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

From text analysis to image interpretation， the Topic Model （TM） consistently plays a pivotal role. With its robust semantic mining capabilities， topic model can effectively capture latent spectral and spatial information from Remote Sensing （RS） images. Recent years have seen the widespread adoption of topic models to address challenges in RS image interpretation， including semantic segmentation， target detection， and scene classification. Thus， clarifying and summarizing the present application status of topic models in remote sensing imagery is pivotal for advancing remote sensing image interpretation technology. This paper initially presents the foundational theory of topic models， followed by a systematic overview of their typical applications in remote sensing imagery. In addition， experimental comparisons and analyses are performed across various typical remote sensing image interpretation tasks， illustrating the extensive applicability of topic models in the realm of remote sensing and the efficacy of distinct topic models in enhancing our comprehension of remote sensing imagery. Subsequently， we have outlined the limitations of topic models and explored the potential and prospects of integrating them with deep learning.

Object recognition technology based on high-resolution remote sensing images is widely used in the fields of land and resource monitoring and intelligence collection. Accurate and fast object detection methods are the hot spots and difficulties in the current research on remote sensing images. However， the current detection methods overly pursue improving detection accuracy while ignoring detection speed. Therefore， an improved lightweight network is proposed based on YOLOX to balance detection speed and accuracy. Firstly， for the backbone of feature extraction， a Mobilenetv3tiny is proposed to improve the detection speed by reducing the parameters of the network. Secondly， the Ghost is introduced into the feature pyramid networks to reduce the complexity of the network under the premise of ensuring detection accuracy. Finally， Alpha-IoU and VariFocal_Loss are used to optimize the loss function to improve the convergence speed and positioning accuracy of the network. The ablation experiment was carried out on the NWPU VHR-10 dataset. The results show that， compared with the baseline， the improved network has a detection accuracy increase of 0.76%， a speed increase of 19.72%， a weight of 11 M （Mega）， and a parameter reduction of 65.66%. The overall effect of the improved network is better. In addition， comparative experiments on the DIOR dataset show that the detection speed is improved by 26.88% while ensuring high detection accuracy. And that proves the effectiveness of the improved network. Therefore， the improved network can effectively balance detection speed and accuracy and is easy to deploy， which makes it suitable for real-time detection of remote sensing image targets.

Taking the SCI and SSCI papers in the Web of Science （WoS） and the journal papers in the CNKI as the data source， we used bibliometrics and the literature analysis software CiteSpace to explore the development and hot spots of urban ecological remote sensing in the past three decades and drew the knowledge map of the field from 1991 to 2021. The results show that： （1） in terms of the number of publications， the research on urban ecological remote sensing has gone through three development stages， i.e.， budding stage， accumulation stage， and rapid growth stage； globally， the center of centroid of the number of published papers shows a trend of migration from the east to the west and then back to the east； （2） in the light of research institutions， Institute of Geographic Sciences and Natural Resources Research， Chinese Academy of Sciences and Beijing Normal University have published most papers in this field in WoS and CNKI； （3） in terms of journals， Remote Sensing and Acta Ecologica Sinica have published most papers in this field in WoS and CNKI， respectively； （4） in terms of core authors， Weiqi Zhou in Chinese Academy of Sciences and Hanqiu Xu in Fuzhou University （China） published the most papers in WoS and CNKI， respectively； and （5） statistics of keywords shows that the keywords of ecological environment， land use， and landscape pattern were the hot spots of urban ecological remote sensing research in recent years. In general， urban ecological remote sensing was playing an increasingly important role in the disciplines of environmental science， physical geography， and geomatics. The application of remote sensing ecological models to quantitatively assess ecological environment quality has become an important trend in this field. Also， urban ecological remote sensing research requires higher and higher spatial resolution of remote sensing images. Collaborative inversion of urban ecological quality with various remote sensing images will also be a development trend in this field.

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

China's coastal areas are not only the most strongly interacting zones extending from land to sea， but also the natural spatial units on the surface of the earth affected by runoff， tides and the effects of human activities and climate change. In this paper， using with the 2003~2018 high quality temporal resolution Land Surface Temperature （LST） and Near Surface Air Temperature （NSAT） products， the spatial and temporal distribution pattern and synergy of urban heat islands and urban heat waves in coastal cities in China are systematically compared. The results show that： （1） Extreme high temperature events in China's coastal regions show a trend of increasing intensity and duration. Specifically， the intensity of urban heat islands in summer is as high as 2.25 ℃， the average heatwave frequency based on ground temperature in the entire coastal area is 24.59 times， and the temperature-based heat wave frequency is 16.33 times， accounting for 90.81% and 96.68% of the annual heat wave frequency， respectively； （2） The frequency of urban heat waves and urban heat island intensity is significantly positively correlated in the North Temperate Zone and North Subtropical Zone along the coast， and is most obvious in the North Temperate Zone. An increase of 1°C in average LST can lead to an average increase in heat wave events twice. Among them， the heat wave frequency in the North Temperate Zone increased the fastest in the three major regions， and the compound growth rate of heat wave frequency based on LST and NSAT exceeded 5%； （3） From 2003 to 2018， the urban population of China's coastal regions increased by 59%， and the number of people affected by heat waves increased by nearly 370%， exceeding 5% of the total urban population （about 40 million people）. Although the urban thermal environment in China's coastal regions and the El Niño and La Niña phenomena in the sea show a more consistent distribution in time and space， how the overall urban change and population growth promote the change of high temperature and thermal environment patterns in different regions still need to be further discussed and analysed using longer time series and high spatial and spatial resolution data.

Forest age is an important characteristic parameter of forest ecosystem， and it is of great significance to accurately estimate carbon storage and carbon sink of forest ecosystem. However， at present， there are few reviews on the age datasets and its estimation algorithm， so this paper systematically summarizes and analyzes the existing forest age datasets， which were divided into global and regional forest age datasets according to the spatial coverage of the data. Then， we analyzed the algorithms of forest age datasets and their advantages and disadvantages. Research has shown that （1） The forest age algorithms mainly include the downscaling statistical method， relationship equations between forest age and forest structure parameters， forest disturbance detection algorithms and machine learning algorithms （such as random forest algorithm）. （2） The advantage of the downscaling algorithm is simple and easy to use， but its main disadvantage is that the saturation of normalized differential vegetation index will underestimate the age of the old-growth forest. The advantage of the relationship model between forest age and forest structure parameters is that the high-precision remote sensing data of tree height or biomass data can reflect the spatial heterogeneity characteristics of forest age， and the forest growth model has a theoretical basis. But the disadvantage is that the results are restricted by the accuracy of forest or tree species distribution map， and the influence factors of forest growth are not considered comprehensively. The advantage of forest disturbance monitoring algorithm is that mature algorithms can be used to detect forest disturbance and infer the change of forest age， but the disadvantage is that the forest age must be combined with other methods to obtain the age of old forest. The advantage of random forest algorithm model is that the model is easy to build， does not need to set specific statistical assumptions and model forms， and doesn’t rely on forest type map or tree species distribution map. The disadvantage of this method is that it is restricted by the numbers and the spatial distribution representation of the model training samples. （3） Forest inventory data and remote sensing data are important data for forest age estimation. Forest age has important application prospects in ecological model driving， forest management and carbon neutrality. In the future， the research of forest age should strengthen the ground observation of forest age， combine the advantages of remote sensing data， and use a variety of machine learning algorithms to develop high spatial and temporal resolution forest age data.

Monitoring the dynamic changes of water area in Dongting Lake is of great significance for flood control， ecosystem stability and biodiversity. The deep learning algorithm represented by the classic Unet the innovative HRNet has become an efficient way to obtain remote sensing image information. Taking Sentinel-1A SAR image as the main data source， this paper qualitatively and quantitatively analyzes the water extraction results of SDWI index method （Sentinel-1 Dual-Polarized Water Index，SDWI）， object-oriented classification method， UNet network model and HRNet network model. Based on the best water extraction method， the temporal and spatial variation characteristics of water area in the flood season （April to October） of Dongting Lake from 2016 to 2021 are analyzed. The results show that ： ① The deep learning algorithm represented by HRNet and Unet has better water extraction effect than traditional methods. Among them， HRNet has superior performance in noise suppression and shadow resistance， and the F1 score， MRate and MIoU are 0.961 6， 0.007 8 and 0.958 6， respectively. ② During the flood season， the water area of Dongting Lake shows the characteristics of “ increase-full-decrease ” in the monthly variation. The lake surface begins to expand from April to May， and the water area maintained at a high level from June to August. Since then， due to the decrease of inflow， the water area gradually decreases from September to October. The 2 263.90 km² at July 30， 2020 is the largest water area monitored during the study period. ③ The submerged frequency of water body in flood season of Dongting Lake gradually decreases from the center of the lake body and the main stream. The distribution patterns of submerged frequency in different lake areas are different. The submerged frequency of East Dongting Lake is higher than that of South Dongting Lake and West Dongting Lake. In summary， the combination of Sentinel-1A SAR image and deep learning technology can realize the efficient acquisition of water information and high-frequency monitoring of the water surface area at Dongting Lake， providing a new idea for the high dynamic lake water monitoring.

With the rapid development of earth observation technology， high-resolution remote sensing image change detection has become a research hotspot in the remote sensing domain. The increase in spatial resolution brings rich spatial information， but also leads to the problem of "pseudo-change" caused by the change of the spectrum and other performance characteristics， which does not change due to phenological differences. Morphological Attribute Profiles （MAPs）， as an efficient spatial information modeling method， can accurately describe complex change characteristics from different attributes and multiple scales， and have been widely used in the field of change detection tasks. Nevertheless， the existing MAPs methods usually do not consider the properties and scale balance of the differential profile， so they are prone to fall into local optimum； at the same time， the effective fusion of differential features into change detection results is another difficult problem faced by such methods. To this end， this paper proposes a change detection method that combines adaptive MAPs with decision fusion. Firstly， the initial differential feature set is extracted by CVA on the MAPs； On this basis， a Balanced Optimal Objective Function （BOF） is designed to extract the optimal differential feature set； Finally， based on the proposed change intensity evidence index （EVI） and evidence confidence index （IOEC）， a multi-feature decision fusion framework is constructed to obtain change detection results. The experimental results show that the Overall Accuracy （OA） and F1 score （F1） of the proposed method can reach 96.41% and 88.67%， respectively. which are significantly better than the comparison methods in both visual analysis and quantitative evaluation. especially for the "pseudo-variation" proposed in this paper. Compared with the comparison method， the method in this paper can realize more accurate discrimination and effectively alleviate the "pseudo change".

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

The application of carbon satellite observation data urgently needs to solve the problems of spatiotemporal discontinuity and low spatiotemporal resolution. Among them， the high spatial and temporal resolution reanalysis data reconstructed from XCO₂ product data will provide a solution to the lack of real-time observation data in regional and industrial CO₂ source and sink research. Based on 4 sets of XCO₂ product data， DataCube multi-dimensional data modeling can integrate the spatio-temporal information of different product data， thus realize the unified storage， correlation and gridding of spatio-temporal information of 4 sets of XCO₂ product data. An XCO₂ reconstruction method is proposed that can realize dynamic mapping， interaction， correlation analysis and feature fusion of multiple XCO₂ product data， and finally the XCO₂ reconstruction product of the 100 m full coverage grid in Beijing is obtained. The results show： the reconstruction results are highly consistent with the TCCON Xianghe station observation values （R²=0.90， RMSE=0.89）， and are consistent with the time change trend of the CarbonTracker simulation results and TCCON observation results； in 2020， XCO₂ in Beijing generally displayed a spatial distribution of lower in the north， higher in the central north and south， and highest in the central part； and dynamically reveals the regularity of the high and low value spatio-temporal variations and the vertical and horizontal transportation characteristics between "ecological conservation areas" and "development areas" in the Beijing area due to the influence of surface carbon sources and sinks.

Exploring snow's response to surface temperature is crucial for understanding snow cover dynamics. In the central Tianshan Mountains， using daily cloud-free snow depth and surface temperature data from 2010~2019， we analyzed coupling， coordination， and lag times via coupled models. Key findings： ①Annual coupling and coordination vary spatially with altitude （rising-declining-rising）， and seasonally （decreasing from winter to summer）， displaying distinct vertical patterns. ② Over the decade， coupling and coordination fluctuated， with increases in the east， decreases in the north， and significant declines below 1 600 m，contrasting with slight gains above the snowline. ③Lag times of snow depth response to temperature increased from spring to winter， peaked during ablation seasons at higher altitudes， and exhibited yearly trends of rising in spring and slight declines in autumn， winter， and summer.

Timely and accurate acquisition of rice growth information plays an important role in guiding agricultural development and formulating agricultural policy. In this paper， based on the MODIS and Landsat8 remote sensing image data of Sanjiang Plain from 2014 to 2022， the Google Earth Engine （GEE） cloud platform was used to extract the rice planting area and analyze its change trend. Three characteristic indexes （NDVI， EVI and LSWI） were objectively weighted by coefficient of variation method， and Rice Growth Index （RGI） was constructed.The interannual difference model was used to evaluate the rice growth in 2022 compared with the perennial （2014~2021）， and to analyze the spatio-temporal variation and yield trend from tillering stage to filling stage in the Sanjiang Plain. The results showed as follows： （1） From 2014 to 2021， the rice planting area in Sanjiang Plain showed an overall increasing trend， with an increasing area of more than 5 000 km². （2） From June to August 2022， the rice growth in all cities and counties in Sanjiang Plain showed a steady upward trend of "equal tillering stage， best jointing booting stage， and better heading and filling stage"， and the growth in the eastern and northern parts was better than that in the western and southern parts， and the northeastern part showed the best growth. （3） The change trend of rice yield in the 2022 growing season is inferred to be "poor harvest in some areas， high harvest in most areas， and the overall trend of steady increase". The results can provide a scientific basis for agriculture department to guide agricultural activities and domestic rice growth monitoring and yield estimation by remote sensing.

Grassland is crucial to the ecosystem stability and agricultural development. In semi-arid or arid areas， the problem of insufficient water for grass growth is mainly solved by irrigation， but grasslands with irrigation conditions are limited. At present， there are few researches on the monitoring of irrigation and rain-fed grassland resources and their spatial-temporal distribution using remote sensing techniques， and the relevant datasets are extremely lacking， which brings inconvenience to the assessment of water resources in areas where irrigation and rain-fed grasslands coexist and land surface process simulation. Therefore， this study used the Google Earth Engine Calculation Cloud Platform （Google Earth Engine， GEE）， called the Sentinel-2 satellite remote sensing image of 2020， selected random forest classification method （Random Forest， RF）， added vegetation moisture index， extracted irrigated and rain fed grassland in the middle and lower reaches of the Bayin River in the northeast of the Qaidam Basin， and formed a dataset. After verification， the overall accuracy of this data set was 99%， and the Kappa coefficient was 0.84. The area of irrigated grass was 74.23 km²， accounting for 4.1% of the total grassland area in the middle and upper reaches of the Bayin River. This data set accurately reflected the interlaced distribution characteristics of irrigated grassland， cultivated land， wetland and other types of land. This study can support the water resources planning and evaluation， land surface process simulation， etc. in the Bayin River Basin.

With the acceleration of urbanization and industrialization in China， the urban thermal environment has undergone tremendous changes， and the urban heat island effect is gradually strengthening， which has adverse effects on the urban ecological environment and climate. This paper calculated Surface Urban Heat Island Intensity （SUHII） by grids using MODIS Land Surface Temperature （LST） of Taiyuan main urban area from 2003 to 2021， analyzed the spatiotemporal distribution changes of Surface Urban Heat Island （SUHI） and their relationship with urban expansion using spatial statistical methods， and then explored the influencing factors on SUHI based on random forest model. The results showed that： （1） In the past 20 years， the heat island effect in Taiyuan City has shown a growing trend with urban development， with significant seasonal differences， with the strongest in summer and the weakest in winter. （2） The urban heat island effect in the main urban area of Taiyuan City has significant positive spatial autocorrelation， and the highly agglomeration area is significantly expanded. （3） The spatial expansion direction of urban heat islands is basically the same as the direction of urban expansion.（4） Human factors are the main factors affecting the urban heat island effect， with GDP and PM_2.5 having the greatest impact. This study can provide a methodological reference for for the quantitative evaluation of urban effects in areas with significant terrain fluctuations， and provide a understanding of the thermal environment in Taiyuan City and a scientific reference for formulating urban planning strategies.

Grassland Fractional Vegetation Cover is an important ecological parameter for evaluating the health status of grassland and monitoring environmental changes. At present， the extraction of Fractional Vegetation Cover at large regional scale is mainly based on satellite remote sensing data， and Unmanned Aerial Vehicle Remote Sensing （UAVRS） data， as a supplementary means of estimating grassland cover from satellite data， can improve the accuracy of model estimation. Based on the UAVRS data and BJ3 satellite data， three vegetation cover inversion methods， namely regression analysis method， pixel dichotomy method and random forest were used to invert and validate the vegetation cover of desert grassland in Otog Banner. The results showed that the best inversion model among the regression analysis models established by the vegetation index was the quadratic polynomial model of Normalized Difference Vegetation Index （NDVI）， with R² =0.752； the R² and RMSE obtained from the random forest model directly using the waveband values of UAVRS data were 0.893 and 0.072， compared with the quadratic polynomial model of NDVI and the pixel dichotomy model， R² is improved by 0.141 and 0.151. Using the UAVRS data and the random forest method， it is possible to quickly and accurately obtain the vegetation cover of the study area on the satellite scale， which can provide support for the inversion of desert grassland vegetation cover in the large region.

Buildings are integral components of urban areas. Extracting buildings from high-resolution remote sensing data holds significant academic importance in areas such as land use analysis， urban planning， and disaster risk reduction. For the problems of building extraction， an improved Mask R-CNN building instance segmentation model is proposed. Based on the residual neural network fusion convolutional attention model， a residual convolutional attention network is constructed to improve the problem of inadequate feature extraction. The loss function is optimized by adding the Dice Loss method， and then the feature learning process is optimized. And a post-processing strategy combining Douglas-Peucker algorithm and Fine polygon regularization algorithm is introduced to make the building contours more regular and smooth. The experimental results show that the improved model improves the detection mAP value by 7.74% at Iou 0.5 and 7.57% at Iou 0.75 compared with the original model， and the post-processing strategy improves the F1-Score value by 6.01% compared with the original model after selecting the appropriate threshold to optimize the mask. The instance segmentation model coupled with Mask R-CNN and attention mechanism improves the small building misdetection and omission problem， building segmentation boundary adhesion problem， and building segmentation accuracy； building post-processing strategy， improves building regularization.

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

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

The cultivation and breeding of rice in Hainan， one of the primary tropical regions in China， play a crucial role in meeting the country's demand for this essential food crop. Currently， there are several challenges in monitoring rice cultivation in the tropical region of Hainan， including limited automation， excessive workload， and low accuracy. In this study， we selected Haikou City in Hainan Province as our experimental area. By utilizing high-resolution multi-spectral satellite remote sensing images such as Jilin-1， Beijing-2， WorldView， and Gaojing-1 along with field verification data， we established a comprehensive database consisting of multi-source and multi-scale samples to accurately identify rice planting areas within the tropical region of Hainan. We employed the DeepLab-V3+ convolutional neural network model for training purposes and proposed an intelligent remote sensing interpretation method specifically tailored for identifying rice planting areas within the tropical region. Experimental results demonstrated that our approach achieved an impressive accuracy rate of 81.9% with a recall rate of 86.7% when extracting rice intelligently based on the DeepLab-V3+ convolutional neural network model. These findings highlight that by training a convolutional neural network model using our interpretive sample database， it becomes possible to accurately extract regions where tropical rice is cultivated from high-resolution multi-spectral remote sensing imagery—a methodology that can serve as a valuable reference for future studies on extracting information related to tropical rice cultivation.

MODIS time series surface reflectance data is widely used in the dynamic monitoring of land surface， but the influence of factors such as cloud cover causes spatial and temporal gaps in the data， which affects the data availability. In this paper， we propose a time-domain reconstruction method based on L1 regularization， which can effectively repair the gaps in MODIS surface reflectance data and realize the reconstruction of long time-series data with high accuracy. The proposed method firstly identifies the noise generated by natural and systematic factors in the time-series data， and then pre-fills the missing information region inter-annually based on noise detection. On this basis， we introduce a L1 regularization model that is more robust to abrupt noise， and construct a variational model combining the noise masks to restore the time series trend of land surface. The experimental results show that compared with SG filtering， HP filtering， L1 filtering and HANTS， the method in this paper achieves the highest reconstruction accuracy at different percentages of missing pixels of 10%， 25%， 50% and 75%， and also achieves better reconstruction results under different ground surface scenes. Therefore， this method has more advantages in both time series curves reconstruction and spatial details restoration， which shows a high practical value.

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

In order to better understanding the distribution of CH₄ concentration in China， this study analyzed the temporal and spatial distribution characteristics of CH₄ concentration in China from 2003 to 2018 using CH₄ concentration data provided by C3C. The results show that：（1）In terms of spatial distribution， the CH₄ concentration features a pattern of high concentration in the southeast and low concentration in the northwest， with high values mainly distributed in Hunan， Hubei， and Guangxi， and low values in Qinghai and Tibet.The interannual variation of CH₄ concentration in China showed an increasing trend over time. The methane concentration remained relatively stable from 2003 to 2006， and increased significantly from 2007 to 2018.（2）The CH₄ concentration exhibits a significant seasonal variation， with higher concentrations in summer and autumn and lower concentrations in spring and winter. The highest concentration was 1 781 ppbv in autumn， while the lowest concentration was 1 748 ppbv in spring. The peak concentration occurred in August and September， and the low point occurred in March and April.（3） In order to better analyze methane concentration in China， China is divided into six geographical regions， which are Northeast China， South Central China， East China， North China， Northwest China， Southwest China.Methane concentrations are higher in East China， South Central China， and lowest in the Northwest China， Southwest China.

Polar sea ice， with its important impact on global climate change， makes accurate acquisition of multi-element information of sea ice the core task of polar observation. Satellite is the main technical means of polar sea ice monitoring， which has been widely used to observe polar sea ice at the domestic and foreign. To clarify the current status of satellite remote sensing of polar sea ice at home and abroad， which is an important guideline for the development of new remote sensing sensors for sea ice in polar regions in the future. In this paper， the domestic and foreign satellites with polar sea ice information acquisition capability that are currently in orbit are reviewed in detail. On this basis， the main application progress in polar sea ice observation based on satellite data is summarized. Finally， it points out the shortcomings of the existing global earth observation system of polar sea ice， and puts forward suggestions for the development of China's subsequent polar sea ice observation.

Object detection in remote sensing images has become a vital aspect of the overall object detection domain. To address the problems of missed detection and false detection of small-scale objects in high-resolution remote sensing images with complex backgrounds， a local adaptive feature weighting algorithm is proposed at the detection stage， combined with the YOLOv5s algorithm. By learning the existing label box information， the foreground containing target features is separated from the background， and the local features of the target with key information in the foreground are obtained. The spatial scale and weight of local features of each layer are calculated adaptively. Meanwhile， a global attention mechanism is proposed to enhance the interaction capability of cross-dimensional feature information between channels and spatial dimensions in the backbone， so as to strengthen the correlation between features and compensate for the loss of global information within local features at the detection stage， thereby reducing the rates of missed detection and false detection of targets. Experimental results show that the improved algorithm achieves certain improvements in precision and recall， with a mean average precision reaching 72.33%， representing an increase of 2.66% compared to the traditional YOLOv5s algorithm.

AW3D30 DEM data is one of the most widely used basic geographic information data， and its accuracy directly affects the reliability and rigor of a series of derivative products. Therefore， the accuracy validation and improvement of AW3D30 DEM data has always been a research hotspot.. However， conventional high-precision verification data are difficult to obtain and expensive to apply in a wide range of research areas. With global coverage and sub-meter elevation accuracy， ICESat-2 data can provide reliable reference data source for AW3D30 DEM data accuracy validation and improvement. Therefore， this paper takes Henan Province as the study area， and uses ICESat-2 data to validate the elevation accuracy of AW3D30 DEM from the perspective of slope， aspect， geomorphic type and land use type and proposes the Random Forest-Long Short Term Memory Network（RF-LSTM） hybrid model to improve AW3D30 DEM. The results show that the elevation accuracy of AW3D30 DEM decreases with the increase of slope， elevation and topographic relief. The slope direction has less influence on AW3D30 DEM’s elevation accuracy， and the error distribution has no obvious regularity. The accuracy is higher in bare land and cultivated land， and worse in woodland land. The RF-LSTM hybrid model can significantly reduce the mean absolute error and root mean square error of AW3D30 DEM， improve the accuracy of AW3D30 DEM， and provide a reference for the establishment of other DEM data improvement models.

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

Scientific research on the spatio-temporal variation characteristics of surface vegetation GPP and its response to climate change is of great significance for in-depth understanding of terrestrial carbon cycle， evaluation of environmental quality of terrestrial ecosystems， and estimation of the ecological effects of future climate change. At present， most of the time series of GPP spatio-temporal changes on the Qinghai-Tibet Plateau are short and the spatio-temporal changes and characteristics of GPP are not deeply analyzed. Therefore， this study used linear trend detection methods to study the changes of GPP in the Tibetan Plateau between 2001 and 2019， and analyzed the response of this feature to topography and land types， while exploring the relationship between GPP and air temperature， precipitation and snow cover. The results show that the inter-annual trend of GPP on the Tibetan Plateau shows the distribution characteristics of low in northwest and high in southeast， with a maximum value of 0.23\mathrm{g}\mathrm{C}\bullet {\mathrm{m}}^{-\mathrm{2}}\bullet {\mathrm{d}}^{-\mathrm{1}} and a minimum value of -0.125\mathrm{g}\mathrm{C}\bullet {\mathrm{m}}^{-\mathrm{2}}\bullet {\mathrm{d}}^{-\mathrm{1}}；From October to March， the inter-annual change trend of GPP in most regions is small. The inter-annual change trend and spatial heterogeneity of GPP in July is the largest， from -0.384\mathrm{g}\mathrm{C}\bullet {\mathrm{m}}^{-\mathrm{2}}\bullet {\mathrm{d}}^{-\mathrm{1}} to 0.303\mathrm{g}\mathrm{C}\bullet {\mathrm{m}}^{-\mathrm{2}}\bullet {\mathrm{d}}^{-\mathrm{1}}，and gradually decrease from August to October. The interannual trend of GPP decreases with elevation increasing.The interannual trend of GPP south of 30° N decreases rapidly and north of 30°N increases slowly with latitude increasing. The increasing inter-annual change trend of cropland/natural vegetation mosaics type is largest， 0.034 8\mathrm{g}\mathrm{C}\bullet {\mathrm{m}}^{-\mathrm{2}}\bullet {\mathrm{d}}^{-\mathrm{1}}.The decreasing inter-annual change trend of closed shrublands is largest， 0.007 6\mathrm{g}\mathrm{C}\bullet {\mathrm{m}}^{-\mathrm{2}}\bullet {\mathrm{d}}^{-\mathrm{1}}.In the past 19 years， temperature has the largest contribution to the GPP， followed by precipitation， and by radiation again， the contribution of snow to the GPP is relatively weak.

Gansu Province plays an important role in the national strategies. Analyzing the regular pattern of urban expansion and temporal and spatial characteristics of Gansu Province is of great significance for the promotion of new urbanization and the implementation of Belt and Road strategy. An urban expansion model analysis framework composed of standard deviation ellipse， Kernel density analysis， geometric feature analysis of built-up area， position-scale law and correlation matrix was established， and the law of urban expansion over the past 10 years was systematically evaluated based on NPP-VIIRS-like nighttime light remote sensing data corrected across sensors. First， after moving from Lanzhou City to Baiyin City， the center of gravity of nighttime light moved back again， while the distribution direction was generally northwest-southeast， with obvious agglomeration phenomenon in this direction. Secondly， the scale of cities in the province has grown significantly. Its spatial pattern evolved from the dual-core pattern of "one main and one time" in Lanzhou and Jiayuguan to Lanzhou as the center， Jiayuguan and Qingyang as the two wings. What’s more， the expansion rate of built-up areas in the province has gradually slowed down， and the expansion mode is divided into four groups related to geographical location according to the geometric characteristics of built-up areas. And then， Prefecture-level cities obey the distribution of the order-scale law， and the distribution type is secondary， which means the primacy index of the provincial capital Lanzhou City is not significant. Finally， Total amount of lights at night and the displacement of the center of gravity in the east-west direction are closely related to socio-economic factors， while the displacement in the north-south direction is not strongly correlated. Research results comprehensively reveal the law of urban expansion in Gansu Province， which has great reference value for optimizing the urban system and optimizing the pattern of land development.

Black and odorous water occur frequently in rural China， and research into monitoring them by employing remote sensing technology has only recently begun， with many technical issues to be resolved.The samples were collected in rural areas of Jilin， Yunnan， and Guangxi provinces for this study， and 75 water samples from black and odorous water and 85 water samples from normal water were collected between 2021 and 2022， and their water quality parameters as well as optical properties were analyzed separately. We analyzed the image spectral characteristics of black and odorous water and normal water using GF-2 images， and observed that the reflectance of rural black and odorous water has an increasing trend in the red and near-infrared bands， whereas the reflectance of red and green bands was very low and the difference was small. Based on the two typical spectral characteristics of black and odorous water， the MBOI （Multi-spectral black and odorous water index） was developed， with a high identification accuracy. The following are the main research findings： （1） Black and odorous water have a higher concentration of total suspended particulate matter than normal water， and the concentration of organic carbon in black and odorous water bodies is 1.82 times higher compared to normal water. （2） At 440 nm， the absorption coefficients of all the materials， including algal particulate matter， non-pigmented particulate matter， and colored dissolved organic matter of black and odorous water were greater than those of normal water. （3） The data modeling and model verification are carried out by using the spectral reflectance data of the image after Rayleigh correction. When the MBOI value is between 0 and 0.18， it is determined as a black and odorous water body， and the model accuracy meets the requirements of black and odorous water body recognition.

Providing a dynamic method to monitor water storage in the Yellow River Basin and obtaining spatial and temporal conclusions on real-time water storage changes is of great significance to the ecological protection and social development of the basin. In this study， we adopted GRACE/GRACE-FO satellite data and meteorological station data from 2002 to 2020 in the Yellow River basin， reconstructed the missing data through multi-layer perceptual neural network. We analysed the characteristics of the Tibetan Plateau region， the Loess Plateau region， and the downstream region by combining the differences in water resources recharge， and human activities under different climatic environments. We further united the basin as a whole， and obtained the sub-districts and basin overall temporal and spatial patterns of change of terrestrial water reserves and temporal patterns of change of climate elements， and then analysed the influencing factors. The results showed as follows：①The overall land water reserves in the Yellow River Basin showed a downward trend， while the Qinghai-Tibet Plateau showed an upward trend， and the driving forces of regional water reserves were different.②There is a seasonal variation of land water reserves in different regions. There is a lag of 2~3 months between peak annual precipitation and peak annual water storage in the Loess Plateau region and downstream region， but not in the Tibetan Plateau region.

The observation characteristics of MODIS data with high temporal resolution and medium spatial resolution can play an important role in fire detection. However， MODIS fire detection is currently in areas with high heterogeneity， and there are many false detections of fire， and cold fire are easily missed. To solve this problem， in order to fully mine the relevant information in MODIS data， realize the high-precision identification of fire points. A MODIS fire detection algorithm using deep learning technology is proposed. Acquisition of a large number of samples with high quality and broad representation is the prerequisite for deep learning to achieve accurate detection of fire. In order to increase the number of fire samples and ensure the quality of wildfire samples， use the American ground wildfire data set as real fire samples to accurately match them with MODIS data in time and space， and build a fire detection sample library based on deep learning methods. According to the analysis of the radiation transfer process， the wave band and band combination with good identification for fire detection are determined as the input source. Based on the constructed sample data set and information source， build a DNN （Deep Neural Network） fire detection model. Application experiments were carried out in three typical scenarios and compared with MODIS fire products. The results show that the improved method reduces the average brightness temperature of 4um by 2 K in the extraction of cold fire in agricultural areas， and the ratio of correct to wrong changes is positive. Compared with MODIS products， the false fire points extracted in the suburban area are significantly reduced， and the false fire points near the 4um brightness temperature of 325 K are excluded， and the false detection rate is reduced by 19.89%.

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

Ecological quality monitoring and evaluation is the basis and key to carrying out ecological and environmental protection work reasonably and efficiently.Ecological quality monitoring is the basis and key process for effective ecological conservations， and it also plays an important role in ecological civilization and high quality development of our society. In this study， we constructed a Remote Sensing based Ecological Index （RSEI） to monitor the spatio-temporal changes of ecological quality in the Hangzhou Greater Bay Area during 1995-2020. On the basis of which， we explored the quantitative relationship between ecological quality and land use types or conversions by coupling RSEI and land use data. The results showed that， （1） the overall ecological quality of Hangzhou Greater Bay Area first increased， then decreased， followed by an increase at the end. The RSEI maintained at 0.62， indicating that a relatively good ecological quality. The ecological quality of its belonging prefecture-level cities exhibited three different trends： decline， rise and fluctuation from north to south. （2） The area with improved ecological quality （57.5%） was greater than the one with degraded ecological quality （42.5%） in the Hangzhou Greater Bay Area. The forests， central urban areas and coastal wetlands were the hotspots for ecological quality imporvement， while the processes of rapid urbanization and tidal flat reclamation were responsible for degraded ecological environments. （3） In recent 25 years， the ecological quality of forest and build-up areas increased rapidly （△RSEI>+0.1）， but the ecological quality of cultivated land decreased significantly （△RSEI=-0.08）. This migrated the gap of ecological quality within human dominant environments， meanwile increased the ecological cost of the transformation from nature to human dominant environments. The study is expected to provide scientific basis for balancing coastal resource developments and ecological conservations， and serve for the high-quality development of coastal urban agglomerations.

This study proposes a method based on machine learning algorithms to improve the accuracy of hyperspectral tree species classification by changing spatial resolution at the regional scale， providing a new approach for tree species classification research in terrestrial surveys. This study used drones to obtain hyperspectral images of the entire Chengdu Botanical Garden， and collected 1 249 samples of 140 tree species in the garden. By constructing 32 vegetation indices and 176 original bands for variable screening， a classification model was established using two algorithms： random forest and support vector machine. Based on the forest stand types and canopy sizes of typical tree species in the study area， 10， 15， and 20 tree species were selected at 9 different spatial resolutions to explore the accuracy of tree species classification. The results showed that when the spatial resolution gradually decreased from 0.12 m to 4 m， the classification accuracy of the models for 10， 15， and 20 tree species reached the highest level at a resolution of 3 m， and the overall accuracy of the support vector machine classification results was relatively high. This indicates that methods based on support vector machine algorithm， feature variable extraction and selection， and determining the optimal observation scale can effectively capture canopy information of different tree species and improve tree classification accuracy.

Non-carbon dioxide greenhouse gases such as methane （CH₄）， nitrogen dioxide （NO₂）， nitrous oxide （N₂O）， and ozone （O₃） also have a huge impact on the climate in addition to carbon dioxide. For example， methane is the second most important greenhouse gas after carbon dioxide （CO₂） in radiative forcing， and ozone has become the primary pollutant in many places in China after PM_2.5. Rapidly locating emission sources， quantitatively monitoring non-carbon dioxide emissions， and accurately estimating the distribution of global and regional non-carbon dioxide sources and sinks is of great practical significance for formulating， implementing， and evaluating emission reduction measures. This study reviews the development of satellite-based greenhouse gas retrieval， starting from the principle of algorithm， then the current state of methane， nitrogen dioxide， Nitrous Oxide， and ozone retrieval， and identify gaps in existing studies for potential future research. The study on satellite-based non-CO₂ gas concentration is undergoing rapid growth. The optimal estimation method and DOAS-based algorithm are used as mainstream methods for estimating non-CO₂ greenhouse gas concentration using satellite observation. The overall column concentration accuracy of CH₄， NO₂， N₂O， and O₃ can be up to 1%， 10%， 1%， 1%， respectively. To better provide reliable data products for monitoring and practical applications， we should： （1） further improve the accuracy and efficiency of Non-CO₂ gas concentration retrieval； （2） further investigate data assimilation issues based on multiple sensors， algorithms， and products； （3） further optimize the retrieval models for different land surface types； （4） introduce refined spatial resolution， spectral resolution and more accurate auxiliary data； （5） further coordinate global multi-satellite network resources； （6） further study the Interrelationships between multiple gases.

The estimation of crop nitrogen content at the field scale by using Unmanned Aerial Vehicle（UAV） images has attracted increasing attention due to their nondestructiveness and time-effectiveness. The black soil region of Northeast China is the main agricultural production base in China， and accurately obtaining crop nitrogen content is of great significance for national food security. In this study， the Leaf Nitrogen Content（LNC） of maize was estimated by the stepwise regression method using UAV hyperspectral images and 22 narrowband spectral indices at the jointing， silking， and maturity growth stages of maize. The results showed that the maize LNC estimation models at the three growth stages all had good performance. Moreover， the estimation accuracy of the model at the maturity stage was slightly higher than those from the other two stages， with R²， RMSE， and nRMSE values of 0.76， 0.31%， and 0.15%， respectively. The estimation model at the silking stage had the lowest accuracy， with R²， RMSE， and nRMSE values of 0.33， 0.27%， and 0.19%， respectively. At the same time， the spectral indices that can indicate maize LNC were obtained. They were VARI （Vegetation Atmospherically Resistant Index）， DDI （Desertification Difference Index） and EVI （Enhanced Vegetation Index） at the jointing stage； MTCI （MERIS Terrestrial Chlorophyll Index） and SIPI （Simple Insensitive Pigment Index） at the silking stage； and EVI （Enhanced Vegetation Index）， CCI （Canopy Chlorophyll Index） and NDVI （Normalized Difference Vegetation Index） at the maturity stage. Finally， the spatial distribution map of maize LNC was obtained using the model with the highest estimation accuracy at each growth stage， and its spatial distribution characteristics were consistent with the actual maize LNC conditions. However， the amount of nitrogen fertilizer had a greater impact on the maize LNC among the microplots with different treatments. The results of this study can provide a database and decision support for the nondestructive， rapid and dynamic monitoring of maize leaf nitrogen content in the black soil region of Northeast China.

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

Tropical islands are among the most biodiverse regions on Earth， with the rivers that are shorter and more diverse than continental rivers. It is crucial to assess the physical habitat health of tropical island rivers for biodiversity protection， water resources management and economic development. At present， the health of river physical habitat is mostly assessed using monitoring points and quadrats， which do not take into account representativeness， convenience， or safety. It is difficult to accurately and completely reflect the status of river physical habitat， and it is time-consuming and laborious. The physical habitat health evaluation index system of tropical island rivers was constructed applying remote sensing technique， with the major rivers in the Nandu River basin， Changhua River basin and Wanquan River Basin of Hainan Island serving as the research objects. The physical habitat health evaluation of 27 rivers in the three river basins was first and comprehensively assessed using hierarchical analysis and expert scoring methods. The results indicated that： （1）The Nandu River Basin， Changhua River Basin and Wanquan River Basin are mainly made up of forest and farmland ecosystems， with the farmland ecosystems accounting for the largest proportion in the entire basin. （2）The vegetation coverage in the riverbank zone of the entire watershed is generally high， reaching over 70%， and is primarily composed of artificial economic forests. The overall natural shoreline rate is low， with an average rate of 41.93%， and the human activities have a significant impact on the riparian zone. （3）The physical habitat evaluation of rivers in the three major basins predominantly falls into a sub-healthy state， followed by an unhealthy state. Based on the current state of ecological and environmental protection in Hainan， relevant research can provide data support for the accurate implementation of ecological protection and governance in the basin， as well as a reference for setting thresholds for relevant evaluation criteria such as water ecological assessment in tropical islands.

Obtaining the boundaries of glacial lakes quickly and accurately from massive remote sensing data is crucial for their inventory. To achieve this， an automatic extraction method based on remote sensing data is needed. This paper presents an improved instance segmentation model based on the YOLOv5-Seg network， which was applied to the automatic extraction of mountain glacial lake boundaries. The results demonstrate that the use of Coordinate-Attention （CA） enhances the network's attention to the glacial lake area. Additionally， a small target detection layer was added to the original three detection layers to improve the network's ability to detect small-area glacial lakes. By modifying the nearest neighbor upsampling method to the deconvolution upsampling method， the upsampling loss feature is solved. Combined with the transfer learning method， this approach reduces the cost of manual labeling. On average， the improved YOLOv5-Seg network achieves an accuracy that is 2.7% higher than that of the original network， reaching 75.1%， and 10% higher than that of other mainstream algorithms. Using the improved instance segmentation model of the YOLOv5-Seg network and Sentinel-2 satellite images， 10 668 glacial lakes were identified in the Hindu Kush-Karakoram-Himalayan region （HKH） in 2022， with a total area of 768.3 km². The study provides the technical basis for the automated mapping of glacial lakes for large geographical regions through the integrated capabilities of deep convolutional neural networks and multi-source remote sensing data.