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.

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.

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.

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.

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

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".

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Vegetation cover （FVC）， as an indispensable climate parameter， and the spatial and temporal evolution characteristics of long time series FVC can provide data reference for assessing the surface vegetation condition. MODIS-NDVI data were used to estimate FVC using the image element dichotomous model， and the spatial and temporal evolution characteristics of vegetation cover in Shenyang from 2000 to 2020 were explored by using trend analysis and deviation analysis， while multi-scenario simulation prediction of vegetation cover in Shenyang in 2030 was carried out based on land use data in 2010， 2015 and 2020 combined with PLUS model. The results show that （1） in time， the annual average FVC in Shenyang City increases at a rate of 3.14%/10 a，the high and medium-high vegetation cover shows an increasing trend， and the proportion of vegetation improvement area is higher than that of deterioration. （2） Spatially， the high value areas of FVC in Shenyang are mainly distributed in Shenyei New District， Hunnan District and Sujiatun District， while the low value areas are distributed in the five districts and the central part of districts and counties in the city. （3） The simulation results found that： in the historical trend scenario， the area of arable land， forest land， grassland and water area decreased； in the arable land protection scenario， the area of arable land increased and forest land decreased； in the low-carbon development scenario， forest land increased significantly. The results of the study provide a theoretical basis for the future formulation of environmental management policies in Shenyang.

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.

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.

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.

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.

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.

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.

Relative radiometric normalization is one of the main means of producing surface reflectance products from high-definition optical satellite images， the relative radiometric normalization method based on Iteratively Reweighted multivariate alteration detection is currently the most widely used method for high-resolution optical satellite image. However， the classical IR-MAD method is only applicable when there is little change in features between the reference image and the image to be corrected， and the method fails when there is a large change in features. In this paper， an improved IR-MAD algorithm is proposed by introducing a pseudo-invariant region constraint mechanism. The main idea is to use a Gaussian mixture model to extract the invariant regions between images， thus improving the quality of the pseudo-invariant points， so that effective radiometric normalization results can still be output when the feature changes a lot. The experiment was conducted using three representative sets of images， with GF-2 as the image to be corrected and Sentinel-2 as the reference image. The results show that compared to the classical IR-MAD method，（1） The pseudo-invariant points extracted by our method are mainly concentrated in the invariant regions， and the spatial distribution is more reasonable； （2） In dataset 1 representing general changes， the average coefficient of determination of our method is increased by 8.8%， while in dataset 2 and 3 representing strong changes， the average coefficient of determination of our method is 95% and 89%， respectively， while the traditional method is lower than 50% or even negative； （3） The spectral reflectance information of typical features in the processed image by our method is closer to the reference image. Therefore， our method provides a new and effective approach for relative radiometric normalization of high-resolution optical satellite images in complex situations.

Alpha approximation method is based on the time-invariant of vegetation and surface roughness that receive a great accuracy in soil moisture retrieval. However， the errors may transfer and accumulate as the extension of time scale， and the selection of different prior-information， the retrieval accuracy of this method need to be under reconsideration. This study is based on Sentinel-1 images and carried out in the Tianjun soil moisture observation network that is aimed at the conditional constraints of soil moisture retrieval through Alpha approximation method. The results indicate that： （1） As the ground measurement is used as prior information， the Root Mean Square Error （RMSE） are 0.061 m³/m³， 0.077m³/m³， and 0.090m³/m³ for the monthly， quarterly， and yearly retrieval of soil moisture through Dobson dielectric model. The error is increase with the extension of time scale. （2） As the SMAP product is used as prior information， the RMSE are 0.088 m³/m³， 0.088m³/m³， and 0.101m³/m³ for retrieved soil moisture from the same retrieval strategy， the error increase compared to the results from the ground measurement using. Therefore， the retrieval accuracy is influenced by the quality of prior information. （3） The accuracy of soil moisture retrievals based on Dobson dielectric model and Topp dielectric model is similar in this paper， the difference of RMSE between the retrievals of soil moisture is lower than 0.005 m³/m³. However， the combination of Alpha approximation method and Topp dielectric model can easily extend to the soil moisture retrieval of surface scale.

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.

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.

One of the difficulties and challenges in research on urban safety disaster mitigation and prevention is the risk assessment of urban storm waterlogging. The numerical simulation evaluation model of urban flooding by high-resolution remote sensing is useful for determining pre-disaster risk， simulating mid-disaster scenario， and determining post-disaster loss of flooding. It also has significant application value for assisting in urban planning and managing flooding emergencies. A numerical simulation model of urban flooding is constructed using the GF-7 satellite stereo photogrammetry， object-oriented classification， and hydrodynamic coupling technique， and the risk evaluation is constructed by combining water depth， flow velocity， impact area， storm frequency， and other characteristics. The model accuracy is better than 82% after testing on the middle and lower reaches of the Dasha River in Shenzhen， and when compared to historical measured data for verification， the flooding risk area is decreased by 3.36% in 2021 compared to 2017. According to the study， a high-precision DEM is essential for increasing the precision of an examination of urban storm waterlogging. The waterlogging depth accuracy of the 2.5 m resolution DEM is 5.31%，23.88%， and 58.09% higher than that of the 5 m，10 m， and 20 m， respectively， based on the results of the 1h simulation. The research results have been applied to the evaluation of sponge city in Shenzhen and achieved better effects， providing scientific means and basis for urban flood disaster risk management.

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.

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.

Soil Moisture （SM） is an important parameter for grape development， and accurate monitoring of SM in grape-growing areas is beneficial for scientifically guiding field irrigation and other measures， thereby promoting grape quality and yield. We took Jingyang County， Shaanxi Province as a typical study area， calculated six remote sensing indicators， including Normalized Difference Water Index （NDWI）， Vegetation Supply Water Index （VSWI）， Temperature Condition Index （TCI）， Crop Water Stress Index （CWSI）， etc.， based on MODIS reflectance， surface temperature， and evapotranspiration products from 2009 to 2016， preferentially selected indicators through sensitivity analysis of remote sensing indicators on SM， and constructed a comprehensive remote sensing model of SM using the classification regression tree （CART） algorithm to achieve 8-day resolution scale estimation and drought monitoring of SM at key growth stages of grape. The results showed that NDWI， VSWI and CWSI could better characterize the temporal and spatial variations of SM in the grape-growing area， and NDWI has the most timely response to SM changes and the highest correlation. The comprehensive estimation model of SM constructed using the three preferred NDWI， VSWI and CWSI indicators achieve an R² of 0.6 or more for all time phases except 121 and 169， and an R² of 0.8 or more for all time phases at mid-ripening stage. From 2009 to 2016， the drought in the grape-growing area of Jingyang County occurs mostly during the flowering stage and mid-ripening stage of grapes. This study can provide reference for drought monitoring in grape-growing areas.

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%.

The Karlik Mountains glacier located in the eastern part of Xinjiang Tianshan is a typical continental glacier， which is extremely sensitive to the response of climate change. Based on Landsat TM， ETM+ and OLI remote sensing images， DEM data and other information， the glacier boundary information was extracted for four periods of 1990， 2000， 2010 and 2020 using a combination of band ratio method and visual interpretation， and the distribution and variation of glacier area in Karlik Mountain in the eastern Tien Shan Mountains and its response to climate change during the past 30 years were studied.The results show that： （1） the glacier area showed a continuous retreat trend from 1990 to 2020， and the glacier area shrank by 28.34 km² with an average annual retreat rate of 0.73%·a^-1， among which， the retreat rate of the glacier end was the fastest after 2010. （2） As the altitude rises， the distribution of glaciers in the study area shows a trend of increasing and then decreasing， with the most glaciers distributed at altitudes of 3 800~4 600 m； the number and area of small-scale glaciers （≤0.5 km²） are increasing， while the area and number of larger-scale glaciers （≥1 km²） are decreasing； glaciers on different slopes also show different degrees of retreat， with the fastest rate of retreat on the east slope. The distribution of glaciers is characterized by more in the west and less in the east， and more in the north and less in the south； glaciers of different slopes also have obvious retreat trends， among which the retreat is the fastest in the range of 30°~35°. （3） A comprehensive analysis of the climate data in the study area shows that the change of glacier area in the study area from 1990 to 2020 is mainly related to the increase of temperature and decrease of precipitation in the period， and the increase of temperature is the main reason for the acceleration of glacier area retreat.

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.

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.

Global climate change and human activities have led to continued increases in the frequency and intensity of droughts. Recently， drought has become a key factor that affects vegetation growth and diversity， which further impacts agricultural production， ecosystem stability， and socioeconomic development. Therefore， mastering the relationship between vegetation dynamics and drought will help to reveal the physiological mechanism of terrestrial ecosystems and formulate effective management strategies. Here， we used long-term datasets of （2001~2020） Solar-Induced chlorophyll Fluorescence （SIF） and Normalized Difference Vegetation Index （NDVI） to explore vegetation changes and their linkage to meteorological drought （SPEI index） across different vegetation types in the Yangtze River Basin （YRB）. Firstly， the correlation analysis method was applied to obtain the maximum correlation coefficient between SPEI and SIF （NDVI）， and the differences of SIF and NDVI responses to meteorological drought of different vegetation types were compared and analyzed. Then we employed an improved partial wavelet coherence method to quantitatively analyze the influence of large-scale climate models and solar activity on the interaction between vegetation response to meteorological drought. The results show that： （1） from 2001 to 2020， the YRB experienced frequent droughts， with summer dryness and wetness exerting the significant impact on its annual climate； （2） SPEI exhibits a greater association with SIF than NDVI does. （3） NDVI has a longer response time （3~6 months） to drought than SIF （1~4 months）， with cropland and grassland displaying shorter response times and evergreen broadleaf and mixed forests showing longer response times. （4） There is a significant positive correlation between drought and vegetation， with a period of 4~16 months. The teleconnection factors of Pacific Decadal Oscillation （PDO）， El Niño Southern Oscillation （ENSO）， and sunspots are crucial drivers in establishing the interaction between drought and vegetation， with sunspots having the most significant impact. Overall， this study indicates that drought is an essential environmental stressor in disturbing vegetation growth over the YRB. Additionally， SIF has great potential and advantages in monitoring drought and vegetation responses. These findings have reference significance for drought prediction， early warning， and ecosystem protection planning in the YRB.

Remote sensing target detection is of great significance in fields such as environmental monitoring and circuit inspection. However， there are challenges in remote sensing images， such as large differences in target scale， a large number of small targets， high inter class similarity and intra class diversity， which lead to low detection accuracy. To solve the above problems， a remote sensing target detection model based on YOLOX-Tiny is proposed. Firstly， by improving the multi-scale feature fusion network to fully utilize shallow detail information and deep semantic information， the detection ability for small targets is enhanced； Secondly， deformable convolution is introduced at the prediction end to improve the robustness of the model to targets of different scales and shapes； Finally， the SIoU loss function is used to move the prediction box in the correct direction， further improving the positioning accuracy of the model. Experiments are conducted on remote sensing datasets DIOR and RSOD， and the experimental results show that without increasing the number of parameters， the improved model achieves a detection accuracy of 73.68% and 97.12%， respectively， which is high compared to some other state-of-the-art models， with a high recognition rate of overlapping targets and good real-time performance.

InThe movement characteristics of floating green tides are strongly influenced by wind and flow fields, making it challenging to conduct a synchronous comparison analysis. Following an analysis of data from 2015 to 2021 in the Yellow Sea of China, two quasi-synchronous high-precision data pairs from Sentinel-2 MSI and MODIS, with imaging intervals of less than 10 minutes, were identified. These exhibited algae drift deviations of less than one MODIS pixel. In order to examine the authenticity of the 10 m MSI identification results and the detection efficiency of MODIS data, this study employs a simulation in which the Algae-containing Pixel Ratio (APR) is calculated within a coverage area of 25×25 MSI pixels (equivalent to one MODIS pixel) as an aggregation parameter of green tides. The results demonstrated that the majority of green tide patches can be detected by MODIS when the APR in the simulated images is greater than 13%. In contrast, algae with an APR of less than 13%, which are primarily composed of dispersed low-aggregation green tide patches, are difficult to detect and are particularly concentrated around the Jiangsu offshore region. The uncertainty in green tide detection by MODIS data with its coarse spatial resolution is primarily due to differences in its ability to monitor low-aggregation patches. Additionally, fine monitoring of small algae patches using high-resolution images is valuable for the timely detection of the generation, extinction, and convergence of green tide evolution with better accuracy.