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.

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.

The semantic segmentation of remote sensing images by using machine learning and deep learning is an important means in the field of intelligent interpretation of remote sensing. Optical and SAR images can reflect different land surface characteristics and provide additional information for land use/cover. Effectively integrating and improving the recognition of land use/cover types is a major difficulty in remote sensing semantic segmentation. To solve these problems， a land use/cover segmentation method based on multi-source data fusion and channel correlation perception was proposed. Firstly， using Gaofen-6 optical image and Gaofen-3 radar image to produce high-resolution image sample data； Then， channel attention mechanism and spatial multi-scale correlation are introduced into deeplabv3p framework using coding-decoding architecture， and CCAMNet（Cross-channel sensing Module network） model based on full convolutional network structure is established. Finally， a comparative experiment is conducted with UNet， Deeplabv3p， SA-Gate， v-fusenet， MCANet and other semantic segmentation models on the research region data set. Moreover， it is compared with Deeplabv3p， PSCNN， MRSDC， v-fusenet， MBFNet， MCANet and other models on WHU-OPT-SAR public data set. The results show that compared with other models in the study area， the overall accuracy of the proposed model is 79.68%， which is better than other models. On the open data set， the overall accuracy is 81.8%， which is better than other models in classification accuracy and can significantly improve the accuracy of semantic segmentation， and verify the feasibility of the model in the application of semantic segmentation in remote sensing land use classification. The research results filled the gap of the lack of high-resolution land use classification dataset in the study area， and verified the feasibility of optical and SAR fusion in significantly improving classification accuracy.

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.

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.

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.

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.

Pine wilt disease is a devastating pine disease， which seriously threatens forest ecological security. Timely and reliable acquisition of the extent and severity of pine wilt disease is very important for forest management and disease prevention and control. However， pine wilt disease spreads rapidly and is difficult to control， and the traditional manual survey methods can hardly meet the demand. Unmanned Aerial Vehicle （UAV） remote sensing can quickly and accurately obtain the extent and severity of forest diseases， and provide reliable information support for forest pest control and management. In this study， UAV was used to acquire high-resolution Red-Green-Blue （RGB） visible light images. Firstly， object-oriented multi-scale segmentation algorithm was used to extract the crown of a single tree， and Vegetation Index （VIs） and texture （GLCM） features were calculated. Then the feature selection algorithm was used to optimize the feature set， and Random Forest （RF） classification and Support Vector Machine （SVM） classification algorithm were used to construct the pine wilt disease classification model based on different feature sets. Through the ablation experiment， the optimal classification model was selected and the object-oriented method was used to monitor the disease degree and spatial distribution of pine wilt disease. The results show that the vegetation index and texture features of pine canopy with different disease degrees are different on the object-oriented single tree crown scale， and the accuracy of classification results using vegetation index was better than texture characteristics （VIs RF：OA=76.52%，Marco-F1=0.77；SVM：OA=79.68%，Marco-F1=0.79）. Compared with a single feature set， the combination of vegetation index and texture features can significantly improve the classification accuracy （VIs&GLCM RF：OA=79.47%，Marco-F1=0.80；SVM：OA=85.45%，Marco-F1=0.85）， indicating that multi-feature combination can effectively improve the pine wilt disease classification. The SVM model outperforms the RF model for classification， both for single feature set modeling and combined feature set modeling. This study provides timely and reliable information to support a comprehensive grasp of the extent and severity of pine wilt disease， and helps to promote the construction of a major forestry pest control system and maintain ecological security.

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.

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.

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.

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.

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%.

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.

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.

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.

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.

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.

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.

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.

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.

Poyang Lake Wetland is an internationally important lake wetland and is of great significance to the healthy and green development of the entire Yangtze River Basin. However， in recent years， the impact of human activities and global climate change has caused rapid changes in the community structure and spatial distribution of Poyang Lake wetland vegetation， and the ecological functions are at risk of degradation. Therefore， timely and accurate knowledge of the spatial distribution of wetland vegetation communities is of great significance for the protection and restoration of Poyang Lake wetland. In view of the characteristics of seasonal changes in the Poyang Lake Wetland， this study uses the 2019 time series Sentinel-2 images as the data source and proposes a random forest classification method that combines statistical and temporal features based on the GEE platform to classify the Poyang Lake National Reserve Wetland. Typical vegetation communities were extracted. The research results show that： （1） Poyang Lake wetland vegetation is affected by periodic flooding and differences in growth phenology， and the differences in vegetation communities are more obvious in autumn and winter. （2） Sentinel-2 images based on time series can better identify vegetation communities， tidal flats and water bodies， among which time features contribute greatly to the identification of vegetation communities. （3） The overall identification accuracy of wetland vegetation communities based on statistical-temporal features combined with random forest classification algorithm reaches 86.21%， and the Kappa coefficient is 0.83. The method proposed in this article can timely and accurately extract the spatial distribution of wetland vegetation communities， and has good application prospects in wetland research. It can provide scientific acquisition and management of wetland resources in Poyang Lake Wetland National Nature Reserve， evaluation of ecological environment， and Repair provides important data support.

Rapid and accurate acquisition of cultivated land change information is of great significance to food security management. This paper aims at the problem that remote sensing semantic segmentation method has many errors and omissions due to insufficient model applicability in large-scale and high-resolution image cultivated land non-agricultural detection. Multiscale Scene Classification-Xception （MSC-Xception）， a multi-scale scene classification method for high-resolution cultivated land images based on Xception， is proposed. The convolutional attention module CBAM is embedded into the output layer of the lightweight scene classification network Xception， which has outstanding performance in cultivated land scene classification， to enhance the model's ability to extract channel and spatial features. At the same time， the problem of low separation degree and rough details of mixed scenes existing in the single-scale scene-level classification in large-scale cultivated land extraction is also overcome. Firstly， a feature fusion method of multi-scale cultivated land scene is introduced to improve the separation degree of mixed scene， and then the boundary constraint of multi-scale segmentation vector is used to achieve the boundary refinement of scene-level classification. Compared with the typical Unet， PSPNet and DeeplabV3+ semantic segmentation methods， this method can better reduce the missed detection of large map spots， and the recall rate and F1 score in the cultivated land extraction experiment of GF-2 images in Qixia District in April 2018 increased by at least 15.1 percentage points and 8.8 percentage points respectively. In the non-agricultural detection of cultivated land in Qixia District from 2018 to 2022， the recall rate of suspicious spots increased by at least 7.16 percentage points.

It is of great theoretical and practical significance to study the temporal and spatial changes of ecological quality and its driving factors in the riverbank of the Yangtze River Basin for the coordinated development of ecological environment protection and economic strategy. Based on three Landsat series remote sensing images in 2000， 2010 and 2020， this paper comprehensively evaluated and analyzed the spatial-temporal changes of ecological quality and land use in the Main Stream of the Yangtze River over the past 20 years through the RSEI and Geo Detector. The results showed that： ① The regional ecological quality in the study area showed a trend of decline first and then increase， and the overall trend of regional ecological environment quality decreased obviously from 2000 to 2010， while the ecological environment quality of some regions was significantly improved from 2010 to 2020.②The spatio-temporal variation of land surface type in the study area was obvious， especially the construction land in each county during 2000~2010.③The factor detection found that the influencing intensity of construction land， forest and grass land and cultivated land on eco-environmental quality was different in each year， but they all had strong explanatory power in the study period.④The relevant interactive detection showed that the interactions among the factors of all the years exhibit dual-factor or nonlinear enhancement， while the q values were all greater than 0.48， which indicates that the change of ecological quality further promoted by the interactions among all the factors. ⑤From 2010 to 2020， the ecological protection and restoration in the Main Stream of the Yangtze River achieved initial results. This study provides monitoring and analysis methods and scientific basis for coordinating regional land resource development and ecological environmental protection under the background of rapid urbanization.

Glacier is one of the most important freshwater reservoirs. Accurate identification of glaciers and monitoring of glacier changes are of great significance for understanding climate change and water resources management. Based on Landsat 8 images， this paper takes the Karakoram region as the research object， and uses single-band threshold method， snow cover index method， unsupervised classification， supervised classification and U-Net convolutional neural network to extract glacier boundaries. The accuracy of glacier boundary extraction results is evaluated by intersection ratio and confusion matrix. The results show that unsupervised classification and single-band threshold method have serious omissions for surface moraine-covered glaciers and glaciers in shadows， and it is easy to misclassify snow-covered mountains into glaciers. The extraction effect of K-means is the worst， with an intersection ratio of 57.69 % and a Kappa coefficient of 0.57. The supervised classification method has significantly improved the extraction effect of moraine-covered glaciers， but the extraction effect of glaciers in the shadow is not good， and the Kappa coefficient of the extraction results is above 0.70. The snow cover index method can effectively extract the glaciers in the shadow， but it is easy to misclassify the non-glacial areas in the large-scale glaciers into glaciers. The intersection ratio is 74.49 %， and the Kappa coefficient is 0.76. The U-Net convolutional neural network can extract the glacier boundary more completely， and the accuracy is significantly higher than other classification methods. The overlapping area is closest to the ground true value area， and the intersection ratio is 88.57 %， and the Kappa coefficient is 0.90. Although the U-Net convolutional neural network performs well， there are still missing points for very small area glaciers. Subsequent research can improve the accuracy by improving the network structure.

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.

Qinghai Lake is an important water body that maintains the ecological integrity of the northeastern Qinghai-Tibet Plateau and is also a natural barrier to control the spread of the western desertification to the east. And the study of the changes Qinghai Lake area is of great significance to regional ecological security and economic development. Therefore， this study aims to investigate the trends and causes of the changes of Qinghai Lake area from 1990 to 2020. With the support of Google Earth Engine （GEE） platform， Landsat data were used to monitor the dynamic changes of Qinghai Lake for winter-spring and summer-autumn in the past 30a， and the reasons for the changes were analyzed in combination with meteorological and hydrologic data. The results show that：（1） With 2004 as the inflection point， the area of Qinghai Lake showed a trend of decreasing first and then increasing during 1990 to 2020. In recent years， the area increase is most obvious. During 1990~2004， the area of Qinghai Lake decreased by 140.36 km² in winter-spring， about 3.19% of the area in 1990； and decreased by 157.547 km² in summer-autumn， about 3.56% of the area in 1990. During the period 2004~2020， the area of Qinghai Lake increased by 336.59 km² in winter-spring， about 7.89% of the area in 2004； and increased by 349.3814 km² in summer-autumn， about 8.18% of the area in 2004. （2） In terms of intra-annual variation， the change in area from winter-spring to summer-autumn was large from 1990 to 2000， with an average change value of 26.09 km²/a， and the intra-annual variation was small after 2000， with an average change value of 5.87 km²/a. （3） From 1990 to 2004， the instability of precipitation and runoff and high steady evaporation in Qinghai Lake area were the important reasons affecting the area of Qinghai Lake. High evaporation， low precipitation and low runoff together led to the decline of the area of Qinghai Lake. From 2004 to 2020， the simultaneous increase of precipitation and runoff makes the area of Qinghai Lake increase significantly， assisted by the weak decreasing trend of evapotranspiration. （4） The shoreline morphology of Qinghai Lake is constantly changing， and the most obvious changes are in Shadao Island on the east bank， Niaodao Island and Tiebuka Bay on the west bank， and the entrance of Shaliu River on the north bank. It may be caused by the sediment of runoff soil and the dynamic mechanism of lake water. The specific reasons need to be further discussed. This study has certain value for the study of lake hydrology and the response of lakes to climate change at different scales.

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 vegetation dynamic monitoring has great significance in regional and global ecological environment protection. Optical vegetation indexes such as Normalized Difference Vegetation Index （NDVI） and Enhanced Vegetation Index （EVI） have been used as important tools for vegetation monitoring for a long time. While， microwave-based vegetation index， which can detect the woody parts of vegetation and be sensitive to vegetation water content， can provide a possible complementary dataset for monitoring global vegetation. With NDVI and EVI as the reference， the changes of MPDI， MVI_B and EDVI in the microwave band in the past three years were analyzed， and the correlation coefficients between other vegetation indexes and NDVI were calculated. The results show that EDVI has a strong ability to monitor the dynamic changes of vegetation. Compared with NDVI， EDVI has a higher sensitivity to vegetation growth and water content changes， contributing to a wider dynamic range and more details. MVI_B is insensitive to seasonal changes for most vegetation types， and MPDI is suitable for monitoring medium- and low-biomass vegetation with distinct growth cycles.

Hongze Lake serves as the primary water supply source in northern Jiangsu and functions as the water storage reservoir for the eastern route of the South-to-North Water Diversion Project. The quality of its water is crucial to ensuring safe water supply and the sustainable utilization of water resources. Secchi Disc Depth （SDD） is an important index to measure water environment quality and plays an important role in water ecosystem. In this study， Hongze Lake was taken as the research area， and a remote sensing estimation model suitable for Hongze Lake SDD was constructed based on Landsat 8 OLI remote sensing data by using field measured SDD data and spectral data. The verified results were the Mean Absolute Percentage Error （MAPE） and Root Mean Square Error （RMSE） as 19.8% and 0.07m respectively. The constructed model was applied to Landsat 8 OLI images of Hongze Lake from 2013 to 2022， and the following conclusions were obtained： （1） The average inter-annual variation range of SDD was 0.18~0.25 m， with the highest value and the lowest value appearing in 2019~2020 and 2013~2014， respectively. The overall variation trend of the three lakes was consistent， with the highest SDD in Chengzi Lake Bay and the lowest in Huaihe Lake Bay. The main factors affecting the SDD of the lake area are wind speed， among which the sediment discharge is the main influencing factor of the Huaihe River Bay. （2） The monthly variation of SDD increased from January to August， and decreased month by month after August， with the highest value of 0.36 m in August and the lowest value of 0.20 m in May. The SDD of the lake area is mainly affected by wind speed. Chengzi Lake Bay and Lihe Lake Bay are obviously affected by meteorological factors， and Huaihe Lake Bay is affected by sediment discharge and shipping， and the factors are complicated.

Mitigating climate change is facilitated by the significant carbon reservoir formed through the accumulation of aboveground biomass in secondary forests. The spatial heterogeneity of Guangdong Province significantly affects the carbon sink rate of secondary forests. However， the driving factors remain unclear， which seriously constrains the accurate estimation and prediction of future carbon sink capacity. Based on the advantages of remote sensing technology in spatial and temporal monitoring， this study quantified the impacts of four major drivers on the rate of accumulation and spatial pattern of aboveground biomass in secondary forests in Guangdong Province， using high spatial and temporal resolution remote sensing data on aboveground biomass， year of planting secondary forests， and climate， and combined with scenario analyses to predict the future potential of secondary forests in Guangdong Province in terms of carbon enhancement. The results showed that， overall， stand age was the most important factor influencing aboveground biomass accumulation in Guangdong Province， however， the contributions of other influencing factors were highly spatially heterogeneous. In the Pearl River Delta （PRD） region， climate was the second most important driver， while in the northern， eastern and western regions of Guangdong， it was topography and geomorphology. The effect of soil element content is generally small in the four regions. Among the four scenarios， the maximum storage scenario could maximise carbon gain of 62.45±2.55 Tg C by 2050.This study can provide scientific reference for sustainable forest management and high-quality development.

Terrestrial ecosystem models are important tools for investigating the complex feedback mechanisms between the global carbon cycle and climate change. However， terrestrial ecosystem models are subject to great uncertainties. Constraining model parameters based on observational data is an effective technical approach to realize accurate modelling of the terrestrial ecosystem models. In order to investigate the ability of different observations and their combinations to constrain the parameters of terrestrial ecosystem models and to improve the understanding of terrestrial ecosystem processes， the assimilation of Carbonyl Sulfide（COS）， Sun Induced chlorophyll Fluorescence（SIF）， and Soil Moisture （SM） data were conducted based on the Nanjing University Carbon Assimilation System （NUCAS）. Results showed that the assimilation of COS， SIF and SM could optimize the parameters related to plant photosynthesis and soil hydrology， and improve the modelling of photosynthesis， transpiration and soil hydrological processes in the model. The joint assimilation of COS， SIF and SM can effectively improve the performance of the model in modelling total primary productivity， latent heat flux， sensible heat flux and soil moisture.

Methane contributes about one quarter to global warming and can affect the concentration of ozone， water vapor， hydroxyl groups and other components in the atmosphere. The inversion of vertical methane concentration is of great significance， and the sensitivity analysis of remote sensing detection and the selection of inversion wavelength are the basis of methane inversion. In this paper， based on the characteristics of the infrared Hyperspectral Vertical Atmospheric Sounder （HIRAS） on the Fengyun-3E star， we carry out forward simulation of atmospheric methane detection on the basis of the infrared atmospheric radiative transfer mechanism， and conduct detection sensitivity analysis and inversion wavelength selection. Firstly， the main absorption window of CH₄ and the main interference components are analyzed based on the high-resolution transmittance spectral line library （HITRAN）， and then based on the atmospheric profile background library and the instrumental parameters of HIRAS， the 1 200~1 400 cm^-1 band CH₄ and its interference elements （atmospheric temperature， surface temperature， surface emissivity，H₂O，N₂O，CO₂， F14， HNO₃ and O₃） detection sensitivity. The results show that the atmospheric temperature， surface temperature， surface emissivity， H₂O， and N₂O have a strong interfering effect on the irradiance brightness as well as the brightness temperature in the 1 200~1 400 cm^-1 band range. Finally， the information entropy principle based on the Jacobian matrix is used for the initial selection of CH₄ detection wavelengths， and combined with the results of detection sensitivity analysis， the selection of CH₄ inversion wavelengths for HIRAS detection is completed.

Fog is a kind of disastrous weather， which seriously affects traffic order and causes serious loss of life and property. The Himawari-8/AHI （H8/AHI） image with 10-minute time resolution， which provides the possibility for near-real-time fog detection， but the reflectivity of the image is greatly affected by the sun altitude angle during the day， and the conventional fog detection algorithms are difficult to adapt. This paper takes the differences in spectral characteristics and motion characteristics of clouds， fog and the surface between time series images as the starting point， uses time series images to synthesize clear sky surface， uses the background difference method to remove the surface， and uses the ratio of adjacent time series images to remove the fast-moving and rough-textured clouds in images， finally combined with the traditional cloud and fog separation algorithm to remove scattered and unseparated clouds in the image， realized the rapid detection of land fog in the daytime. The test results show that the algorithm can realize near real-time automatic daytime fog detection. The algorithm is applicable in the daytime from 9：00 to 15：00. The algorithm has high quantitative verification accuracy.The average probability of detection for 6 consecutive days in winter is 96.6%，the false alarm ratio is 9.4%，and the critical success index is 87.9%.The advantage of this algorithm is that the detection threshold is not affected by the angle of the sun's altitude. Compared with the existing detection algorithms of daytime land fog， this algorithm has higher detection accuracy. The false alarm ratio of fog detection results for 120 days from February to May in winter and spring is 3.6%，which proves that the algorithm has certain reliability and stability.

Readily available and accurate maps of population distribution are of critical importance in decision-making. In this study， a new methodology based on ensemble learning technology is introduced that leverages geospatial big data and multi-source remote sensing data for high-resolution and high precision population mapping. Population predictor variables were extracted from Tencent location big data， points of interest and remote sensing data. Using three individual machine learning algorithms （i.e. XGBoost， neural network， and random forest） and the Stacking ensemble learning method， four population prediction models were established to disaggregate the 2020 census population data of Zhejiang Province to grids with 100 m resolution. The results show that： （1） Among three machine learning algorithms， random forest has the best prediction performance. Compared to individual machine learning algorithms， the Stacking ensemble learning strategy has good generalization performance， alleviates the high-value overflow issue， and reduces prediction errors； （2） The results from the ensemble show that the high population density in Zhejiang Province located in the city's core region， with a peak value of 500 people/grid. Population density decreases in steps with increasing distance from urban centers； （3） The gridded population data from the stacking ensemble outperform the WorldPop dataset in terms of higher population density in urban centers and data integrity. This study provides new methods and technical means for rapidly and accurately population mapping in the era of big data.

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.