining surfaces caused serrated bank lines during sandbar digging. Furthermore, surface cover changed near mining areas, including turbid water and shrinkage of sandbars and shoals. This study analyzed three typical cases, namely the evolution of the Hualong sand yard, the treatment of the Chenglingji wharf, and the illegal sand mining in Piaoweizhou of the eastern Dongting Lake. The analytical results indicate that high-resolution remote sensing can provide technical support for supervising river and lake sand mining.

Exploring the remote sensing-based classification of coastal wetlands is significant for their conservation and planning. Hence, this study investigated the Yellow River Delta with the 8-view Landsat8 OIL images from March to October 2019 as the data source. It constructed seven classification schemes based on different features of the images on the Google Earth Engine (GEE) cloud platform. Then, it employed the random forest classifier to classify different feature sets, with the scheme exhibiting the best classification effects selected for mapping the wetland categories of the Yellow River Delta. Considering poor data quality in August and September due to cloud contamination, this study filled in the cloudy zones using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) algorithm. The results show that: ① The predicted images generated from the ESTARFM manifested a high correlation with the real image bands, with R values above 0.73, suggesting that the reconstructed images could be used in this study; ② The random forest algorithm was used to classify the surface feature types in the study area. Through optimal feature selection, the classification results of Scheme 7 demonstrated an overall accuracy of 92.28%, higher than those of conventional schemes, with a Kappa coefficient of 0.91, aligning with the actual wetland conditions. The results of this study can assist in deeply understanding the spatial distributions of different wetlands in the area, and provide a scientific basis for the conservation and planning of the regional ecological environment.