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.

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.

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.

Change detection refers to the technology of extracting land cover changes by comparing and analyzing multi-temporal remote sensing images acquired at different periods covering the same area. With the advancements in satellite and sensor technologies， there has been a significant increase in Earth observation data. Change detection plays an important role in various fields， including geoinformation survey and ecological environment protection. In recent years， deep learning technology has become an advanced method for change detection due to its powerful feature mining ability. This paper provides a comprehensive overview of deep learning-based change detection methods from three aspects： pixel-level， object-level， and scene-level. Furthermore， this paper discusses the practical implementation of deep learning in change detection through three research examples. Finally， the study concludes by outlining future development trends in deep learning-based change detection.

Large-scale Earthquake disasters often result in significant losses and even casualties. Making prompt assessments of the disaster situation is crucial in the aftermath. Synthetic Aperture Radar （SAR） possesses advantages such as all-weather and all-day capabilities， as well as resilience to lighting and weather conditions. Therefore， the use of SAR imagery for change detection has garnered significant attention in various fields， including post-earthquake rescue and damage estimation， flood extent detection， urbanization studies， and coastline extraction. In this context， this paper proposes a deep learning-based earthquake damage extraction method that integrates spatial and frequency domain texture features. The method demonstrates a robust capability to identify collapsed and intact structures. Using the 2023 earthquake in Kahramanmaras， Turkey， as a case study， the region severely affected by the earthquake， this research incorporates both spatial and frequency domain features into the deep learning network for classification. Experimental results show that the proposed method achieves a classification accuracy of 80.98%， significantly surpassing the original image's accuracy of 47.84%. Moreover， this accuracy is higher than using only spatial domain features （73.30%） or only frequency domain features （73.42%）. The proposed method in this study provides fundamental support for post-earthquake disaster assessment and situational awareness.

Over the past two decades， studies about the spectral invariant theory have been developed rapidly in vegetation remote sensing. The theory has been widely used in vegetation parameter measurement， canopy reflectance modeling， and biophysical parameters retrieval. A review paper titled "Photon recollision probability and the spectral invariant theory： Principles， methods， and applications" was published by the author in “Remote Sensing of Environment” in 2023 （DOI： 10.1016/j.rse.2023.113859）. The current paper provides a comprehensive overview of the background of spectral invariant theory， general principles of the theory， determination and applications of spectral invariants. Recent progresses of the theory are summarized and potential future developments are discussed. A special section is dedicated to the researches made by Chinese scholars. The goal is to provide a synthetic overview of the theory. Some new thoughts about the theory are also given in the paper.（1） The spectral invariant theory evolves from the successive orders of scattering approximation method of the multiple scattering process of photons in vegetation canopy. The theory faciliates the conversion of spectral parameters between different spectral bands and different scales （mainly between leaves and canopy） and provides new means for calculating the directional reflectance， albedo， and fluorescence escape probability.（2） Spectral invariants can be obtained through empirical methods， spectral methods， and structural methods. The spectral method is divided into single scale spectral method and spectral scaling method， and the structural method is divided into the Silhouette to Total Area Ratio （STAR） method， clumping index method， Stenberg method， and approximation method. Different methods can be cross-validated.（3） The spectral invariant theory has been applied in a number of canopy reflectance models. Based on these models， researchers have carried out a large number of inversion studies for vegetation structural parameters and physiological parameters. The principles， methods and applications of the theory can be further explored in the future.（4） New approximation methods for the spectral invariants are proposed and new formulae for the visible sunlit leaf area index and the hemispherical directional area scattering factor are summarized. Chinese researchers have made significant contribution to the development of the theory， especially in calculating the escape probability of the solar induced fluorescence using the theory.

The super-resolution reconstruction of remote sensing images is a type of method that uses image analysis methods to reconstruct high-resolution images from one or more low resolution images， in order to restore high-frequency details lost during sensor imaging， storage， and transmission， and improve the quality of remote sensing image data. The core lies in constructing a mapping relationship between high- and low- resolution images. This paper reviews the mainstream methods and representative research works in image super-resolution reconstruction and focuses on analyzing the recent advances in traditional methods and deep learning methods in the field of remote sensing image super-resolution reconstruction. The results indicates that： （1） Methods based on deep learning frameworks are the main focus and frontier of research in remote sensing image super-resolution reconstruction methods； （2） Model lightweighting and real-time performance are the main challenges faced by super-resolution reconstruction methods for multispectral remote sensing images in complex scenes； （3） There is an urgent need to construct public datasets for research on remote sensing image super-resolution reconstruction methods and to improve the evaluation index system. In addition， this paper also discusses the effects of methods based on bicubic interpolation， CNNs， GANs， and DPMs frameworks on remote sensing image super-resolution reconstruction in complex scenes through experiments.

The terrain of the “Asian Water Tower” area centered on the Qinghai-Tibet Plateau is complex and the weather is changeable. Especially in the alpine areas， ground observations are scarce. The existing remote sensing observation energy and water cycle elements lack a comprehensive systematic framework， and the accuracy is not high， making it difficult to obtain the understanding of the temporal and spatial distribution and variation characteristics of water balance in the Asian Water Tower area. Relying on the special project of "The Second Comprehensive Scientific Expedition to the Qinghai-Tibet Plateau"， systematic satellite remote sensing observation research on energy and water cycle in the Qinghai-Tibet Plateau was carried out. The development and sharing of satellite remote sensing datasets for 15 key elements of energy and water cycle were completed. The retrieval methods and technologies for each element were improved and developed. A satellite networking observation system for key elements of regional energy balance （6 types） and water cycle （9 types） in the Asian Water tower has been formed （including cloud， surface temperature， emissivity， radiation， albedo， Precipitation- Evapotranspiration， atmospheric water vapor， soil moisture， soil freeze-thaw， snow cover and snow depth， surface water change and terrestrial water storage， etc.）， and a systematic remote sensing dataset of energy and water cycle elements with high precision， spatiotemporal continuity and high spatiotemporal resolution has been produced. The systematic analysis based on this dataset indicates that the multi-year average spatial distribution pattern of surface evapotranspiration on the Qinghai-Tibet Plateau is controlled by the precipitation distribution and shows a decreasing trend from southeast to northwest. Evapotranspiration is mainly controlled by moisture conditions in most areas （arid and semi-arid regions）， and is dominated by radiation factors only in a few humid areas. The energy parameters that affect the evapotranspiration of the plateau and other factors of the water cycle， such as the downward long-wave radiation on the surface， show an increasing trend， while the surface reflectivity shows a decreasing trend， which has a significant correlation with the decreasing trend of snow coverage. In addition， key parameters of the water cycle such as atmospheric water vapor， soil moisture， lake area and land water storage all show a significant increasing trend， while snow coverage and the annual freezing days of the surface show a decreasing trend. Overall， since 2000， the Qinghai-Tibet Plateau has generally shown a trend of warming and humidification. The dataset provided in this paper is conducive to supporting the combination of remote sensing and models， and promoting the verification and improvement of models for regional climate-land surface and multi-sphere hydrology.

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.

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.

Based on six indexes of greenness， humidity， heat， dryness， salinity and soil and water conservation factors， a remote sensing ecological index URSEI suitable for arid areas was constructed by using PCA， and the tem-spatial characteristics of ecological quality changes in Shiyang River Basin from 2000 to 2018 were explored by using trend analysis and Hurst persistence analysis. The findings are as follows： （1）URSEI considers salinity and soil and water conservation indicators， and the contribution rate of the first principal component is higher than 80%， integrating the main information of each indicator， which is conducive to improving the comprehensiveness of ecological quality assessment. （2） From 2000 to 2018， the URSEI in Shiyang River Basin increased from 0.288 to 0.316， which tended to improve on the whole， mainly due to the strengthening of ecological management and control policies and the implementation of water transfer projects such as Jingdian Phase II. Among them， the area of ecological quality improvement is about 10 495.75 km²， which is distributed in a belt along Jinchang City， Liangzhou District and Gulang County； The degraded area covers 2 388.50 km²， which is concentrated in the main urban area of Liangzhou District in the middle reaches， the Longshoushan mining and the middle oasis area of Minqin in the lower reaches， and there is a risk of continuous degradation in the future. （3） In the future， the ecological quality of the basin will be mainly unchanged and continuously improved， accounting for about 72.55%， but at the same time， about 2 231.25 km² of the area will develop from improvement to degradation， distributed in Minquan Township of Gulang County， Huangyang Township of Liangzhou District， Yongfengtan Township， Jiaojiazhuang Township of Yongchang County， etc.， basically corresponding to the surrounding areas of the main urban areas of each district and county. It is the main expansion area of urbanization in the future.

Leaf Area Index （LAI） is an important parameter for studying vegetation canopy structure and physiological and biochemical characteristics. Due to the high complexity and heterogeneity of mountain’s surface and forest canopy structure， there is no unified standard for mountain LAI ground measurement methods， resulting in significant differences in ground measurement between different methods. In order to analyze the effect of various factors on the ground measurement， and improve the accuracy and reliability of LAI ground measurement data in mountainous areas， this article uses LAI2200 Plant Canopy Analyzers （LAI2200） and Digital Hemisphere Photography （DHP） to conduct LAI ground observation experiments in typical mountain forest scenes， quantitatively analyzing different optical measurement instruments， vegetation clumping effects， the impact of terrain and other factors on the LAI ground measurement in mountainous areas. The results showed that both LAI2200 and DHP optical instrument could be used to measure LAI in mountain forests. The experiment found that LAI2200 was more sensitive to LAI of coniferous forest than DHP； Regarding the impact of terrain factors on LAI ground measurement， the hinder of incident radiation by surrounding terrain fluctuations is an important factor that causes measurement errors and needs to be eliminated in the measurement； In addition， the vegetation clumping effect has a significant impact on the measurement results， and it is necessary to correct the effective LAI by introducing the Clumping Index （CI） to obtain the true LAI. This article provides effective reference suggestions for improving the accuracy and precision of LAI ground measurement in mountainous forests.

Accurately and rapidly assessing soil salinity is crucial for land quality evaluation and agricultural development. Hyperspectral remote sensing， as an effective monitoring technology， offers new avenues. Optimizing hyperspectral data processing to enhance features is critical for accurately estimating soil parameters. However， the impact mechanisms of different spectral combination treatments on soil salinity estimation need further study. This study aims to investigate the potential of hyperspectral data for estimating soil salinity under nonlinear transformation and fractional derivative combination treatments. Based on 60 typical soil samples from the Yulin area， the spectral characteristics of saline soils under different spectral transformation combinations were analyzed. The Competitive Adaptive Reweighted Sampling method （CARS） and Successive Projections Algorithm （SPA） were used to select characteristic bands as input variables. Partial Least Squares Regression （PLSR） and Random Forest （RF） models were established to compare and analyze the estimation capabilities of different spectral processing and modeling strategies for soil salinity. The results indicate that fractional derivatives， compared to integer derivatives， better reflect the absorption characteristics of saline soil spectra. Reciprocal and logarithmic transformations effectively enhance the correlation between spectral data and soil salinity information， especially in the 1905–2078 nm range. The PLSR model generally outperforms the RF model， with the CARS-PLSR achieving optimal modeling accuracy （R²=0.966）. These findings can provide a theoretical basis for the implementation of saline soil prediction and precision agriculture.

Henan is a major maize growing province in China， while the southeastern region of Henan is an important maize producing area within the province. Evaluating land suitability can optimize land use structure and strengthen the effectiveness of land management systems. This article took maize cultivation in the Henan Southeast as the research object， comprehensively considering environmental and regional characteristics， and based on the Analytic Hierarchy Process， constructed an evaluation index system for the suitability of maize cultivation and designed a corresponding Comprehensive Evaluation Index （CEI） to complete the regional suitability evaluation. This study calculated the distribution of the CEI for maize planting suitability in the Henan Southeast， and ultimately extracted data within the dryland range for analysis. It is believed that the overall suitability for maize planting in the Henan Southeastern is relatively high， with 98.21% of the dry land being very suitable or more suitable for planting maize. After analysis， this is mainly due to the complementary terrain and hydrothermal conditions in the study area， which have certain environmental advantages and are conducive to the growth of maize.

Fire threatens the safety of human life and property and causes great damage to ecosystems. The study of remote sensing response characteristics of burned area is important for the accurate extraction of area， quantitative assessment of fire damage and vegetation restoration. Based on Sentinel-1 SAR remote sensing images， the characteristics of unburned forest， burned area， buildings， water bodies were analyzed in six fire cases. The time series of burned area from one year before the fire to two years after it was analyzed. The results shows that the cross-polarization ratio and the backward scattering intensity of VH polarization are lower in the burned area compared to the unfired area， the backward scattering intensity of VH and VV polarization of buildings are much higher than those of other features， and the backward scattering of water bodies in both polarizations is very low， while the cross-polarization ratio is higher. From time series perspective， the backward scattering intensity for VV polarization shows obvious seasonal variations. In most cases， the backward scattering intensity for VV polarization is significantly higher within one month after the fire， and the cross-polarization ratio rapidly decreases. The time series variation of Normalized Burned Ratio index （NBR） calculated from Sentinel-2 MSI follows a consistent pattern with SAR images， showing obvious seasonal changes. It rapidly decreases within half a month after the fire and gradually recovers.

The classification of wetlands with high-resolution images is one of the research hotspots of remote sensing classification. Aiming at the complex mottling of high-resolution image wetlands and wetland hydrological boundaries fluctuate seasonally， the traditional classification of high-resolution image wetlands adopts the manual extraction feature interpretation method， which is time-consuming， laborious and has low accuracy. Therefore， how to achieve automatic and efficient interpretation of wetlands is an urgent problem to be solved. In recent years， with the rapid development of artificial intelligence technology， the use of deep learning to achieve high-resolution image wetland classification has become a new research direction. In order to promote the development of high-resolution image wetland classification technology， the latest research results of deep learning models commonly used in high-resolution image wetland classification， including deep neural networks， convolutional neural networks， and generative adversarial networks， are summarized， and the application and innovation of various deep learning models in high-resolution image wetland classification were analyzed and discussed， such as the application of ensemble learning and the construction of semi-supervised models. Finally， from the aspects of samples and models， the problems of deep learning in the classification of wetlands with high-resolution images and the possible research trends in the future are prospected.

As the main surface water resource carrier， lakes play an important role in regulating river water volume and meeting water demand for life， production and ecology. Similarly， artificial lakes （reservoirs） also play a key role in water supply， irrigation， power generation and ecological protection. In recent years， affected by climate change and human activities， the water volume and quality of lakes and reservoirs （referred to as "lakes and reservoirs"） have changed greatly. Long-term remote sensing monitoring can more objectively and accurately understand the temporal and spatial changes. The study combined multi-source remote sensing image data and satellite altimetry data to extract the area and water level of China's key monitored lakes （83） and reservoirs （118） from 2013 to 2022， estimated the changes in the water volume of lakes and reservoirs， and sorted out the monitoring data of key lakes and reservoirs through the monthly water quality reports issued by relevant national departments， analyzed the changes in their eutrophication levels， and finally conducted a comprehensive analysis of the two. The results show that： in terms of water volume， the water volume of China's key lakes and reservoirs showed an overall increasing trend during 2013~2022， with the water volume of lakes increasing by 10.65 km³ and the water volume of reservoirs increasing by 2.50 km³； in terms of water quality， the water quality of most monitored lakes was poor， ranging from mesotrophic to mildly eutrophic， and the number of lakes with mesotrophic to mildly eutrophic levels generally increased year by year； the water quality of reservoirs was relatively good， but the water quality of some reservoirs showed a trend of deterioration. The combined analysis of water volume and water quality change information found that the water volume of lakes and reservoirs changed greatly when they were at the mesotrophic level， and there were different correlations between water volume and water quality. Among them， the water volume and water quality changes of 33 lakes showed a negative correlation， especially Chengxi Lake and Shijiu Lake in the eastern plains， with correlation coefficients of -0.67 and -0.90， respectively. In addition， the water volume and water quality changes of nearly half of the reservoirs showed a positive correlation. By combining the water quantity and water quality monitoring data of lakes and reservoirs and exploring the relationship between the two， we can provide an important scientific basis for the comprehensive water resources management and ecological protection of lakes and reservoirs.

Emergencies such as natural disasters and armed conflicts have become significant research hotspots in the global academic community due to their sudden onset and substantial impact. An important research topic is how to efficiently and accurately assess the effects of these events and monitor the subsequent recovery processes. Nighttime light remote sensing， a dynamic subfield within remote sensing， has gained widespread attention in recent years across socio-economic and environmental studies because of its ability to reflect variations in human activities on the Earth’s surface. This study systematically reviews the application of nighttime light remote sensing data in Chinese and international research from 2012 to 2022， focusing on three key areas： armed conflicts， public health emergencies， and natural disasters. The analysis highlights research themes， methodological approaches， and interpretative frameworks. Findings indicate that nighttime light data are used to capture the impacts of emergencies through changes in human activities， the status of public infrastructure， and unexpected shifts in regional economic development. These data have been extensively applied to assess the spatial extent， severity， and recovery progress of emergencies， providing robust evidence for governments to formulate emergency response and post-disaster recovery strategies. With advantages such as accessibility， relative objectivity， and strong correlations with socio-economic factors， nighttime light remote sensing supports rapid and extensive assessment of emergency impacts. Future research should aim to develop nighttime light datasets with higher spatial and temporal resolution， enhance integration with ground monitoring， socio-economic data， and other Earth observation data， and establish a more comprehensive system for emergency monitoring and assessment. This would provide more scientific and effective support for emergency management.

Land use/cover change is an essential part of the frontier of global climate change and sustainable development. In this paper， based on Google Earth Engine （GEE） platform， we used Landsat remote sensing images of 1990， 2000， 2010， and 2020， combined with Random Forest （RF） algorithm and field survey to sequentially decode the four phases of land use/cover data in the order of build-up area， water， vegetation， farmland， and unused land and tracked the spatial and temporal patterns of land use/cover changes in the Triangle of Central China. The results show that ① In 2020， the Triangle of Central China will mainly be composed of farmland and vegetation， accounting for more than 86% of the area， with farmland and vegetation mainly distributed around the urban agglomeration， build-up area in a spatial pattern of scattered clusters mosaic； ②Over the past 30 years， the quantitative structure of land use and land-use transfer in the study area have varied， with the land use structure showing an increased continuously in build-up area and farmland， a fluctuating increase trend of vegetation and unused land， and a weak increase trend of water； and ③ The results of the analysis of the evolution of spatial clustering of land use focusing on build-up area and farmland show that the hotter and hot spots of land use degree are increasing. In general， the extreme hot spot area is gradually decreasing， while the hotter sites are on the contrary， and the hot spots are growing in trend， and all of them located in and around Wuhan city circle. The study results lay the foundation for the research on the conservation and sustainable use of land resources in ecological urban agglomeration.

Accurate monitoring and assessment of vegetation restoration effectiveness are essential for ecological conservation， sustainable development， and environmental management. Previous studies have primarily relied on remote sensing imagery， using two-dimensional monitoring indicators such as Fractional Vegetation Cover （FVC） to assess vegetation restoration effectiveness. However， these studies have focused solely on changes in vegetation coverage area， neglecting structural measures， which limits the precise understanding of ecological governance effects. To address these limitations， this study proposes the integration of LiDAR technology with two-dimensional optical remote sensing for a more comprehensive and accurate monitoring approach in ecological restoration areas. This method combines two-dimensional indicators derived from optical remote sensing imagery with three-dimensional indicators obtained from UAV LiDAR to capture both changes in vegetation coverage and structural differences， enabling comprehensive monitoring and evaluation of vegetation restoration effectiveness. To validate the feasibility of this method， four mine restoration areas in Beijing were selected as examples. Using UAV LiDAR point cloud data in 2022 and Sentinel-2 time series remote sensing imagery data from 2018 to 2022， four vegetation structure indicators （canopy height， canopy cover， leaf area index， and canopy entropy） and two-dimensional plane indicators of FVC were calculated for the study areas. Various analytical methods， including comparative analysis and trend analysis， were employed to evaluate the vegetation restoration situation in the study areas. The results indicate a significant increasing trend in vegetation coverage area in all mining areas from 2018 to 2022. However， when evaluating vegetation structure indicators， only one mining area exhibited consistency between structural indicators and two-dimensional evaluation results. This suggests that both increased vegetation coverage area and improved vegetation structure in this specific mining area. In contrast， other mining areas only showed an increase in vegetation coverage area， emphasizing the need for subsequent efforts to enhance the restoration and management of vegetation structure. The introduction of LiDAR technology alongside optical remote sensing provides a more comprehensive assessment approach， offering more accurate references for ecological restoration effectiveness evaluation and the further implementation of ecological restoration projects.

Domain adaption transfers the source domain knowledge to the target domain to improve the classification accuracy of hyperspectral image classification model for features in different scenes. The development of domain adaptation classification methods for hyperspectral images is rapid， however， there is a lack of comparative analysis for domain adaptation methods. Therefore， the domain adaptation classification methods are classified into four categories： Distribution Adaptation， Feature Selection， Subspace Learning， and Deep Domain Adaptation. In this paper， eight typical methods are selected and three standard hyperspectral datasets from Pavia Center， Pavia University and HyRANK are used to design the comparison experiments. The experimental results show that the deep domain adaptation methods are more advantageous， among which the overall classification effect and computational efficiency of the topological structure and semantic information transfer network method are the best overall.

Single tree segmentation plays an important role in forest structure analysis， tree parameter extraction and forest biomass inversion. As a low-cost and high-efficiency data source， Light Detection and Ranging （LiDAR） provides a solid data foundation for the study of forest single tree segmentation. At present， the research on single wood segmentation mainly focuses on the forest area with relatively simple structure， and the individual wood segmentation is usually realized by considering the spatial relationship between point clouds and formulating appropriate discrimination criteria. However， for forests with complex structures， there are relatively few existing single tree segmentation algorithms. In this paper， a single tree segmentation algorithm that combines kernel density estimation， digital surface model and K-means clustering methods was proposed. The results show that the method proposed in this study can significantly improve the segmentation accuracy between artificial spruce forest and natural spruce forest when dividing the spruce forest in Northwest China with Gannan Tibetan Autonomous Region of Gansu Province as the study area. Compared with the traditional K-means clustering single tree segmentation algorithm， the overall number of trees in the proposed method is increased by 32% and 15%， and the accuracy is increased by 51% and 27%， respectively， and the recall rates of 83% and 89%， and the accuracy of 92% and 55%， respectively. This method provides a new technical support for the further application of airborne LiDAR in forest ecological applications， especially for the problem of single tree segmentation in complex forest structures.

The monitoring of river runoff is of great significance to the management and utilization of water resources， but how to obtain river runoff flexibly and accurately is still a difficult problem. Due to the limited resolution of satellite remote sensing， it is difficult to accurately invert the runoff of small and medium-sized rivers. The traditional river flow monitoring technology is complex and expensive， and its application is limited in the areas without data and in the emergency monitoring of sudden disasters. Therefore， this study takes advantage of the fast and flexible characteristics of UAVs and the advantages of LiDAR to obtain terrain information with high accuracy. Based on the 3D model of UAVs LiDAR point cloud data， combined with the Particle Tracking Velocity （PTV） method， This paper presents a method of runoff monitoring for small and medium-sized rivers. In this method， the boundary line between water body and land is extracted by using the strong absorption characteristics of the near infrared band of LiDAR， and the cross section is obtained by matching and merging with the original profile of the river. Based on low-altitude UAV optical remote sensing images， the particle tracking velocity measurement method is used to calculate the river velocity， and then the river runoff is estimated by the velocity area method. After 24 UAV runoff monitoring experiments in the reach of Liancheng Hydrology Station， the following conclusions are reached： The average relative Error between the flow monitored by LiDAR and the measured flow is 8.67%， the minimum relative error is 0.46%， and the Root Mean Squared Error （RMSE） is 0.09 m³/s. MPE （Mean Percentage Error） is 0.02， Pbias （Percent bias） is 1.95%， the Nash-Sutcliffe efficiency coefficient （NSE） was 0.94， which could meet the monitoring accuracy requirements of small and medium-sized rivers in areas without data. By comparison， the monitoring accuracy of runoff using this method is significantly higher than that of Manning formula runoff estimation （RMSE， NSE）. This study demonstrates the feasibility and reliability of the unmanned aerial vehicle Lidar point cloud data runoff monitoring， and provides a new idea for the emergency monitoring of sudden disasters in areas without data and the runoff monitoring of small and medium-sized rivers.

In order to realize more accurate mangrove extraction and monitoring， four mangrove plantation areas， including the Aojiang River coast in Wenzhou City， were taken as the study area， and the distribution of Mangrove forests was extracted and accuracy verified based on the DeepLabV3+ semantic segmentation model using Sentinel-2 remotely sensed imagery data， which was applied to analyze the spatial change of mangrove forests in the period of 2019~2023. The results show that： ① the Mangrove information extraction model constructed by DeepLabV3+ network can better distinguish Mangrove and Non-Mangrove areas， with fewer mis- and omissions； ②the semantic segmentation algorithms are significantly better than traditional machine learning methods， with the DeepLabV3+ method having the highest accuracy， with an precision of 84.89% and a Kappa coefficient of 0.82； ③The growth of Mangrove forests is greatly affected by the geographical location and growth environment， and Mangrove forests in the intertidal zone of the coast or at the mouth of the sea are more susceptible to the influence of typhoons， tides， etc.， and the encroachment of mangrove forests' growth space by exotic species， such as spartina alterniflora， etc.， are all the key factors that cause the low survival rate of Mangrove seedlings and the slow growth rate. Therefore， the semantic segmentation model based on DeepLabV3+ can better recognize and extract the Mangrove forests and provide data base support for the monitoring and assessment of Mangrove forests in Wenzhou City.

Terrestrial Gross Primary Production （GPP） is the total amount of organic carbon fixed by plant photosynthesis， and it is also the start of terrestrial carbon cycles. The remote sensing data-driven GPP models can accurately monitor the spatio-temporal pattern of GPP at the regional scale. The remote sensing data-driven GPP products support the studies of terrestrial carbon cycles， ecosystem responses to climate change and ecosystem service. However， large discrepancies in absolute magnitude， spatial distribution， and interannual variability in remote sensing data-driven GPP models lead to a large uncertainty in the estimation of global GPP. The uncertainty mainly stems from the fact that different researchers have adopted different modeling frameworks and assumptions， different remote sensing data sources， and different research periods， and thus researchers have come up with diverse conclusions. For this reason， it is necessary to summarize the results of existing representative studies to form a knowledge base and understanding of the remote sensing data-driven GPP models and their applications at the macro-scale level. Meanwhile， this study also discusses the common problems in current remote sensing data-driven GPP models and provides an outlook for future model developments.

It is important to analyze the change of the quantity and spatial pattern of cultivated land for evaluating the current situation of regional food security and sustainable utilization of cultivated land resources. Based on the second national land survey and change data， this paper analyzes the changes in the quantity and spatial distribution of cultivated land in Northeast China from 2009 to 2018 by means of spatial analysis and trend analysis， and reveals the pattern of newly added cultivated land and its physical geographical characteristics. The results showed that： （1） the cultivated land in Northeast China increased slightly from 2009 to 2018， and the growth rate was slower than before； （2） The newly added cultivated land was mainly distributed in the west Liaohe Plain， the west Songliao Plain and the Sanjiang Plain， mainly from the reclamation of forest grassland and unused land； （3） In Northeast China， the water and heat conditions of newly added cultivated land were slightly better than those of traditional cultivated land， but the situation was opposite in Sanjiang Plain and Lesser Hinggan Ling-Changbai Mountains. Although the newly added cultivated land is mainly distributed in the plain area， the phenomenon of cultivated land going up the mountain still appears in some areas. At the same time， it was found that the soil fertility of newly added cultivated land in eastern Inner Mongolia and Songliao Plain was slightly weaker than that of traditional cultivated land. The study suggests that the natural conditions of newly added cultivated land are different from those of traditional cultivated land. Considering the ecological risks brought by cultivated land reclamation， quality assessment and monitoring of newly added cultivated land should be strengthened in the future， especially the risks of economic value-added and ecological degradation of cultivated land reclamation should be weighed， so as to promote the orderly utilization and sustainable development of cultivated land resources.

The continuous changes in land use patterns have profound impacts on the ecological environment and socio-economic development of the Yellow River Basin. Understanding the dynamic changes in land use in the middle reaches of the Yellow River Basin can provide a scientific basis for achieving sustainable development in the Yellow River Basin. This study is based on four periods of Landsat remote sensing data from 1995， 2005， 2015， and 2023 in the middle reaches of the Yellow River Basin. Obtain the spatial distribution of land use in the demonstration area through Support Vector Machine （SVM） and maximum likelihood method classification， and analyze the characteristics of land use change in the study area from 1995 to 2023 using quantitative indicators such as land use change and transition matrix. The Markov model was applied to predict the land use changes in 2025 and 2030. By establishing the FLUS-Markov model， the land use changes in the study area in 2025 and 2030 were predicted. For the six types of land use changes （forest， grassland， wetland， cultivated land， construction land， and unused land）， the results show that： （1） Over the past nearly 30 years， cultivated land and forest land have decreased by 8 600 km² and 6 400 km² respectively， while the area of construction land has significantly increased by 7 500 km²； （2） The transfer directions of land use types are diverse， mainly from cultivated land to construction land and vegetation， and the landscape of each land type tends to be balanced in spatial distribution， with coordinated urban development； （3） Between 1995 and 2023， the number of forests， grasslands， and construction land showed an upward trend， while the number of wetlands， cultivated land， and unused land showed a downward trend； （4） In the next 10 years， there will be significant changes in land use in the middle reaches of the Yellow River， as humans require more land for construction.

Underground coal fires are regarded as a global disaster "without geographical boundaries"， not only resulting in substantial waste of coal resources but also posing serious threats to ecological environments and the safe development of society. Remote sensing technology has demonstrated unique advantages in the long-term detection and identification of underground coal fires， providing critical technical support for monitoring and remediation efforts. Focusing on the typical surface response characteristics induced by underground coal fires， this paper elaborates on the fundamental principles of remote sensing detection and identification in coal fire areas， systematically analyzes and reviews the existing remote sensing-based methods for detecting and identifying underground coal fire zones， highlighting the strengths and limitations of each approach. Building on this analysis， the paper discusses the existing research gaps and provides a forward-looking perspective on the existing challenges and future development directions in this field.

As an important vegetation ecosystem， the study of phenological changes in grasslands is important for a comprehensive understanding of global climate change and ecosystem carbon cycling. Inner Mongolia has the most important grassland resources in China. In this study， we employed the double logistic method to extract the key phenological parameters， including the start （SOS）， peak （POS）， end （EOS）， and length （LOS） of the growing season， of Inner Mongolia's grasslands using GIMMS NDVI3g data over a period of 34 years （1982~2015）. Subsequently， we analyzed the spatial and temporal variation of these parameters， as well as their response to meteorological factors and the impact on productivity. Our findings revealed that the SOS in Inner Mongolia grasslands was mainly concentrated from early May to early June， while POS was primarily concentrated from early July to early August. Moreover， the EOS mainly occurred from mid-October to early November， while LOS ranged from 140 to 170 days. We observed that SOS， POS， and EOS were all dominated by the trend of advancement， with the proportions of extended and shortened LOS being relatively similar. In addition， we found that SOS and POS were jointly influenced by temperature and precipitation， while EOS was mainly influenced by precipitation and less by temperature. Our analysis further indicated that an advancement of SOS， postponement of EOS， and extension of LOS would result in a decrease in GPP， while an advancement of POS would lead to an increase in GPP.

Dynamic monitoring of ecological quality is of great significance for achieving regional sustainable management and development. Based on Landsat 5/TM images and Landsat 8/OLI images， a remote sensing ecological index was constructed using the Google Earth Engine platform to achieve a dynamic evaluation of ecological quality in the Daxing'anling Mountains region from 1986 to 2020. （1） Greenness and humidity have a positive impact on ecological quality， while dryness and heat have a negative impact on ecological quality. Heat and greenness have the greatest impact on ecological quality； （2） From 1986 to 2020， the distribution of ecological Mass distribution in the Daxing'anling Mountains changed greatly. Compared with 1986， the phenomenon of low or high ecological quality in large areas decreased， and the distribution was more uniform， and the ecological quality was more stable； （3） From the relevant statistics of the changing areas， it can be seen that the ecological quality change areas in the Daxing'anling Mountains region account for about 96.4% of the total area， with significant changes in ecological quality； （4） The ecological quality of the severely burned areas in the Daxing'anling Mountains shows a trend of first decreasing and then increasing， but the overall ecological quality of the Daxing'anling Mountains region fluctuates greatly. On the whole， the distribution of ecological quality is more stable， there is no phenomenon of local too low or too high， and the level of ecological quality is gradually improved.

High resolution optical images are susceptible to imaging conditions and changes in ground features， resulting in geometric and radiative distortions which are difficult to ignore. Traditional registration methods are difficult to ensure accuracy and stability. Therefore， this study fully utilizes the advantages of phase congruency in the geometric structure expression of images， proposes a registration algorithm based on multi-directional phase congruency， uses Log-Gabor filter to calculate the phase congruency and minimum moments of images in different directions， further extracts image corners through multi threshold constraints， and constructs descriptors that are weakly sensitive to radiation and rotation to achieve complex radiation Matching homonymous features in geometric distortion scenarios， and finally using matching features to solve local registration models. Multiple experiments based on GF-1/2/6/7 images have demonstrated that the proposed algorithm can achieve high-precision registration and is suitable for domestic high-resolution optical image registration tasks from different sources， scenes， and resolutions.

The identification of urban functional zones can assist in the decision-making process in urban construction. This paper proposes a multi-source scene feature fusion method utilizing the Transformer model for urban functional zone identification. Firstly， the Traffic Analysis Zone （TAZ） is constructed based on the road network. The graph structure of POI （Point of Interest） data is created using the Delaunay Triangulation （DT）. Additionally， remote sensing data is utilized to obtain the corresponding image objects for each TAZ. Subsequently， the POI graph structure is processed using a Graph Convolution Network （GCN） to extract social scene features. Meanwhile， the natural scene features of remote sensing data are obtained through encoding with ResNet-50. Finally， the multi-head attention mechanism of Transformer decoder is utilized to fuse multi-dimensional feature vectors， facilitating accurate identification of urban functional zone with SoftMax. Taking the main urban area of Shenyang as an example， multi-source data such as OSM （Open Street Map）， POI and remote sensing data in 2021 are used as experimental data. The results indicate that the overall accuracy and Kappa coefficient of this method are 82.2% and 70% respectively. Furthermore， the Kappa coefficient is at least 18% higher than that of the single data method and at least 9% higher than that of other fusion methods. This study innovatively employs the Transformer model to integrate social and natural scene features， effectively addresses the challenge of combining diverse features from multiple sources into an integrated representation， and provides a new technical approach for urban functional zones identification.

Under the background of the “Four Million Mu” ecological project and “the community of life is the mountain， water， forest， field， lake， grass and sand”， this paper studies the habitat quality and carbon storage and influencing factors under the land use change of the Aksu River Basin and typical areas in the basin-- the Kekeya greening project， so as to provide a basis for the ecological and environmental protection policy of the Aksu River Basin， provide guidance for land use， carry forward the spirit of Kekeya， and promote the sustainable development of the Aksu River Basin. Based on the land use data from 2000 to 2020， the Habitat quality and Carbon storage module in the InVEST model were used to explore the spatial and temporal variation patterns and influencing factors of Habitat quality and Carbon storage in the Aksu River Basin from 2000 to 2020. The results show that： （1） From 2000 to 2020， the Habitat quality of the Aksu River Basin remained stable on the whole， and there was a slight increase， and the areas with better Habitat quality were distributed in the upper reaches of the basin and oases on both sides of the basin， mainly forest land and grassland land use types. In the past 20 years， the Habitat quality of Kekeya Greening Project Area has shown a steady upward trend. （2） The Carbon storage in the Aksu River Basin showed a stable growth trend during the study period from 2000 to 2020， with a total increase of 13.21×10⁶ t， and the areas with high Carbon storage values showed the distribution characteristics of "northern central cluster and southern band". The Carbon storage in the Kekeya greening project area showed an increasing trend， which was consistent with the overall trend of the Aksu River Basin. （3） Habitat quality and Carbon storage presented a significant synergic relation. The influencing factors of regional Habitat quality and Carbon storage were analyzed by using geographic detectors， land use type was the main influencing factor of Habitat quality and Carbon storage， and the interaction of influencing factors showed two-factor enhancement or nonlinear enhancement.

Black and odorous water is an extreme water pollution phenomenon that is a common problem in many cities， seriously affecting the well-being and satisfaction of residents. Therefore， addressing the issue of urban black and odorous water is urgent. Scientifically monitoring the urban black and odorous condition of water is the first step in the remediation of black and odorous water， providing accurate targets for subsequent assessment. Compared to traditional ground-based monitoring methods， remote sensing is a more effective way to discover black and odorous water， allowing for a wider and more rapid identification of black and odorous water areas and timely comprehensive representation of the spatiotemporal evolution of black and odorous water. This paper reviews the main research advances in remote sensing monitoring of urban black and odorous water. First， it briefly introduces the overview of black and odorous water， summarizes the remote sensing feature， and common data sources for urban black and odorous water. Second， it discusses in detail the methods for remote sensing identification and classification of urban black and odorous water and compares the applicability and advantages and disadvantages of each method. Finally， it summarizes the current status of remote sensing research on urban black and odorous water and its shortcomings and looks forward to future development directions. The aim is to provide reference and thinking for related research and better decision support for the long-term management of black and odorous water.

GRACE satellite has opened a new era of quantitative retrieval of groundwater change by remote sensing， but it has the problem of low spatial resolution. High-resolution groundwater observations will significantly improve the accuracy of local-scale hydrological process understanding， thereby offering essential data support for the development of scientifically based groundwater management policies. After sorting out the characteristic factors such as precipitation， air temperature， evapotranspiration， surface temperature， normalized vegetation index and soil water in the Yellow River Basin， partial least squares regression method was used to screen the characteristic factors respectively from January to December， and the optimal monthly characteristic factor subset was constructed. Then， the random forest algorithm was used to downscale the groundwater data of the Yellow River Basin from 0.25°× 0.25° to 1 km×1 km， and compared and verified with the measured groundwater level data. The results show that： （1） Except evapotranspiration and surface temperature， the importance of other factors changes with the change of month； （2） In the time series， the correlation coefficient and Nash coefficient of groundwater data before and after downscaling are as high as 0.95， and the root-mean-square error is 3.17 mm； （3） Spatially， compared with before downscaling， the correlation coefficient between the change data of groundwater reserves and the measured groundwater level after downscaling increased by 47.67%. The research results can meet the demand for high-resolution groundwater data in practical applications， and provide reference for the feature factor screening of groundwater downscaling research.

The next-generation satellite LiDAR systems， including the Ice， Cloud， and land Elevation Satellite-2 （ICESat-2） and the Global Ecosystem Dynamics Investigation （GEDI）， offer unique advantages in estimating forest canopy height. The fusion of these two LiDAR datasets not only increases the sample size for canopy height retrieval but also allows for spatial complementarity between different datasets. First， the Random Forest-Recursive Feature Elimination （RF-RFE） method was used to select photon feature parameters. Subsequently， five fusion models—Stepwise Linear Regression （SLR）， Random Forest （RF）， Light Gradient Boosting Machine （LightGBM）， Random Forest with Stepwise Linear Regression （RF-SLR）， and Particle Swarm Optimization Random Forest （PSO-RF）—were analyzed for their applicability. The optimal model was selected to construct a point-scale canopy height dataset， which was then combined with multi-source remote sensing imagery to map the canopy height in Qilian Mountain National Park. Finally， the retrieval results were compared with existing canopy height products using GEDI footprint data and field survey data. The results showed that： （1） the Particle Swarm Optimization Random Forest （PSO-RF） model provided the best fusion performance （R² = 0.71； RMSE = 3.15 m； MAE = 2.66 m）； （2） the retrieval model based on PSO-RF fusion of point-scale canopy height data achieved the highest accuracy （R² = 0.56； RMSE = 3.02 m； MAE = 2.38 m）； （3） compared to existing canopy height products， the retrieval results demonstrated higher accuracy （based on GEDI footprint data： R² = 0.43； RMSE = 4.50 m； MAE = 3.59 m）， and the errors were smaller when compared to field survey data （R² = 0.36； RMSE = 3.15 m； MAE = 2.56 m）. The findings reflect the spatial distribution pattern of vegetation in Qilian Mountain National Park and provide scientific support for forest resource management， carbon sequestration estimation， and ecological resource conservation.