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.

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.

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.

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.

Solar-Induced chlorophyll Fluorescence （SIF）， a proxy for vegetation photosynthetic activity， has gained widespread applications. This review synthesizes the principles， progress， and key frontiers in satellite SIF remote sensing. Firstly， we introduced the SIF retrieval principles and algorithms at both ground and spaceborne platforms. The SIF retrieval methods can be divided into two categories： physically-based inversion and data-driven algorithms. The accurate separation of SIF from reflected radiance in upwelling radiation is the key challenge. The current ground-based retrievals are still limited by spectrometer resolution and Signal-to-Noise Ratio （SNR）， developing instrument-agnostic， high-robustness algorithms remains a research priority. Data-driven approaches dominate satellite SIF retrievals. However， huge uncertainties persist in red-band SIF retrieval， demanding transformative algorithmic breakthroughs. Second， we analyze global SIF satellite developments over 30 years， highlighting China’s rapid progress （e.g.， successful experiments with TanSat and Goumang）. Nevertheless， gaps persist in satellite longevity， data sharing， and scientific utilization compared to international counterparts. The next-generation TanSat-2 （to be launched in 2026） is expected to revolutionize SIF remote sensing， offering 2-km resolution， global daily coverage， and dual-band （red/far-red） SIF data—resolving critical limitations of low resolution， SNR， and revisit frequency. Finally， we investigated the progresses of spatiotemporal fusion of SIF satellite data. Machine Learning （ML）-based simulation methods have achieved high-precision simulation of SIF data and have been widely used. However， the ML-based SIF datasets represent the modeled signals driven by reflectance and meteorology， not observations. Spatial downscaling of satellite SIF products preserves observational fidelity despite lower spatiotemporal continuity than ML counterparts. Emerging multi-sensor， long-term （1995—2024）， 0.05°-resolution downscaled products hold potential to accelerate SIF science applications. Therefore， despite the inherent challenge of high-precision retrieval of weak SIF signal， advances in payload technology and quantitative remote sensing are rapidly transforming SIF monitoring capabilities. China’s SIF remote sensing program （TanSat-2） is positioned to play an increasingly pivotal role in guiding global SIF science and applications.

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.

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.

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.

Soil Organic Matter （SOM）， a vital component of the soil solid phase， provides essential nutrients for plant growth and serves as a key indicator of soil fertility. Recent advancements in remote sensing technology have introduced novel approaches for efficient SOM estimation and mapping， yet challenges persist due to environmental interference and data complexity. This review systematically examines the applications of multispectral and hyperspectral data in SOM inversion and mapping， alongside critical data processing methodologies. Comparative analyses demonstrate that laboratory-acquired spectral data under controlled conditions exhibit significantly higher model accuracy and robustness compared to field-collected data， attributed to stable measurement environments. Feature selection and extraction， particularly for hyperspectral datasets， enhance inversion precision by mitigating data dimensionality and multicollinearity. Ensemble modeling frameworks integrating machine learning and deep learning outperform single-model approaches by effectively characterizing the nonlinear complexity of soil systems. Multi-temporal datasets further improve predictive capabilities by incorporating seasonal vegetation dynamics and temporal evolutionary patterns. However， optical data remain susceptible to atmospheric disturbances， especially in cloud-prone regions such as southern China， while microwave remote sensing emerges as a complementary solution for its all-weather operability and topographic adaptability. Future research should prioritize multi-source synergy strategies， including optical-SAR synergies， multi-sensor platform integration， and physics-informed machine learning to address confounding factors like crop residue cover and soil moisture. Advanced preprocessing techniques， such as wavelet analysis and blind source separation， are essential for isolating soil-specific spectral signatures. Spatiotemporal modeling frameworks that integrate soil types， agronomic practices， and climatic variables will enhance prediction generalizability. Concurrently， developing interpretable artificial intelligence models and geographically adaptive spatial interpolation methods is crucial to ensure scientific rigor and global scalability. This study provides theoretical and practical insights for leveraging multi-source remote sensing in precision agriculture and sustainable land management.

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.

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 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.

The Qinba Mountain region， encompassing the transition zone between North and South China， holds significant ecological importance. Investigating the phenological changes in vegetation and their response to climate change in this region is crucial for understanding the complexity of the ecological environment in the transitional zone and reconstructing historical climate patterns. Based on MODIS MCD12Q2 data and meteorological remote sensing data， this study employs trend analysis and correlation analysis methods to explore the spatiotemporal characteristics of vegetation phenology in the Qinba Mountain region from 2001 to 2020 and its relationship with climate change. Results indicate that the start， end， and length of the growing season in the Qinba Mountain region exhibit distinct vertical zonal distribution characteristics from east to west. The start of the growing season is primarily distributed from mid-to-late March to late April （70-110 days）， while the end of the growing season is concentrated from late October to late November （290-320 days）， with the majority of growing seasons falling between 180 and 260 days. Over the 20-year period， the overall characteristics of phenological interannual variation in the Qinba Mountain region show an average advancement of 0.38 days per year. The end of the growing season exhibits an average delay of 0.43 days per year. The length of the growing season displays an average extension of 0.80 days per year. Significant trend of change. Regarding the time lag response to climate factors， the start of phenology in the Qinba Mountain region shows the highest correlation with monthly temperature and potential evapotranspiration without significant time lag effects， while precipitation exhibits a time lag of approximately 1.73 months. Altitude to some extent determines the response relationship between the start of phenology and various meteorological elements in the Qinba Mountain region.

Forest ecosystems play a crucial role in regulating climate， maintaining water and soil， and balancing carbon. However， in recent years， this system has been increasingly disturbed by climate change and human activities， making precise and timely forest change monitoring urgently needed. Remote sensing technology， with its advantage of multi-temporal resolution data and automated processing capabilities， has become a key means for forest change detection. This paper focuses on multi-temporal resolution remote sensing change detection methods， systematically reviews and compares two types of technologies： bi-temporal and time series remote sensing. Bi-temporal change detection includes manual visual interpretation， traditional machine learning， and deep learning techniques； time series include research on temporal trend analysis， dynamic change monitoring， and multi-algorithm integration. By summarizing the related problems of deep learning and multi-modal time series data in practical applications， relevant solutions are proposed， providing references for improving the accuracy of forest change detection.

In this study， a comprehensive overview of snow avalanche hazards occurred in the past in Xizang area was reviewed first， followed in-depth analysis on the spatial distribution and main driving factors of snow avalanche hazards in the Xizang mountain region. Snow avalanche-prone areas for the study area were then mapped based on the spatial distribution of snow cover and DEM （Digital Elevation Model） data， and were validated using in-situ observations in southeastern Xizang. Results indicated that there are the highest frequencies of avalanche occurrences in southeastern Nyainqentanglha mountains and southern slope of the Himalayas. In the interior of plateau， avalanche occurrence is constrained due to less precipitation and flatter terrain. The perennially snow avalanche-prone areas in Xizang account for 1.6% of total area of the plateau， while it reaches 2.9% and 4.9% of total area of Xizang in winter and spring， respectively. Snow avalanche hazards and fatalities present increasing trends under global climate warming due to more human activities at higher altitudes. In addition to continuous implementation of engineering prevention and control measures in the key regions， such as in Sichuan-Xizang highway and railway sections， enhancing monitoring， early warning and forecasting services are important to prevent and mitigate avalanche hazards in the Xizang high mountain regions.

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.

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.

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.

In response to the challenge of significantly degraded image quality and difficulties in target detection caused by heavy fog during typhoon weather， as well as the limited generalization of existing research in power scenarios， an improved algorithm that integrated dark channel defogging with YOLOv8 is proposed. The proposed algorithm employs a two-stage processing technique. Initially， color adaptive defogging is applied based on an analysis of the image's color distribution using cumulative distribution functions and the dark channel algorithm to enhance the visibility of targets within foggy environments. Subsequently， to further enhance detection accuracy for multi-scale targets in remote sensing images，A small target detection layer is introduced into the YOLOv8 network architecture. This addition facilitates deeper feature extraction for small targets while employing MPDIoU instead of CIoU to reduce computational complexity. Experimental results demonstrate that the proposed algorithm improves detection accuracy for power towers and wind turbines by 8.1% and 3.9%， respectively. These findings validate both the feasibility and effectiveness of the proposed algorithm in processing foggy remote sensing images and recognizing targets， thereby providing reliable technical support for defogging operations on such images and identifying large-scale outdoor power facilities.

The previous methods for hyperspectral data classification only focus on the extraction of spectral features， which often wastes some valuable spectral spatial information and leads to unsatisfactory classification results. In view of this， this paper proposes a method that combines Principal Component Analysis （PCA）， guided filtering and deep learning architecture into hyperspectral data classification. First， PCA， as a mature dimension reduction architecture， can effectively reduce the redundancy of hyperspectral information； Then， guided filtering is used to provide a simple and effective channel to obtain spatial dominant information； Finally， the stack Autoencoder model is used as a deep learning architecture to effectively process deep level multi feature image data. Train and test the algorithm using two common datasets， and then use a third common dataset to test the models trained on the other two datasets. The experimental results show that the proposed GF-FSAE algorithm achieves classification accuracy above 99%， demonstrating good classification performance and generalization ability. Compared to the CNN-AL model， the algorithm’s accuracy is slightly higher， verifying the superiority of the spectrum-spatial hyperspectral image classification framework。

It is of great significance to have a detailed and accurate inventory of anthropogenic CO₂ emissions and to strengthen the effective control of CO₂ emissions in order to achieve the carbon peak and neutrality targets. Therefore， this study evaluated the reliability of EDGAR and ODIAC global anthropogenic CO₂ emission inventories in China and developed a high-accuracy and high-resolution estimation model for anthropogenic CO₂ emission based on multi-source remote sensing data and the Random Forest algorithm. The anthropogenic CO₂ emissions from 2005 to 2020 at the resolution of 1 km×1 km were estimated and the clustering patterns of anthropogenic CO₂ emissions were explored using the spatial autocorrelation. The spatial and temporal variations of anthropogenic CO₂ emissions in China and key areas of clustering patterns were then analysed. Results show that the estimation model of anthropogenic CO₂ emissions in China can effectively integrate the advantages of global inventory and remote sensing data， providing valid support for the spatial and temporal estimation of anthropogenic CO₂ emissions at a fine scale； from 2005 to 2013， China's anthropogenic CO₂ emissions increased dramatically from 5.53 billion tonnes to 10.73 billion tonnes； the growth rate of CO₂ emissions declined in 2013-2020， with changes tending to flatten out； China's overall anthropogenic carbon emissions demonstrate regional characteristics of "high in the east and low in the west， high in the coastal and low in the inland"， clustering in the focus areas of Yangtze River Delta， Beijing-Tianjin-Hebei region and western region； the characteristics of anthropogenic carbon emissions in different regions are closely related to their industrialization process， level of economic development， scale of counties and so on.

In the process of obtaining agricultural planting structure through remote sensing images， the uncertainty of remote sensing images itself and the classification process will inevitably reduce the accuracy and reliability of mapping results. As the basic unit of agriculture， parcels have the advantages of classification accuracy and uncertainty control. However， current remote sensing classification uncertainty research methods mostly use pixels as the basic unit， which is difficult to directly apply to parcels crop classification. Therefore， using parcels as the basic spatial unit and selects information entropy as the posterior uncertainty evaluation index to carry out crop classification and uncertainty analysis experiments in the Ningxia Irrigation District of the Yellow River. With the help of temporal characteristics and multiple value characteristics inside the parcel， the influence effects of random uncertainty and fuzzy uncertainty in remote sensing data on posterior classification uncertainty are analyzed. The experimental results show that： （1） Compared with pixel-scale classification， parcel-scale classification can effectively weaken posterior uncertainty； （2） Random uncertainty has a significant impact on posterior uncertainty and is significantly correlated with temporal characteristics； （3） The fuzzy uncertainty introduced by mixed pixels at the edge of the parcel and internal mixed heterogeneity amplifies the random uncertainty and further affects the posterior uncertainty. Targeted reduction measures can effectively reduce classification uncertainty， and the reduction effect is basically consistent with the correlation analysis results. The research results can provide ideas and methods for subsequent improvement of crop classification process and uncertainty control.

The Liaohe Estuary coastal wetland is the northernmost estuary wetland in China， which is an ideal breeding and migration station for many kinds of waterfowl. In recent years， several ecological restoration projects have been carried out to improve the habitat quality in this region. Accurately mapping the landcover types using remote sensing is very important for efficiently evaluating wetland habitat quality and restoration effectiveness. However， most of the classification methods in the Liaohe Estuary were object-oriented， and the mapping results were not fine enough and needed to be updated in years. The applicability of pixel-level method and dense time-series information in this region needed to be further evaluated. This paper relied on Google Earth Engine （GEE） platform， utilized Sentinel-2， Sentinel-1， and topographic multi-source data to extract the features including spectral indices， texture， topography， backscattering， and phenology from the dense time-series vegetation indices. Multi-year sample datasets were generated by field sampling and sample migration， and the pixel-level fine classification mapping from 2018 to 2022 was carried out based on the random forest model. The effects of different features on the classification accuracy were also evaluated. The classification method combining GEE and dense time-series information got an overall classification accuracy of 95.77%. Adding phenology features improved the accuracy most obviously， especially for the mixing between suaeda salsa and reeds， rice， or aquaculture ponds. Adding texture and backscattering features significantly improved the accuracies of aquaculture ponds and construction land. In the last five years， aquaculture ponds decreased while the mudflat and suaeda salsa expanded， which indicated the effects of ecological restoration project. The results provide data and technology supports for analyzing the spatial-temporal changes and driving mechanism of coastal wetland， which is of great significance for strengthening the protection and restoration of wetland ecosystems.

Above Ground Biomass （AGB） is an important indicator of grassland ecosystem function and grassland productivity. Accurate estimation of AGB is of great significance for grassland management and ecological environment assessment. Taking part of grassland in Xilinhot， Hulunbuir and Ordos grassland of three different types as the research area， based on UAV multi-spectral data， LiDAR data and field measured sample data， multiple texture and vegetation index were obtained， and different feature combinations were obtained through various feature screening methods. Five regression analysis algorithms， including Random Forest（RF）， Multiple Linear Regression（MLR）， BP neural network（BP）， Support Vector Regression（SVR）， and Long Short-Term Memory neural network（LSTM）， were used to construct a grassland AGB estimation model， and the optimal estimation model was obtained after comparison and evaluation， and the spatial biomass estimation was carried out. The results indicate that： （1） The feature combination selected by the feature importance method， including spectral indices and the measured average plant height （Mean Height） within the sample plots， achieved high accuracy in multi-model comparisons for grassland AGB estimation； （2） The Random Forest model outperformed other models in estimation accuracy. Using the coefficient of determination（R²），Root Mean Squared Error（RMSE），and Mean Absolute Error（MAE） as evaluation metrics，the test sample achieved an R² of 0.859， with RMSE and MAE values of 35.17 g/m² and 28.22 g/m²， respectively. The study demonstrates that integrating multi-source remote sensing features with machine learning algorithms can effectively overcome the limitations of traditional AGB estimation methods. The Random Forest model based on optimized feature combinations provides a reliable methodological reference for accurate grassland AGB estimation.

Approximately 60% of global methane emissions originate from anthropogenic sources， making their effective control a critical aspect of greenhouse gas reduction efforts. The energy sector is a major contributor to anthropogenic methane emissions， with emission events traceable to specific facilities and characterized by a heavy-tailed distribution of emission rates. High-resolution hyperspectral satellite remote sensing data enable methane emission monitoring at the point-source scale and facilitate attribution to specific facilities. A high-resolution remote sensing monitoring system for methane point sources in the global energy sector has been developed based on hyperspectral retrieval methods and a WebGIS platform. The system comprises a point-source retrieval algorithm， a methane emission retrieval dataset， and a monitoring platform. By the end of 2024， effective monitoring of 573 methane emission events worldwide has been achieved. The system is designed to detect sudden methane emissions from coal mining and oil and gas extraction and storage， enabling facility-specific attribution. Support and validation data for emission source identification and estimation in the energy sector are provided， contributing to global methane reduction efforts.

The interaction between human activities and land cover has important impacts on ecosystems. A study was conducted on the land cover of Shanxi Province to estimate the Human Activity Intensity Index of the Land Surface （HAILS） for the years 2015 and 2020. Spatial analysis was employed to explore the spatial and temporal distribution characteristics of the HAILS in terms of spatial pattern， slope and water flow paths， and correlation indices were used to discuss the validity of the HAILS and explore the influencing factors of its changes. The results indicate that the areas with higher HAILS from 2015 to 2020 were predominantly distributed in the basin and along the Fen River， and the percentage of areas with HAILS values greater than 20% increased in all slope bands. The water flow path results show that the HAILS values of the Fen River and other rivers decreased significantly after 2 km， and the HAILS values of the Fen River were highest both before 20 km and after 28 km.The HAILS was significantly correlated with the actual water consumption and the first， second and third GDP， but weakly correlated with the population density， the GDP and the night light data. Consequently， economic development， industrial structure and ecological protection policy are identified as the primary factors influencing the change of human activity intensity.

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.

Forest canopy closure is a crucial parameter in forest resource inventory that plays a significant role in evaluating and monitoring the stability of forest ecosystems. With the continuous development of remote sensing technology， the estimation of large-scale forest canopy closure using multi-source remote sensing data has become a hot research topic. In this study， a regression model was constructed using machine learning algorithms based on laser point cloud data and multi-source optical remote sensing data to estimate forest canopy closure in large forested areas. Firstly， the dependent variable of the regression model， which is the true values of forest canopy closure， was calculated from the Airborne Laser Scanning （ALS） point cloud data. Secondly， 18 independent variables， such as vegetation indices and texture， were extracted from Sentinel-2 MSI， Landsat-8 OLI， and Sentinel-1 SAR images. Then， taking 14 forest plots in Guangxi as an example， the impacts of different independent variable combinations on forest canopy closure inversion were experimentally analyzed using two machine learning models， Random Forest Regression （RFR） and Support Vector Regression （SVR）. Finally， the best variable combination and machine learning method were selected to map the forest canopy closure in Guangxi. The experimental results showed that RFR performed better than SVR， and the S2+S1 combination had the highest accuracy， with a correlation coefficient R² of 0.703， Root Mean Square Error （RMSE） of 0.19， and Mean Absolute Error （MAE） of 0.13. Additionally， polarization features can significantly improve the inversion accuracy of forest canopy closure.

The Tasseled Cap Transformation （TCT） is a widely used remote sensing image processing technique. For Landsat 8 satellite imagery， three TCT algorithms have been proposed by Baig et al.， Zhai et al.， and Liu et al.， respectively. However， the consistency among these three algorithms remains unclear， leaving users uncertain about which one to choose. Additionally， while Zhai et al. provided an algorithm based on Surface Reflectance （SR） data， the other two algorithms are only applicable to Top of Atmosphere （TOA） reflectance data. It is thus essential to verify whether the TOA-based algorithms can be applied to SR data. To address these issues， three Landsat 8 images with different land cover types were selected， and three TCT algorithms were applied. The inversion results were quantitatively compared to identify differences among the algorithms. The results show that the Baig and Zhai algorithms exhibit the highest consistency， with an average R² of 0.974 4 and an average RMSE of 0.025 for the three components. In contrast， the inversion results of the Liu algorithm exhibit significant differences compared to those of the Baig and Zhai algorithms. The average R² values between Liu and Baig， and between Liu and Zhai， are 0.803 1 and 0.865 2， respectively， while the average RMSE values are 0.077 6 and 0.065 5， respectively. Therefore， the inversion results of Baig and Zhai are more comparable. Furthermore， applying the TOA-based TCT algorithms of Baig and Liu to SR data reveals substantial differences between the components derived from SR and TOA reflectance， with RMSE reaching 0.053 5 and an average |PC| of 146.07%. Thus， it is not recommended to use the TOA-based algorithms of Baig and Liu for TCT on SR data. In conclusion， the algorithm proposed by Zhai et al.， which is applicable to both TOA and SR reflectance data， is recommended for Landsat 8 TCT applications.

The application of atmospheric composition data obtained from spaceborne remote sensors in climate and atmospheric environment research has promoted the rapid development of atmospheric composition remote sensing from visible， shortwave infrared to ultraviolet band， and from channel sensing to hyperspectral detection technology. The requirement for quantitative application of hyperspectral data is accurate spectral and radiometric calibration. Compared with the relatively mature calibration technology for visible and shortwave infrared remote sensors， calibration in ultraviolet band is significantly affected by the stability of the on-orbit calibration device， and atmospheric ozone absorption. Thus on-orbit calibration technology for ultraviolet hyperspectral remote sensing data is a challenging issue that urgently needs breakthrough. This paper presents the review made in on-orbit calibration methods for ultraviolet satellite remote sensors. It highlights the development of on-orbit spectral and radiometric calibration techniques， as well as alternative calibration approaches. The paper also compares the advantages and shortcomings of various calibration methods used in visible band remote sensing satellites， and analyzes the applicabilities and challenges of calibration in ultraviolet band. In light of the development of new-generation atmospheric composition remote sensors， the paper proposes major challenges that need to be solved for on-orbit calibration， with a focus on hyperspectral ultraviolet atmospheric composition remote sensing， which have important guidance for the improvement of ultraviolet spaceborne remote sensors and the enhancement of the calibration accuracy of ultraviolet remote sensors.

Colored Dissolved Organic Matter （CDOM） exhibits unique optical properties and significant advantages in remote sensing monitoring. However， notable differences exist in the optical characteristics of different CDOM types. This study systematically summarizes CDOM features in global water bodies and various lake types through literature review. This study demonstrates that models established based on aquatic optical properties under different natural geographical zones can more accurately identify the key factors for CDOM variation patterns. This research provides theoretical foundations for regional CDOM monitoring and water quality assessment through remote sensing techniques.

Geostationary meteorological satellite imagers enable extensive and continuous monitoring of sea fog. However， when cloud layers cover the fog region， the signals received by the satellite primarily originate from the upper cloud layers， making it challenging to ascertain the presence of sea fog in the lower layers. In the East China Sea， occurrences of fog events beneath cloud cover are frequent， impeding the operational application of satellite remote sensing in sea fog detection. Based on the channel design features of the Advanced Himawari Imagers （AHI） on-board the Himawari-8 geostationary meteorological satellite， we creatively proposed an algorithm aiming at detecting sea fog covered by high-level ice clouds combined with radiation transfer theory simulation and real observations. Two long-wave infrared channels （8.5 μm and 11 μm） were utilized to identify the cloud-top phase （ice clouds or water clouds）. Low-level water clouds and sea fog beneath ice clouds were distinguished by the differences in radiation characteristics in the short-wave infrared （1.6 μm and 2.25 μm） and visible （0.64 μm） channels when ice clouds were identified. Finally， the accuracy of this algorithm was verified according to Cloud-Aerosol Lidar with Orthogonal Polarization （CALIOP） data. The results showed good consistency between our algorithm and CALIOP， with an average probability of detection of 49%， a false alarm ratio of 7%， and a critical success index of 46%. Due to the difficulty of detecting fog under clouds in the field of satellite remote sensing， the results demonstrate that this algorithm can detect sea fog under ice clouds.

This study proposes a novel method to joint utilization of visible and thermal infrared images from UAV perspectives. The method involves the development of a multimodal semantic segmentation model， termed CDFNet， which is designed based on cross-modal feature decoupling and attention refocusing. A cross-modal feature decoupling module is introduced to explicitly disentangle and enhance complementary discriminative features from different modalities， thereby improving the representational capacity of fused features in complex urban scenes. Furthermore， a focalizing attention decoder is incorporated to dynamically refine the attention scope towards small-scale objects during decoding， thereby effectively mitigating the interference from noisy backgrounds. Extensive experimentation on the Kust4K dataset demonstrates that CDFNet achieves mIoU improvements of 6.3% and 3.1% over the baseline and the current state-of-the-art multimodal method Sigma， respectively. Feature visualization and modality robustness evaluations further confirm that CDFNet yields more robust feature representations under low signal-to-noise conditions and significantly enhances segmentation accuracy for small targets in challenging urban road scenes from UAV perspectives.

Accurate estimation of leaf or canopy chlorophyll content is essential for monitoring crop growth. Crop chlorophyll monitoring using remote sensing is a non-destructive， large-area，real-time monitoring method， which requires reliable inversion models and satellite data. Focusing on summer maize， parameter settings of PROSAIL model were determined through local and global sensitivity analysis. In combination with measured ground data and related literature， canopy reflectance of summer maize based on PROSAIL model was simulated. Then， based on spectral response function of Sentinel-2A image， equivalent reflectance data of Sentinel-2A image were obtained. Typical hyperspectral vegetation indices and vegetation indices of improved band combination mode based on Sentinel-2A image data were calculated and analyzed to determine the best estimation model of Leaf Chlorophyll Content （LCC） and Canopy Chlorophyll Content （CCC）. Based on PROSAIL simulation data， Sentinel-2A image data and ground measurement data， the modeling and validation analysis of summer maize LCC and CCC were carried out. The results showed that the R² of vegetation indices inversion based on PROSAIL model and Sentinel-2A image were 0.61 and 0.65， RMSE were 7.54 μg/cm² and 8.46 μg/cm²， respectively. The inversion accuracy of LCC using vegetation indices based on PROSAIL model and Sentinel-2A image was consistent， and the inversion accuracy met the requirements of summer maize growth monitoring. The R² of CCC retrieved by the two methods were 0.75 and 0.77， RMSE were 1.03 g/m² and 0.02 g/m²， respectively. This study provides an effective method for retrieving chlorophyll content in the region where there are few ground measured data， which is helpful for the growth and pest control monitoring of summer maize.

Accurate inversion of Fractional Snow Cover （FSC） in forested areas is significant for hydrological process simulation， climate change projection and ecosystem management. This study proposed a hybrid machine learning model， RF_ART， based on the Random Forests （RF） algorithm and the Asymptotic Radiative Transfer （ART） model， aiming to improve the inversion accuracy of FSC in forested areas. The model integrated multiple environmental variables， including spectral characteristics， vegetation， terrain， angles， land surface temperature， and snow grain size. Experiments were conducted in the Altay region and the central-eastern Tianshan Mountains of Northern Xinjiang. The results showed that the average Root Mean Square Error （RMSE） of RF_ART in the training images was approximately 0.048 0， and the average RMSE in the testing images was approximately 0.096 6， which was significantly lower than those of the NDSI_FSC and NDFSI_FSC methods. Additionally， while the RMSEs of RF_ART and RF_FSC were similar， in testing images， RF_ART introduced physical constraints that enhanced the model's robustness， making it the preferred algorithm for FSC inversion in forested areas. Notably， in the predominantly deciduous forests， the RMSE of the RF_ART model gradually decreased with the incorporation of various variables. Moreover， under conditions of scarce data， the RF_ART model demonstrated strong robustness and application potential. By combining hybrid machine learning models with multi-source remote sensing data， this study provides an important reference for the inversion of FSC in forested areas.

In order to meet the demand for accurate monitoring of forest resources， this paper explores the potential of backpack Light Detection and Ranging （LiDAR） on extracting forest structure parameters for the practical applications. Taking Jiande Forest Farm in Zhejiang Province as the study area， based on backpack LiDAR data collected from eight sample plots， an improved K-means hierarchical clustering algorithm is proposed for individual tree segmentation. Then， six individual tree structural parameters， including diameter at breast height， tree height， crown diameter， crown area， crown volume and gap fraction， as well as 56 cloud point layer height variables were calculated based on the segmented individual tree point cloud data. After that， the random forest method is applied to estimate the volume of individual trees and sample plots. The results showed that， the average comprehensive segmentation accuracy F of the improved K-means hierarchical clustering algorithm was 0.87， and the extraction accuracy of single tree diameter at breast height was 91.26%， and the tree height accuracy was 85.77%. The individual tree volume estimation model using only six tree structural parameters obtained an accuracy of the coefficient of determination（R²） of 0.89， and the Root-Mean-Square Error （RMSE） was 0.053 m³. After using the Pearson correlation coefficient and the importance of random forest features to select the optimal features from the individual tree structure parameters and layer height parameters， the outperformed model was obtained with an estimation accuracy of R² was 0.93， RMSE was 0.041 m³， and the overall plots’ accuracy reached 94.20%. This study indicated that the proposed K-means hierarchical clustering algorithm can effectively segment individual tree point clouds， and the random forest method can estimate individual tree volume and sample plots volume well， which can provide an important reference for backpack LiDAR in extracting forest resource parameters.

In the past few decades， water bodies around the world have continued to suffer from systemic pollution and severe water quality deterioration. Total phosphorus is one of the important indicators for water quality evaluation and an important factor affecting water eutrophication and cyanobacteria bloom outbreaks. This paper discusses the relationship between total phosphorus and other optical water quality parameters， the remote sensing inversion of total phosphorus concentration in different water body types， the remote sensing algorithm of total phosphorus concentration， and the remote sensing inversion of total phosphorus concentration on different remote sensing platforms. Since the 1990s， there have been more than 300 documents on total phosphorus concentration inversion. In recent years， research hot spots have gradually focused on topics such as “remote sensing technology” and “machine learning”. For the study of inland lake water bodies， Landsat/TM and MODIS images are mainly used. Among them， the accuracy of models using the combination of green band， near-infrared band and mid-infrared band is generally higher. Observing the total phosphorus concentration of global lakes through satellite image data， it was found that the total phosphorus content of global lakes is generally on the rise， with the highest phosphorus content in Asian lakes， followed by South America， Africa and Europe. No significant increasing trend was found in Oceania. With the development of computer technology， machine learning algorithms have gradually become a current hot topic. Compared with traditional algorithms， models built using machine learning algorithms are more accurate. The random forest algorithm is widely used because the model it builds has higher accuracy than other machine learning algorithms. With the continuous development of research， the construction of a total phosphorus concentration model suitable for different water types is the general trend in the future， and the development of sensors with high spatial resolution and high temporal resolution is even more urgent.

Understanding the dynamics of Leaf Area Index （LAI） in tropical evergreen forests is crucial for assessing ecosystem health and carbon dynamics. In this paper， a method is proposed for decomposing LAI into leaf age cohorts in tropical evergreen broadleaf forests across Amazon Basin. The method simplifies the canopy into three major leaf age stages （i.e.， young， mature， and old leaves）. The method integrates leaf-level photosynthetic biochemistry models with remote sensing climate data to simulate carbon assimilation across these leaf age stages. Then， utilizing a novel neighbor-based approach and the linear least squares solver with bounds or linear constraints （Lsqlin） to derive the values of three LAI cohorts. Validation against ground-based phenology camera data shows good agreement in seasonal dynamics of LAI cohorts （LAI_young： R² = 0.32； LAI_mature： R² = 0.61 and LAI_old： R² = 0.49）， indicating the method's ability to capture seasonal variations accurately. Spatial patterns of LAI cohorts closely correspond to climatic phenology variables across the Amazon Basin. This approach enhances our understanding of LAI dynamics in tropical forests， providing valuable insights for ecosystem management and carbon cycle modeling in the Amazon Basin.