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.

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.

In the context of global climate warming and rapid urbanization，the Urban Heat Island （UHI） effect exacerbates population heat exposure risk，accurately identifying high-risk areas of population heat exposure is crucial for adapting to and mitigating the threats posed by high temperatures．This study focuses on the area within the sixth ring road of Beijing，from the perspective of Local Climate Zones （LCZ），analyzing the temporal and spatial variations of Surface Urban Heat Island Intensity （SUHII） in different LCZs during summer，based on ECOSTRESS Land Surface Temperature（LST） data and Tencent mobility population data. The study also evaluates the daily dynamic changes in population heat exposure under different SUHII levels. The results show：①During the day，significant differences in SUHII are observed across different LCZ types. Except for LCZ 9 （Sparsely built），built-up LCZs，as well as LCZ E （Bare rock or paved） and LCZ F （Bare soil or sand），exhibit characteristics of heat sources，while other types are heat sinks．At night，the SUHII differences between LCZs decrease，and the heat sink effect of LCZ A （Dense trees） and LCZ B （Scattered trees） weakens，while LCZ G （Water） shifts from a heat sink to a heat source.②Population heat exposure is lowest between 6—7 AM and peaks between 10—11 AM.Built-up LCZs （except LCZ 9），along with LCZ E and F，show population heat exposure risk. Within the fourth ring road，where buildings are dense and population density is high，population heat exposure is particularly prominent.③When SUHII exceeds 2 ℃，the risk of population heat exposure significantly increases，particularly in areas with high building density and low green coverage，controlling SUHII to stay within 2 ℃ can effectively reduce the risk of population heat exposure. This study provides a theoretical basis and practical support for enhancing urban livability，improving living environments，and formulating urban heat risk control strategies．

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.

Forest canopy height is a key parameter for evaluating forest structure and ecosystem functions， playing a crucial role in forest resource management and carbon stock monitoring. Satellite borne LiDAR has been widely applied in the retrieval of forest canopy information because of its high vertical penetration accuracy. However， studies based on China's domestically developed spaceborne full-waveform LiDAR data remain limited. China's forests resources are abundant and mostly located in areas with complex terrain， and different vegetation cover and complex topography can have varying degrees of impact on the feature extraction of full-waveform LiDAR signals. This study employs multi-beam full-waveform LiDAR data acquired by “Goumang”， China's first Terrestrial Ecosystem Carbon Inventory Satellite， from which the original footprint waveforms are filtered and key feature points are extracted， followed by class-specific thresholding to optimize the identification of waveform start and end positions. Based on the optimized signal endpoints and a simulated LiDAR waveform model of forest canopies， the effects of terrain slope are then corrected， followed by the inversion and validation of the forest canopy height in two typical demonstration areas， namely， the Northeast China Tiger and Leopard National Park and the Hainan Tropical Rainforest National Park. The results show that， under the terrain condition of slope ≤15°， the forest canopy height obtained from the optimized Goumang laser altimetry data exhibit strong consistency with the maximum canopy height from the CHM reference data of the sample plots， with correlation coefficients all exceeding 0.85， and the RMSE values decreasing significantly from 4.52 m to 2.56 m in the Northeast China Tiger and Leopard National Park and from 5.51 m to 3.20 m in the Hainan Tropical Rainforest National Park， which further proves that Goumang has a great potential for application in the estimation of the forest canopy height at the laser-footprint scale.

Spartina alterniflora， as a typical invasive species in China's coastal wetlands， poses a severe threat to ecosystem structure and function due to its rapid expansion. There is an urgent need to establishan efficient， accurate， and operational large-scale monitoring system to support ecological risk assessment， invasive species control， and wetland conservation decision-making. Given the limitations of traditional monitoring methods—such as low efficiency and challenges in large-scale applications—and the constrained accuracy of remote sensing in complex environments， this study systematically reviews recent progress in remote sensing monitoring of S. alterniflora invasions， focusing on three key aspects： identification methods， spatiotemporal analysis， and ecological impact assessment.In terms of identification methods， remote sensing relies primarily on the unique spectral and phenological characteristics of S. alterniflora. The application of traditional machine learning and deep learning methods has significantly improved identification accuracy， while phenology-based strategies effectively mitigate interference from the "different objects with similar spectra" phenomenon. Numerous studies indicate that the spread of S. alterniflora along China's coast has undergone distinct stages—from scattered distribution to rapid expansion—with spatial patterns exhibiting clear regional differences. Its invasion systematically damages wetland ecosystems such as salt marshes， tidal flats， and mangroves， significantly compressing native vegetation habitats， degrading habitat quality， reducing waterbird habitat ranges， and altering tidal flat sedimentation processes. This leads to biodiversity loss， soil degradation， and ultimately undermines the stability and service functions of wetland ecosystems.Overall， while significant progress has been made in identification accuracy and understanding invasion mechanisms， challenges remain， including difficulties in identifying small patches， insufficient model generalizability， and inadequate synergistic application of multi-source data. Future research should integrate high spatiotemporal resolution and multi-source remote sensing data to enhance monitoring capabilities， develop transfer learning and few-shot learning methods to improve model generalizability， deepen the fusion of phenological， spectral， and texture features， and further elucidate the driving mechanisms and ecological effects of invasion. These efforts will provide scientific support for the control of invasive species in coastal wetlands and the maintenance of ecological security.

The near field high cloud characteristics of stratospheric airships are closely linked to stratospheric flight safety. Existing methods for cloud detection based on ground-based， airborne， and space-based systems are introduced， and their respective advantages and limitations in high cloud detection are analyzed and compared. To address the shortcomings of current detection techniques， two cloud detection methods based on stratospheric airships are proposed： near field detection and tethered payload-based detection. The development status of meteorological payloads and tethered equipment is also reviewed. To ensure the safety of stratospheric airship operations， a collaborative cloud detection system integrating ground， airborne， and space-based components is required， providing comprehensive cloud characteristic data to support the stability and reliability of the airship platforms. Moreover， in light of existing data gaps and technological limitations in high cloud detection in China， the development of meteorological payloads and related technologies should be accelerated to enhance the accuracy and reliability of meteorological sensing equipment， thereby improving the country’s independent technological capabilities and data collection capacity.

The Guangdong-Hong Kong-Macao Greater Bay Area （GBA） stands as a paramount symbol of China's reform and opening-up， characterized by its exceptional economic vitality. Understanding its spatiotemporal urbanization patterns is crucial for deciphering the evolution of mega-city clusters and their interplay with economic development. To address the inconsistencies between DMSP/OLS （1992—2012） and NPP/VIIRS （2012—2021） nighttime light data， this study constructed a continuous， long-term dataset by evaluating two integration approaches： simulating DMSP/OLS from NPP/VIIRS and vice versa. The optimal method was selected based on the performance of linear regression models with Gross Domestic Product （GDP）， resulting in a consistent nighttime light dataset （1992—2021） for the GBA that shows a higher consistency with socioeconomic indicators. Our analysis revealed that： （1） From 1992 to 2021， the nighttime light intensity in the GBA showed a continuous increasing trend， with distinct spatial dynamics across three periods： initial rapid growth was concentrated in the core urban areas of Guangzhou， Hong Kong， and Macao （1992—2001）； subsequently， the growth hotspot diffused into the suburbs （2002—2011）； and finally， it expanded further into county-level towns and rural areas （2012—2021）. （2） The expansion rate of urban built-up areas was higher in the northern and western parts of the GBA than in the eastern and southern parts. Moreover， the primary direction of urban expansion underwent a notable shift， transitioning from a “southeast-northwest” orientation to a predominantly “east-west” orientation.

Monitoring of glacial lakes based on remote sensing is one of the current research hotpots in cryospheric science. Imageries from the Multispectral Imager for Inshore of SDGSAT⁃1 would be one of important data resource for glacial lake researches. Our study focuses on nearly 2 000 glacial lakes in the Mount Everest region. An automatic extraction for glacial lakes is applied to the multispectral bands of SDGSAT-1 images. The accuracy of the glacial lakes on SDGSAT-1 MII images is assessed by comparing with lakes from both Sentinel-2 MSI and Landsat 8 OLI. The area difference of glacial lakes between SDGSAT-1 and Sentinel-2 MSI or Landsat 8 OLI is quantified as error. And the Jaccard coefficient performances the ratio of overlapping between glacial lakes from SDGSAT-1 and Sentinel-2 MSI， which indicates the position bias between two lakes. Coregistration error between bands 2 and 7 of SDGSAT-1， band 2 of SDGSAT-1 and band 3 of Sentinel-2 is calculated by COSI-Corr software. And the correlation analysis between co⁃registration error and area error， Jaccard coefficient is employed in SPSS software. The results indicate that the decision tree based on Normalized Difference Water Index enables rapid extraction of glacial lake vector data from SDGSAT-1 multispectral imagery. The accuracy of glacial lakes on SDGSAT-1 is higher than those from Landsat 8 OLI but lower than those from Sentinel-2 MSI， with an average error of 16.6%. Glacial lakes in shadowed areas on imagery of SDGSAT-1 after atmospheric correction was more exactly outlined than that before， although there was 10% more error compared to other glacial lakes. The area error of glacial lake on SDGSAT-1 is mainly defined by co-registration error of bands 2 and 7 of SDGSAT-1， while the Jaccard coefficient is influenced by the error from both bands 2 and 7 of SDGSAT-1， and band 2 of SDGSAT-1 and band 3 of Sentinel-2. SDGSAT-1 multispectral data serves as a robust data source for studying glacial lakes. Although its accuracy of identifying supraglacial lakes on SDGSAT-1 MII is lower than for other lakes， SDGSAT-1 MII will be the potential data for researches of the identification of supraglacial lakes and the change of supraglacial lakes.

Soil， as the most valuable natural resource， faces challenges of global， regional， and local degradation， with issues ranging from quality deterioration to salinization leading to significant losses of high-quality farmland. These problems impact agricultural productivity and ecological balance， disrupting food security and sustainable development. Therefore， timely monitoring and accurate mapping of salinization processes are crucial， especially in semi-arid and arid regions where the impact of climate change has reached alarming levels. With the rapid development of remote sensing technology， soil salinization mapping techniques are showing great potential. This paper systematically reviews the application prospects of remote sensing technology in soil salinization assessment. Analysis of relevant literature in the Web of Science database reveals that the United States and China have conducted in-depth research on remote sensing technology for soil salinization. Keyword searches indicate that recent research focuses on salt-alkaline remote sensing monitoring based on machine learning and artificial intelligence. Bare soil and vegetation information are widely used in current soil salinization detection， introducing the concept of spectral indices. By studying the reflectance of visible （VIS）， near-infrared （NIR）， and shortwave infrared （SWIR） bands， soil salinity changes are monitored. The aim is to integrate multi-source， multi-scale， multi-platform remote sensing data with ground information to establish a comprehensive "sky-ground" soil salinization investigation and monitoring technology system. However， large-scale soil salinity estimation based on remote sensing technology remains a major challenge due to the cost and coverage of acquiring high-resolution， hyperspectral， high signal-to-noise ratio， and multi-modal remote sensing data， as well as their revisit periods. In addition， efficient acquisition of ground survey data matching multiple-source multi-modal remote sensing data， extraction of effective salt response variables from multi-modal data， effective fusion of multi-dimensional （one-dimensional， two-dimensional， three-dimensional） multi-modal information to build inversion models for multiple characterization parameters of saline-affected land， and the application of developed remote sensing products in agricultural production and ecological environment protection practices are pressing issues that require further research.

Frequent extreme high-temperature weather under the background of urbanization and climate warming poses a threat to the health of urban residents. The Park Cooling Effect （PCE） is vital for alleviating these adverse effects. However， in land-scarce urban areas， it is unrealistic to expand the size of parks without limits， and maximizing the cooling effect per unit area becomes an urgent issue.Taking China’s hottest “furnace city”， Fuzhou， as the study area， this research quantifies PCE using three indicators： cooling amplitude （∆T_max） ， cooling distance （L_∆max） and cooling gradient （G_temp）.It calculates the PCE indicators of 50 urban parks within the city and analyzes the influencing factors from the perspectives of external park morphology and internal landscape patch characteristics. Results reveal that： ①Among 50 parks， 42 exhibit a PCE， whereas 8 do not； ②Larger parks are not always better； it is crucial to consider both external morphological and internal patch attributes； ③Regarding external morphology， parks with simple and regular boundary shapes are more conducive to cooling effects； ④With regard to the internal patch characteristics of the parks， low impervious surface proportions， high proportions of water bodies and vegetation， and complex patch morphologies enhance PCE， while excessively high edge densities and landscape fragmentation weaken it. Therefore， efforts should be made to maintain the continuity and integrity of vegetation and water body coverage， design diverse and multi-tiered vegetation boundary structures， or leverage terrain changes to increase the vertical complexity of impervious surface boundaries to optimize cooling and alleviate urban heat island effects.

Under the background of “Double carbon”， the Yellow River basin is an important energy base and the national ecological corridor along the Yellow River. The study takes the cities in the upper reaches of the Yellow River as an example， and builds a carbon emission estimation model based on luminous remote sensing data， a 21-year analysis of the spatiotemporal pattern of carbon emissions using gravity models， standard deviation elliptic models， and hot spot analysis， the carbon emission intensity of cities in the upper reaches of the Yellow River in 2030 was predicted by CA-Markov model. The results show that the carbon emission in the upper reaches of the Yellow River has a rising trend from 0.52×10⁹ t to 1.78×10⁹ t from 2000 to 2020， in general， there is “Strong gravity in the northeast， weak gravity in other regions” and the carbon emission center of gravity shifts to industrial cities such as Ordos， while the MOLAIN index is relatively stable at the city and county levels in the upper reaches of the Yellow River， cities such as Baotou， Hohhot and Shizuishan have high concentrations， while cities such as Longnan and Xining have low concentrations. The spatial distribution of carbon emission cold and hot spots shows significant changes in the degree of agglomeration， with the river source area gradually increasing and to the east， the canyon area relatively stable， and the alluvial plain area showing fluctuating changes.The carbon emission intensity of cities in the upper reaches of the Yellow River shows little change by 2030.Carbon emissions in the upper reaches of the Yellow River exhibit significant spatial heterogeneity， with industrial cities showing high emissions. Future mitigation efforts should focus on the northeastern and industrial areas， implementing clean energy substitution， regional coordinated reduction， and enhanced regulatory mechanisms to achieve basin-wide carbon reduction and promote green and sustainable urban development.

Cyanobacterial blooms pose serious threats to aquatic ecosystems and human health. Operational satellite-based monitoring of cyanobacterial blooms， with rapid response to management needs， is of significant scientific and practical value. Current research mainly focuses on automated extraction algorithms and spatiotemporal variation analysis， while high-precision， standardized， and operational methods for rapid spatiotemporal response are limited. Consequently， monitoring results often cannot effectively support national and local water environment management. To address this， we developed a comprehensive methodology for satellite-based operational monitoring of cyanobacterial blooms， adhering to principles of scientific rigor， operability， and comparability. The methodology includes satellite imagery selection， monitoring frequency and response times for routine and emergency monitoring， bloom distribution extraction， and quality control of results. Based on this， a product system for operational rapid spatiotemporal monitoring was established. This approach improves the accuracy， consistency， and timeliness of cyanobacterial bloom monitoring and provides critical support for water environment management and decision-making at national and local levels.

Ocean color remote sensing chlorophyll a concentration data are of great significance for a wide range of scientific issues， such as global change and biogeochemical cycle studies. However， the existing operational remote sensing data on chlorophyll-a concentrations suffer from spatial incompleteness and temporal discontinuity，significantly limiting their application potential. Research shows that data reconstruction is the primary approach to address this issue. Based on recent research findings on ocean color remote sensing data reconstruction， this research summarizes the theoretical foundations and development history of data reconstruction methods. It particularly discusses the hot topics in chlorophyll-a concentration data reconstruction， including common datasets， methods， and major research areas. The paper also identifies existing challenges in chlorophyll-a concentration data reconstruction， highlighting critical issues such as data reconstruction in extremely data-scarce regions， the development of short-time scale data reconstruction methods， multivariate combined reconstruction methods， improvements in Empirical Orthogonal Function data interpolation， and the integration of remote sensing with numerical simulation for data reconstruction. The paper concludes by outlining future research directions in this field.

Urban informal settlements are a global common phenomenon in the process of rapid urbanization. The research on their optical remote sensing identification is of great significance for achieving the 11th Sustainable Development Goal of sustainable cities and communities. Based on the statistics of relevant domestic and foreign literature from 2001 to 2024， this paper sorts out and analyzes the data sources and spatial resolutions of optical remote sensing images of urban informal settlements， single-class feature classification and multi-source feature fusion classification， as well as the identification methods and their advantages and disadvantages. The results show that data sources are classified into three categories according to spatial resolution： ultra-high/high， medium， and low； single-class features or data sources are classified into optical image features such as spectral， texture， geometry and context， and auxiliary data features such as GIS data； multi-source feature fusion is classified into three types： feature-level， data-level and decision-level. Compared with deep learning， traditional identification methods such as pixel-based and object-based methods have limitations such as weak adaptability and low recognition efficiency. Deep learning methods， with their strong feature extraction capabilities and good generalization ability， have broad prospects in the remote sensing identification of large-scale and long-term informal settlements. This paper aims to provide a reference for the future remote sensing identification of urban informal settlements and promote the construction and development of sustainable cities.

Aboveground biomass of winter wheat is an important physiological indicator reflecting crop growth status and yield potential. Accurate and efficient monitoring of biomass is essential for implementing precision agricultural management， optimizing fertilization and irrigation strategies， and ensuring food security. In this study， a field experiment was conducted in the southern part of Gaocheng District， Shijiazhuang， Hebei Province， China. The aboveground biomass and canopy hyperspectral reflectance were measured at regreening stage， jointing stage， and grain filling stage of winter wheat. Sensitive wavelengths significantly correlated with aboveground biomass were preliminarily screened from the original spectra， multiplicative scatter correction spectra， First Derivative（FD） spectra， and continuum-removed spectra of winter wheat at different growth stages using Pearson correlation analysis. Based on this， feature band selection was performed using the Competitive Adaptive Reweighted Sampling （CARS） algorithm and the Successive Projections Algorithm （SPA）， and biomass estimation models were then constructed by integrating the selected features with three machine learning methods： Partial Least Squares Regression （PLSR）， Gaussian Process Regression （GPR）， and Support Vector Machine （SVM）. The results indicated that： （1） Spectral data processed using the first derivative method overall outperformed other spectral preprocessing approaches in both correlation analysis and machine learning modeling. （2） Both CARS algorithm and SPA effectively eliminated redundant information in hyperspectral data， reducing data dimensionality. Among them， SPA showed more prominent performance in simplifying bands， but the model estimation accuracy based on CARS algorithm was higher than that of SPA. The highly sensitive feature bands selected by both algorithms， which were mainly concentrated in red-edge and near-infrared spectral regions， were strongly related to aboveground biomass. （3） At different growth stages， the FD-CARS-GPR model demonstrated superior overall performance， exhibiting good adaptability and robustness. Particularly， the model achieved the highest estimation accuracy at jointing stage， with validation set R² of 0.802， RMSE of 50.97 g/m²， and RPD of 2.30， followed by regreening stage， and the lowest at filling stage. The aboveground biomass estimation model for winter wheat， developed based on first derivative spectra in combination with the CARS algorithm and constructed using GPR， demonstrated high accuracy and stability. It is suitable for quantitative estimation of aboveground biomass across different growth stages of winter wheat， providing reliable technical support for growth monitoring and precision agricultural management.

Leaf Area Index （LAI） is an important parameter reflecting crop growth. Its accurate acquisition is crucial for agricultural monitoring and yield assessment. The Sentinel-2 satellite has multiple red-edge and shortwave infrared bands， which have potential advantages in LAI estimation. Therefore， comparing the estimation capabilities of different models and band combinations is of great significance to improving the accuracy of maize LAI estimation. This study uses the Daman Observation Field at the Heihe Remote Sensing Experimental Station in Zhangye， Gansu Province， as the research area. Based on PROSAIL model sensitivity analysis， we screened band combinations and key parameters sensitive to LAI and constructed a simulation database. LAI was inverted using three methods： a Look Up Table （LUT）， a Genetic Algorithm （GA）， and a Random Forest （RF）. Accuracy was verified using Sentinel-2 imagery and field data. The results show that： ①outliers significantly affect estimation accuracy. LUT is most sensitive to outliers （ΔR²=0.20~0.26）， while RF is relatively stable （ΔR²=0.14~0.20）. Adding the red-edge band RE₂ （B， R， RE₂， RE₃， NIR， RE， SW₂） to the LUT improves accuracy while maintaining interference resistance （mean ΔR²=0.18）. ②After removing outliers， LUT achieves the highest inversion accuracy （R² = 0.88， RMSE = 0.31）， followed by GA. Adding RF to the RE₂ band significantly improves performance （R² = 0.65~0.79， RMSE = 0.64~0.53）. ③The inversion accuracy of the three models in the high LAI range （2.5~5.0； LUT： R²>0.84； GA： R²>0.71； RF： R²>0.57） is significantly better than that in the low LAI range （0.5~2.5）， among which the R² of RF combined with RE₂ band can increase from 0.57~0.82. In summary， the physical model inversion method and RE₂ band play an outstanding role in improving the accuracy of maize LAI estimation， and can provide some valuable references for maize LAI estimation and growth monitoring.

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.

Accurately obtaining long-term evolution information of coastal wetlands is of great significance for ecological protection and resource management. Taking the buffer zone and core area of the Yancheng Rare Birds Nature Reserve as the study area， this research monitored the wetland evolution process and explored its spatiotemporal heterogeneity by employing Landsat remote sensing data from 1996 to 2024 on the Google Earth Engine cloud platform. An optimized method integrating the Continuous Change Detection and Classification （CCDC） model， vegetation phenological features， and Markov Random Field （MRF） post-processing （CCDC-MRF） was developed. The results show that： （1） The proposed method achieved high classification accuracy， with an average overall accuracy exceeding 86% and a Kappa coefficient over 0.85 for wetland identification， effectively suppressing salt-and-pepper noise. （2） Wetland evolution in the study area exhibited stage-specific characteristics， following a sequence of "reclamation - stabilization - returning ponds to wetlands - Spartina alterniflora control". Changes were more pronounced in the buffer zone， where artificial wetlands largely transitioned to reed marshes， while the core area experienced smaller changes dominated by natural vegetation succession， with recent changes primarily driven by S. alterniflora control actions. （3） The mechanisms governing water body and vegetation gain/loss showed spatial differences： water body dynamics in the buffer zone were mainly influenced by the conversion of aquaculture ponds back to wetlands， whereas large-scale vegetation loss in the core area was directly related to S. alterniflora eradication. The study demonstrates that the optimized continuous change detection algorithm enhances the accuracy of coastal wetland identification. The differential evolution characteristics of coastal wetlands in the Yancheng Reserve， shaped by policy regulation， natural processes， and S. alterniflora invasion， deepen the understanding of coastal wetland evolution mechanisms and can provide technical support for precise monitoring and zonal management of coastal wetlands.

The economic value of various services and functions rendered by ecosystems to human society is quantified by ecosystem service value assessment； decision-making support for ecological protection， environmental management， and sustainable development policy formulation is provided； and an important foundation for promoting sustainable development is established. As China's crucial ecological barrier and economic belt， the Yellow River Basin confronts severe challenges such as soil erosion and habitat quality degradation， impacting the ecological well-being of hundreds of millions of people. The study focused on the densely populated middle and lower reaches of the Yellow River Basin as the research area. Based on land cover data from 2000， 2010， and 2020， as well as annual precipitation and elevation data， the InVEST （Integrated Valuation of Ecosystem Services and Tradeoffs） model was utilized to analyze the spatiotemporal variations of four ecosystem services—water yield， carbon storage， soil retention， and habitat quality—from the perspectives of provincial scales and land cover types， thereby revealing the interactions among these ecosystem services. The results indicated that， over the two study periods， water yield and soil retention services exhibited a gradual upward trend； carbon storage progressively declined； habitat quality demonstrated a dynamic change of first rising and then falling； significant differences in ecosystem service performance were observed at the provincial scale； the ecological service functions of forestland and grassland were notably prominent； ecosystem services generally exhibit synergistic relationships， with significant spatial heterogeneity. The study provided high-resolution assessments of ecosystem service dynamics and proposed region-specific ecological conservation strategies based on provincial differences， offering references for ecological management and sustainable development in the middle and lower reaches of the Yellow River Basin.

Individual trees are the fundamental units of a forest and are critical factors in forest resource surveys and ecosystem monitoring. With the development of UAV technology and deep learning methods， the efficient extraction of individual tree parameters has become possible. Based on this， the present study focused on a eucalyptus plantation in Jiajiang County， Leshan City， Sichuan Province. Using UAV-based visible light imagery， two deep learning algorithms—SSD （Single Shot MultiBox Detector） and Faster R-CNN （Region-based Convolutional Neural Networks）—were employed for individual tree detection and crown width classification. The two models were compared across multiple dimensions， including fitting accuracy， validation accuracy， and training time. The results showed that both models achieved fitting accuracies above 83%， indicating good performance. The Faster R-CNN model achieved an average precision of 95.13%， an average recall of 88.62%， and an average F1-score of 91.76%， while the SSD model achieved an average precision of 91.03%， an average recall of 82.66%， and an average F1-score of 86.64%. Faster R-CNN outperformed SSD in terms of accuracy and stability， whereas the SSD model showed clear advantages in training time and detection efficiency. Using the Faster R-CNN model， a total of 129 045 eucalyptus trees were identified. Applying the interquartile range method， the growth conditions of all trees were classified into three levels. The results indicated that normally growing trees （Level II） accounted for 96.07%， vigorously growing trees （Level I） for approximately 3.84%， and slow-growing trees （Level III） for only 0.09%. These findings confirm the strong application potential of deep learning algorithms for individual tree detection using UAV visible light imagery， and highlight the practical significance of crown width classification in guiding forestry production and formulating tending measures.

PS-InSAR technology is widely used for monitoring surface deformation； however， its results are often affected by spatial noise. To improve monitoring accuracy， this study introduces a regional stacking filtering method， constructs a residual grid based on a 0.1° × 0.1° mesh， and applies ISODATA spatial clustering to effectively eliminate Common-Mode Errors （CME）. Based on PS-InSAR data from southeastern Tibet collected between October 2014 and January 2024， the results show that removing CME significantly improves the model fitting accuracy， with the RMSE reduced by approximately 45% and R² increased by around 25% on average. The deformation velocity results reveal significant uplift （>5 mm/a） in the northwestern and central-eastern parts of southeastern Tibet， mainly controlled by fault activity. In contrast， subsidence zones are concentrated around Biru County， closely associated with the Anduo South Fault Zone and the 2021 Ms6.1 earthquake， with deformation patterns highly consistent with tectonic activity. In addition， this study extracts the Line-Of-Sight （LOS） coseismic deformation field of the earthquake and analyzes changes in surface deformation rates before and after the event. The results indicate that the epicentral region experienced up to 23 mm of subsidence， with abrupt coseismic displacement observed in some areas， reflecting fault rupture characteristics. The pre-seismic deformation rate was relatively stable （<5 mm/a）， whereas a post-seismic acceleration of approximately -10 mm/a was observed. Combined with focal mechanism solutions and InSAR inversion， the earthquake is confirmed to have been triggered by a strike-slip–oblique-slip normal faulting mechanism. This study significantly enhances the accuracy of PS-InSAR data and provides insights into the spatiotemporal distribution and geological causes of surface deformation in southeastern Tibet. The findings offer scientific support for geological hazard monitoring and hydropower development in the Yarlung Tsangpo River basin.

Snow plays a crucial regulatory role in the ecosystem of the Tibetan Plateau and has a profound impact on carbon storage and the carbon cycle. As a key variable in the carbon cycle， the spatiotemporal variation characteristics of the Gross Primary Productivity （GPP） of surface vegetation and its response mechanism to snow parameters are directly related to the carbon budget of alpine grassland ecosystems. However， current research on the impact of snow changes in the Tibetan Plateau on GPP is still insufficient， especially the regulatory role of snow phenology （such as the start and end dates of snow cover） on GPP has not been clarified. This study， based on multi-source remote sensing data including the GPP dataset from 2000 to 2018 and snow phenology data， analyzed the spatiotemporal distribution characteristics of GPP and snow parameters in alpine grasslands of the Tibetan Plateau. Combined with meteorological data such as near-surface air temperature， downward shortwave radiation at the surface， and precipitation， the study explored the influence mechanism of snow parameters and meteorological factors on GPP in alpine grasslands. The results show that the annual average GPP value in the Tibetan Plateau presents a distribution pattern of being lower in the northwest and higher in the southeast， and shows an overall increasing trend year by year. Both the start date and end date of snow cover show an advancing trend， while the duration of snow cover shows a decreasing trend. The annual average GPP increases with the delay of the start date of snow cover， the advance of the end date of snow cover， and the reduction of the duration of snow cover. The dominant factors influencing the change of GPP are， in order， near-surface air temperature， duration of snow cover， precipitation， downward shortwave radiation at the surface， start date of snow cover， and end date of snow cover. Although meteorological factors have a greater impact on GPP， the influence of snow parameters on GPP cannot be ignored， especially on spring GPP. The change of GPP is comprehensively affected by the spatial distribution of snow parameters and meteorological factors as well as their interaction. This study reveals the regulatory role of snow dynamics on GPP in alpine grasslands and clarifies the relative contributions of snow parameters and meteorological factors to GPP. The research results are helpful to deepen the understanding of the carbon cycle process in alpine ecosystems of the Tibetan Plateau and provide a scientific basis for predicting the dynamic changes of GPP under the background of climate change.

Owing to complex climatic and topographic constraints， optical and microwave remote sensing applications in the Meili Snow Mountains region face significant limitations. This study employed high-precision photogrammetry using a DJI Mavic 3E drone equipped with a Haixingda iRTK20 GNSS system to survey the terminus of the Gongsenglongba Glacier on October 1， 2023， and October 1， 2024. Automated extraction of glacial morphological features was accomplished using a U-Net deep learning model. The result shows： The U-Net model achieved high accuracy and exhibited strong generalization capabilities in extracting glacial morphological features. Key observations during the study period （October 2023 to October 2024） include： a significant average surface elevation reduction of 2.37 meters at the glacier terminus； a continuous expansion trend in supraglacial lakes with relatively uniform spatial distribution； widespread development of ice cliffs showing progressive expansion towards the direction of glacier retreat， particularly pronounced in the middle and upper terminus sectors； and a concentrated distribution of supraglacial crevasses within the 3 910~4 000 m and 4 110~4 200 m elevation bands， contrasting with sparse occurrence elsewhere. This research establishes an intelligent extraction framework for glacial morphological features by integrating drone imagery with deep learning methodologies. The developed approach provides robust technical support for high-precision monitoring of glacial dynamics and their climatic responses.

Using radar remote sensing technology for large-scale identification and monitoring of landslide-prone areas is a current focus in disaster prevention and mitigation. High-voltage power transmission lines， as an essential part of the national power grid， can suffer significant economic losses if landslides occur in their vicinity. This study employs SBAS-InSAR technology and optical remote sensing imagery to conduct an in-depth investigation of landslide hazards and influencing factors in Longyan City， Fujian Province， with particular attention to landslide-prone areas near power lines. The research findings indicate that landslide disasters in Longyan City predominantly occur in areas with slopes and steep inclines， at elevations ranging from 200 m to 600 m， and are closely related to seasonal rainfall and mining activities. In addition， this study focused on seven significant deformation areas located within two kilometers of the transmission towers， with annual deformation rates ranging from -0.25 to -0.6 m， posing a direct threat to the transmission infrastructure. These research findings have been validated through field investigations， confirming the critical role of the proposed method in ensuring the safe operation of transmission lines.

The Datong River Basin is located at the border of Gansu and Qinghai provinces. It is the largest sub-basin of the Huangshui River Basin and an important component of the Qilian Mountains. The middle and lower reaches of the basin feature narrow， deep valleys and steep mountains， with abundant water and forest resources. Forest resources play a crucial role in water conservation， climate regulation， and soil and water preservation for this basin and its surrounding areas. Based on the Google Earth Engine （GEE） cloud platform， long-term time-series Landsat satellite imagery from 1987 to 2023 for the study area was obtained， and cloud-free images during the growing season （June to September） were composited annually. Using an improved VCT （Vegetation Change Tracker） algorithm combined with an automated forest sample extraction method， this study monitored the spatiotemporal changes and disturbances of forests in the basin over the past 37 years. The research indicates that forests are primarily distributed along the river valleys and mountain slopes in the middle and lower reaches of the basin. The improved VCT detection algorithm achieved an overall accuracy of 90.63% and a Kappa coefficient of 0.85 in identifying three categories： forest， non-forest， and forest disturbance， demonstrating the method's effectiveness and reliability in monitoring long-term forest change. Forest disturbances were more concentrated between 1989 and 2000， while they were relatively fewer from 2000 to 2023. The findings provide data support for forest resource management and ecological protection in the basin. The improved VCT detection algorithm combined with the GEE platform proves to be an effective tool for monitoring forest disturbances and dynamic changes.

Timely and precise mapping of imitate wild Chinese medicinal materials is of importance for fostering local economic development and safeguarding traditional Chinese medicine resources. To harness the potential of integrating GaoFen series and Sentinel-2 remote sensing imagery for mapping these materials， an innovative approach was adopted. This involved fusing the Segment Anything Model （SAM） and the Simple Non-Iterative Clustering （SNIC） model to achieve high-precision extraction of field boundaries and leveraging long-term time series data of the Normalized Difference Vegetation Index （NDVI） to constructed identification features from both inter-annual and intra-annual perspectives. The findings reveal that fusing the segmentation results of the SAM and SNIC models enables high-precision identification of cultivated land boundaries in mountainous regions， with Dice Coefficients ranging between 0.94 and 0.97. Furthermore， the coefficient of variation and variance have been identified as the most effective inter-annual change indicators for differentiating imitate wildness Chinese medicinal materials from other categories. Notably， May， August， and October emerge as critical intra-annual time windows for their accurate identification. The constructed identification system boasts an impressive accuracy rate of 88%. In Huachi County， the cultivated area of imitate wild Chinese medicinal materials has witnessed a cumulative growth of 49.7% over the past five years， with a consistency coefficient of 0.79 when compared to statistical data. This underscores the high identification accuracy of the technology and suggests its potential applicability in regions with similar agricultural structures， serving as a valuable reference.

Efficient extraction of spectral-spatial information is the core problem of hyperspectral remote sensing image classification. Transformers framework can effectively characterize the advantages of high-level semantic features， while CNNs can efficiently extract local features， and both of them have great potential in the field of hyperspectral image classification. In order to fully extract the spectral-spatial features， this paper proposes a Transformers-based high-frequency spectral-spatial information-enhanced hyperspectral image classification method （HFSST） by fusing the advantages of Transformers and CNNs networks. First， the low-level spatial-spectral features are extracted collaboratively by 3D-CNN and 2D-CNN. Second， the CAB （Converted Attention Block） attention mechanism is embedded in the Transformers Encoder （TE） to balance the channel and spatial information. Finally， a local convolution branch is added after the output features of the TE module to achieve better recovery of high-frequency information. The experiments are validated on Indian Pines， Pavia University， and Houston 2013 datasets， and the classification performance outperforms several current state-of-the-art methods， indicating that the method in this paper can strengthen the differentiability of inter-class features by enhancing the high-frequency spectral-spatial information. In addition， the correctness of the generalization of the hyperspectral image classification problem to the locally advanced semantic classification problem can be demonstrated.

With the launch of the new generation of geostationary meteorological satellites， their high-frequency and high spatiotemporal resolution observation characteristics provide unique advantages for dynamic fire point monitoring. Using AGRI data from China’s new generation geostationary meteorological satellite FY-4A， this study systematically compares and analyzes the monitoring performance and applicability of time-series and context-based methods at different development stages of forest and grassland fires. By calculating the brightness temperature change rate of adjacent times for the same pixel （time-series method） and constructing a local background window to statistically analyze the mean and variance of brightness temperature （context-based method）， the accuracy of fire point identification was analyzed for several typical forest and grassland fires in Sichuan Province and Inner Mongolia Autonomous Region. The results show that in the early stages of a fire， the time-series method is sensitive to sudden changes in brightness and temperature， effectively capturing fire points， with an accuracy rate of 80%~89% for identifying forest and grassland fires. In the middle stages of a fire， the spread of fire leads to significant spatial thermal anomalies， and the context-based method performs better， with an accuracy rate of 82%~88%. In the later stages of a fire， the fire source weakens， and the identification ability of a single method decreases. However， the combined identification strategy of the time-series and context-based methods can significantly improve monitoring capabilities， with the highest accuracy rates reaching 93.56% and 90.36% for forest and grassland fires， respectively. This study confirms that using targeted identification methods for different stages of fire development can effectively reduce missed and false alarms.

The integration of drone and handheld LiDAR point cloud data can significantly improve the accuracy and efficiency of forest tree parameter estimation. The Point Cloud Segmentation（PCS）algorithm， which combines region growing and threshold judgment， is used to segment individual trees from UAV LiDAR point clouds. The Density-Based Spatial Clustering of Applications with Noise（DBSCAN）algorithm is applied to segment tree trunks from handheld LiDAR point clouds. The Iterative Closest Point（ICP）algorithm is then used to fuse the point clouds. The individual tree detection rate with fused data reached 96.15%， higher than that of UAV LiDAR alone（93.28%）. For tree height estimation，the fused point cloud achieved an R² of 0.961， RMSE of 0.898 m， MAE of 0.726 m， and MAPE of 7.01%， outperforming both UAV LiDAR（R² = 0.942， RMSE = 0.946 m， MAE = 0.754 m， MAPE = 6.8%）and handheld LiDAR（R²=0.867， RMSE=1.447 m，MAE = 1.224 m， MAPE = 11.6%）. For Diameter at Breast Height（DBH）extraction， the fused point cloud has an R² of 0.979， RMSE of 1.373 cm， MAE of 1.155 cm， and MAPE of 11.87%， comparable to handheld LiDAR（R²=0.967， RMSE=1.286 cm， MAE=1.000 cm， MAPE=10.25%）. In crown width extraction， the fused point cloud achieves an R² of 0.733， RMSE of 0.894 m， MAE of 0.752 m， and MAPE of 35.78%， higher than handheld LiDAR alone（R²=0.561， RMSE=1.131 m， MAE=0.935 m， MAPE=42.42%）. The fused point cloud effectively overcomes the limitations of single data sources， providing an efficient technical means for precise forest resource monitoring and promoting the scientific and refined management of forest resources.

Global warming has led to frequent extreme weather events. As a vital ecological barrier and economic zone， Southwest China has experienced recurring droughts in recent years， posing severe threats to water security， agricultural production， and the ecological environment. Therefore， systematically investigating the spatiotemporal evolution patterns and future trends of drought events in Southwest China holds crucial theoretical and practical significance for deeply understanding regional drought dynamics and establishing effective disaster prevention and mitigation strategies. This study utilizes GRACE data to explore the spatial and temporal evolution characteristics of land water storage and groundwater storage in Southwest China between 2002 and 2022. It diagnoses the drought characteristics and future development trends in Southwest China based on the GWSA Drought Severity Index （DSI）. The results show that： （1） the reconstructed quantitative results of water storage and drought severity index are reliable， which can provide a scientific basis for drought evaluation； （2） both land water storage and groundwater storage have obvious seasonal fluctuations and continue to increase， with Guizhou and Chongqing being the most significant ones； （3） a total of 15 droughts have occurred during the 20-year period， with the highest frequency in spring and winter， and the most severe drought is the winter-spring drought in 2010； （4） The droughts in southwest Yunnan， west Sichuan， and south Guangxi have increased significantly during the past 20 years， and the regions of east Sichuan， Chongqing， Guizhou， and north Guangxi will face more severe droughts in the future. The study indicates that despite the overall increase in water storage in the Southwest， frequent and spatially unevenly distributed droughts necessitate enhanced water resource monitoring and drought prevention and control in high-risk areas to provide scientific support for regional disaster prevention and mitigation.

This study investigates the utilization status of arable land resources in Jilin Province and provides theoretical support for promoting their sustainable development. The Per Capita Cultivated Land Area Model and EF-NPP Model were used to assess the ecological balance and supply-demand dynamics of arable land in Jilin Province from 2001 to 2021. The Sustainable Utilization Index and Grey Prediction Model were then employed to predict the sustainable utilization trend of cultivated land from 2022 to 2031. Finally， rational control zones were proposed based on the findings.（1） From the perspective of food security， the supply-demand profit and loss coefficient of arable land increased from 0.532 to 0.710， indicating a trend of “surplus orientation and gradual optimization”.Spatially， this trend shows a “surplus in the central and western regions and a deficit in the southeastern region”.（2） From the ecological security perspective， the ecological profit and loss coefficient of cultivated land improved from -0.201 to -0.118， reflecting a positive trend. Spatially， this pattern reveals a “loss in the west and surplus in the east”. The Sustainable Utilization Index increased from 0.455 to 0.472， with projections indicating that cultivated land will remain in a weakly unsustainable utilization state （Ⅲa） from 2022 to 2031， suggesting that the sustainable utilization level has not yet reached an ideal state.（3） Based on the comprehensive profit and loss value and relevant policies， the utilization of arable land resources is classified into three types for zoning control： stable improvement， potential development， and rectification optimization. This classification aims to enhance the level of sustainable utilization and promote the sustainable development of arable land. The proposed control zones are expected to contribute to achieving sustainable utilization goals.

The study of spatiotemporal changes along the coastline has important guiding significance for coastal ecological environment protection and resource planning. Based on the GEE platform， Landsat remote sensing images from 2000 to 2023 were screened to calculate mNDWI（Modified Normalized Difference Water Index）， EVI（Enhanced Vegetation Index）， NDVI indices（Normalized Difference Vegetation Index） for obtaining water edge frequencies. The water edge frequency was calculated and visually interpreted to compare with high-precision remote sensing images to select segmentation thresholds for extracting the coastline of the Jiaodong Peninsula. This article uses the DSAS system（Digital Shoreline Analysis System） to analyze the spatiotemporal variation patterns of coastlines and show that from 2000 to 2023， the overall length of the coastline of the Jiaodong Peninsula increased by 285.77 km， with an increase of 12.42 km/a. The coastline advanced towards the sea by 126.58 m， with a rate of 8.53 m/a； Taking 2005 as the node， the length and movement rate of the coastline from 2000 to 2005 were lower than the average level. After 2005， the coastline grew rapidly and moved faster towards the sea. Among them， the growth rate of coastline length from 2010 to 2015 was the highest， reaching 47.55 km/a； The speed of coastline movement towards the sea has also accelerated rapidly since 2005， with the period from 2005 to 2010 being the fastest period of movement，NSM 69.56 m，LRR 14.81 m/a. On a spatial scale， the coastline of Yantai City has the greatest degree of expansion， NSM 212.81 m，LRR 10.92 m/a； The NSM and LRR of the coastline in Weihai City are the lowest， and the degree of change is the smallest； The changes in Qingdao's coastline are basically on par with the average level of the entire region. Due to the fact that coastal cities and coasts are mainly affected by human activities， an analysis of the intensity of human activities along the coast of the Jiaodong Peninsula found that since 2005， the area of high-intensity human activities in the coastal region of the Jiaodong Peninsula has significantly increased. It is preliminarily believed that the coastline of Jiaodong Peninsula pushed towards the sea as a whole from 2000 to 2023， and 2005 was the key node of change，and then the speed accelerated significantly，and human activities were the main driving factors.

The Yellow River Basin is abundant in water resources， and the water conservancy hubs within this region significantly influence the living conditions of residents and the economic development of the area. However， the geological environment in the Yellow River Basin is fragile， and many dams have been in operation for extended periods. Conducting health assessments of these dams is of great value and importance. This article employs spaceborne Synthetic Aperture Radar （InSAR） technology to investigate and analyze the spatiotemporal deformation characteristics of typical dams in the Yellow River Basin. To achieve high-precision measurements of dam deformation， this study utilizes InSAR phase unwrapping assisted by network optimization. The research focuses on the Gongboxia Dam in the upper reaches of the Yellow River and the Xiaolangdi Dam in the middle reaches. The experimental data is derived from Sentinel-1A， covering the period from 2017 to 2024. The findings indicate that the deformation rate in certain areas of the Gongboxia hydropower station dam exceeds 2 millimeters per year， while some areas of the Xiaolangdi water conservancy dam exhibit a deformation rate of 20 millimeters per year. The periodicity of the dam deformation time series correlates with fluctuations in the water level of the reservoir area. The results of this study provide essential support for the safe operation of water conservancy hubs in the Yellow River Basin and offer valuable insights for the health assessment of water conservancy dams in other regions of the Yellow River Basin.

Land Surface Temperature （LST）， as one of the key physical quantities characterizing the energy exchange between the land surface and the atmosphere at regional and global scales， has been listed as one of the parameters prioritized for determination by the International Geosphere-Biosphere Programme （IGBP）， and high-resolution LST products are of great significance for the characterization of land features. Based on exploiting the advantages of SDGSAT-1 TIS high-resolution （30 m） data， this paper explores an LST retrieval algorithm suitable for SDGSAT-1 TIS high-resolution data by combining its spectral response function characteristics. To validate its reliability， LST retrieval was performed on 9 periods of SDGSAT-1 TIS data across 4 regions. The results were comprehensively evaluated through Temperature-based method with 3 SURFRAD stations data and Cross-validation method with different LST products. The results showed that：①The LST retrieved using this method exhibits high reliability， with an R² of 0.91 in three SURFRAD stations and a consistency of up to 0.98 with Landsat C2L2. ②The LST retrieved using this method shows some variability across different land cover types. The smallest retrieval differences are observed for water， with mean errors of -1.62℃ and -1.49℃ compared to Landsat C2L2 ST and MOD11A1， respectively. Followed by vegetation， cropland， and artificial surfaces. While bare land exhibits the largest differences， with mean errors of -1.51℃ and 3.23℃， which better than it in Landsat C2L2 ST and MOD11A1 （4.29℃）. ③The LST products retrieved using this method are able to more accurately reflect temperature differences among various land features， better distinguish internal details of land features， and more precisely delineate land feature boundaries. Comprehensive analysis indicates that the LST retrieval method proposed in this paper based on SDGSAT-1 thermal infrared data meets the application requirements for generating land surface temperature products from thermal infrared remote sensing data. It effectively enhances the capability to characterize LST with higher precision， which holds significant implications for advancing research in global climate change and global carbon balance.

Total Suspended Matter （TSM） is one of the critical indicators to evaluate inland water quality. In this study， the TSM concentrations and in situ water reflectance spectra from 92 water samples （covering multiple seasons） were collected in four representative study areas of Poyang Lake. Retrieval algorithms to estimate TSM in Poyang Lake were developed based on simulated Landsat-8 image bands with measured spectral data. The results indicated that the inversion models based on remote sensing reflectance of Landsat-8 B4， B5 and B4/B3 had better accuracy， and the optimal fitting coefficients （R²） were 0.87， 0.90 and 0.86， respectively. The three optimal inversion algorithms based on B4， B5 and B4 /B3 were applied to Landsat-8 image which was quasi-synchronous with in-situ sampling date. These algorithms were used to extract TSM concentrations in Poyang Lake， followed by a verification and evaluation process. The application results demonstrated that the inversion algorithm with the highest validation accuracy was the exponential model based on B4. The Root Mean Square Error （RMSE） of TSM was 9.92 mg/L， and the percentage root mean square error （%RMSE） reached 36.6%. In this study， the index model based on the B4 band was employed to analyze the spatiotemporal distribution of TSM in Poyang Lake during 2022. Spatially， the areas with high TSM concentrations were primarily located in the northern channel connecting with the Yangtze River and the region extending from Songmen Mountain to the central lake. Temporally， TSM concentrations were significantly higher during the dry season compared to the wet season. These spatial and temporal variations of TSM in Poyang Lake indirectly reflect the influence of human activities on the water quality of the lake. This study evaluated and demonstrated the potential capability of Landsat-8 satellite imagery for effectively monitoring TSM in Poyang Lake， which can provide data and model support for protection of water environment in Poyang Lake.

Assessing the applicability of various spatiotemporal fusion methods in lake dynamic monitoring is of great significance for the optimization of monitoring schemes. Taking the typical area of Poyang Lake as the research object， five mainstream spatiotemporal fusion methods （STARFM， OL-STARFM， FSDAF， OL-FSDAF， and OL-FSDAF 2.0） are selected， and two experimental schemes， namely the dynamic transition group and the water extreme group， are designed. Fusion experiments are conducted in four sub-regions with different geomorphic features， and application validation is performed through water body extraction. The results show that in the dynamic transition group， the highest spectral accuracy of fused images is achieved by FSDAF， while the best performance in structure preservation is obtained by the fused images of OL-FSDAF 2.0. In the water extreme group， robust performance is exhibited by the FSDAF model， which is suitable for processing fragmented water bodies， and outstanding performance in local boundary enhancement is demonstrated by the OL-FSDAF 2.0 model. Water body extraction experiments show that the fused images can generally provide effective data support for lake water body extraction.The synergistic effects of hydrological stages and geomorphic conditions on the performance of spatiotemporal fusion models are revealed， and the deficiencies of existing literature regarding the application of spatiotemporal fusion methods in dynamic environments are remedied. Theoretical support is provided for the selection of optimal fusion models under different monitoring scenarios. Multi-source spatiotemporal fusion images effectively compensate for the limitations of single data sources， and high-frequency and high-precision data support is supplied for water resource management， ecological protection， and flood monitoring.

A systematic calibration framework is proposed for X-band dual-polarization one-dimensional phased array weather radars operating in narrow-transmit-narrow-receive mode. Firstly， static testing is performed on the radar under calibration， encompassing internal/external instrument tests， antenna far-field testing， and metal sphere calibration， to evaluate its performance under near-ideal conditions. Secondly， dynamic testing is conducted， involving precipitation data collection and analysis， as well as assessment of beam discrepancies across different elevation angles. Finally， a consistency comparison algorithm based on multi-range-bin weighting is employed， utilizing the standard S-band weather radar from the Changsha Meteorological Radar Calibration Center for direct performance comparison between the calibrated radar and the reference standard. Test results indicate that the calibrated X-band dual-polarization one-dimensional phased array weather radar generally performs well， with stable beam detection performance across different elevation angles. However， issues such as insufficient sensitivity and lower-than-expected reflectivity factors were observed， necessitating further troubleshooting to ensure compliance with operational requirements.

Forest canopy height is a critical parameter for estimating forest biomass and carbon sequestration. The Terrestrial Ecosystem Carbon Inventory Satellite， equipped with a full-waveform LiDAR system （referred to as the "GouMang full-waveform" data）， provides valuable information on forest vertical structure. Accurately extracting forest canopy height from GouMang full-waveform data is therefore of great significance. This study was conducted in the Genhe area， where preprocessed GouMang full-waveform data and UAV-LiDAR data were used to generate a Canopy Height Model （CHM） and extract footprint data. After data screening， footprints within the CHM coverage were selected， and the average CHM value within each footprint （hereafter referred to as footprint-average CHM） was calculated. The full-waveform data were then subjected to noise filtering， quality evaluation， and threshold processing to identify peak regions， from which waveform features were extracted. Subsequently， four machine learning models （XGBoost， AdaBoost， CatBoost， and GBRT） were constructed using the waveform features to predict the footprint-average CHM. A ten-fold cross-validation approach was employed， and model performance was evaluated using R²， RMSE， MAE， ME， Acc. The results showed that 108 high-quality footprints containing 201 full-waveform sequences were selected， with 109 waveform features extracted from each waveform. Among the models， XGBoost achieved the highest performance， followed by CatBoost， GBRT， and AdaBoost. The optimal model yielded R²=0.67， RMSE = 3.13 m， MAE = 2.31 m， ME = 0.12 m， and Acc = 68.54%. The findings demonstrate that through data screening， noise filtering， threshold processing， feature extraction， and model construction， forest canopy height can be accurately derived from GouMang full-waveform data， offering a new technical pathway for forest carbon sink estimation and biomass monitoring.

Offshore oil and gas platforms are important equipment for the exploration， exploitation and transportation of marine oil and gas resources. Monitoring offshore oil and gas platforms is of great significance for oil spill monitoring， greenhouse gas emission estimation， and marine ecological protection. Satellite remote sensing imagery have been widely used to monitor offshore oil and gas platforms. However， the “dynamic and static separation” strategy adopted in existing studies ignores the situation of ships passing by the vicinity of offshore oil and gas platforms， and additionally it is difficult to reflect the short-term changes of mobile offshore oil and gas platforms. To this end， this study proposed a novel dynamic and static separation strategy that considers the spatiotemporal overlap of targets， and monitored dynamics of offshore oil and gas platforms using time-series Sentinel-1 synthetic Aperture Radar （SAR） imagery. Firstly， the time-series Sentinel-1 remote sensing imagery and the Faster R-CNN deep learning model were used to extract bright targets at sea， and the spatial overlap of the target bounding box in the time-series images was calculated. The moving targets were removed by combining the residence time， and the target area threshold was set to remove small targets and to capture the extraction results of offshore oil and gas platforms. The dynamics of oil and gas platforms in typical areas were further monitored according to the appearance time and disappearance time of the platforms. This method is applied to monitor the dynamics of offshore oil and gas platforms in the central area of Weizhou oilfield during from 2015 to 2025. The extraction results were verified using Gaofen series satellite imagery and Sentinel-2 satellite imagery， yielding an extraction accuracy of 98.6%. A total of 80 offshore oil and gas platforms were identified in the study area， including 13 fixed platforms that existed throughout the monitoring period， 4 newly built platforms， and 63 mobile platforms that changed position during the monitoring period. The residence time of the 54 changed platforms was ≤30 days， indicating that offshore oil and gas platforms in this area are active， there are many short-term drilling and exploration operations. In summary， our method can accurately monitor the dynamics of offshore oil and gas platforms， and especially can capture the short-term dynamics of mobile offshore oil and gas platforms.