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.

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.

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.

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.

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。

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.

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．

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.

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.

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.

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.

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.

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.

To monitor the spatiotemporal variations of soil moisture over the Tibetan Plateau—known as the "Roof of the World" and the "Water Tower of Asia"—in the early 21st century， this study evaluates multiple soil moisture products and adopts the AMSR-E/2 soil moisture dataset retrieved using the Multi-Channel Collaborative Algorithm （MCCA）， which demonstrated the highest accuracy. Based on data from 2002 to 2022， we applied Sen’s slope estimator combined with the Mann–Kendall test to systematically analyze the spatiotemporal patterns of soil moisture changes and its relationship with precipitation across the plateau. The results show that the Tibetan Plateau has experienced a general wetting trend in the early 21st century， with the proportion of significantly increasing soil moisture trends （31.52%） far exceeding that of significantly decreasing trends （1.70%）. Most river basins exhibit a pattern of “high increase in dry areas and low increase in wet areas”.Furthermore， notable differences exist in the trends among various river basins and monsoon zones. Precipitation is identified as a key driving factor influencing soil moisture changes on the Tibetan Plateau.

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.

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.

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.

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.

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.

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.

Accurate farmland information is of great significance for ensuring national food security. Traditional farmland extraction methods have limitations when dealing with increasingly complex remote sensing images. The development of deep learning has brought new approaches for farmland extraction. However， the classic DeepLabV3+ network has problems such as a large number of training parameters， less than ideal image segmentation accuracy， and poor generalization ability in practical applications. To address these issues， this study proposes a lightweight DeepLabV3+ network integrated with an attention mechanism. In this network， the Xception， which is the main structure of DeepLabV3+， is replaced with MobileNetV2 to reduce the number of training parameters. The channel attention mechanism SENet is introduced to improve the segmentation accuracy. The loss function is replaced with the Hybrid function to enhance the generalization ability of the model.The research results show that： （1） After lightweighting， the average training time of the network is reduced from 7.45 minutes to 1.86 minutes， and the model training parameters are decreased from 208.7 MB to 24.73 MB； （2） The precision， recall， and Mean Intersection over Union （MIoU） of the model reach 90.26%， 90.08%， and 81.58% respectively， all of which are superior to those of other comparative models； （3） The absolute value of the relative error of the extraction results in Gaomiao Village， Yongchuan District， is 7.95%， which performs better than the DeepLabV3+ network model. In conclusion， the improved DeepLabV3+ network can effectively improve the farmland extraction effect. While reducing the number of parameters and increasing the operation speed， it also improves the extraction accuracy. It has certain universality and transferability and can provide accurate spatial distribution information of farmland for agricultural planning.

Soil erosion evaluation based on soil erosion estimation model requires high-quality input data， including land use classification data， however， the traditional land use classification method has the problems of low efficiency and the phenomenon of “heterogeneity and homogeneity” when facing the task of multi-class classification. Therefore， this study tries to apply deep learning to soil erosion evaluation， adopts the semantic segmentation model with Swin Transformer as the backbone network to realize high-precision land use classification， and applies the classification results to the RUSLE model to evaluate the degree of soil erosion. It is verified that the overall accuracy of the semantic segmentation model reaches 95% and has good generalization performance. The results of soil erosion evaluation show that soil erosion in Poyang County is dominated by light erosion， and spatially presents the distribution characteristics of band and point. The results show that land use classification based on deep learning has a wider application prospect in the field of soil erosion evaluation.

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.

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.

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.

Accurate estimation of large-scale terrestrial evapotranspiration （ET） is critically important for hydrological， ecological， agricultural， and water resource management. Penman-Monteith （PM） equation coupled with the Jarvis stomatal conductance model plays a pivotal role in remote sensing-based evapotranspiration estimation models. However， current PM-Jarvis frameworks rely on optimization-based methods to derive maximum stomatal conductance （g_sm） for vegetation or International Geosphere-Biosphere Programme （IGBP） categories， which lack a biophysical foundation. These approaches overlook interspecific variations in stomatal traits and fail to capture the temporal dynamics of leaf-level g_sm， leading to significant estimation errors. This study proposes a novel methodology to calculate g_sm using species-specific stomatal anatomical characteristics （stomatal length and density） and a physiological constraint function based on Normalized Difference Vegetation Index （NDVI）， enabling accurate estimation of surface evapotranspiration.. Stomatal anatomical characteristics can determine g_sm of various plant types and physiological constraint function can simulate the dynamic changes of g_sm. Validation using observational data from sites of AmeriFlux network demonstrates that compared to the approach that does not distinguish vegetation types and based on IGBP fixed g_sm， the precise calculation of dynamic g_sm significantly improves evapotranspiration estimation accuracy. The coefficients of determination （R²） increase from 0.626 and 0.726 to 0.83， and the Root Mean Square Error （RMSE） decreases from 33.31 W/m² and 27.72 W/m² to 21.81 W/m²， respectively. Therefore， a more detailed consideration of vegetation physiological differences in large-scale evapotranspiration estimation proves to be an effective method for enhancing accuracy.

The extraction of groundwater for winter wheat irrigation in the Hebei Plain has resulted in a continuous decline in the regional groundwater levels， triggering a range of ecological and environmental issues and intensifying the contradiction between water resources and food security. Employing remote sensing technology to accurately extract the irrigation information of winter wheat is significantly important for realizing the sustainable development of water resources and rational allocation of water resources. In this study， the MOD09Q1 and TRIMS LST data were utilized to calculate the NDVI and TVDI of the Hebei Plain from October 2020 to June 2021. Based on the NDVI time series characteristics of winter wheat， the planting area of winter wheat in Hebei Plain in 2021 was extracted. Leveraging the response characteristics of TVDI to winter wheat irrigation， a method for monitoring irrigation information was determined， and the irrigated area during the different growth periods of winter wheat was inverted， subsequently obtaining the spatial distribution of the number of irrigations throughout the entire growth period of winter wheat. The results showed that the planted area of winter wheat in Hebei Plain was 20.91×10³ km² in 2021， and the extraction results were highly accurate. The less precipitation during the growth period of winter wheat had minimal impact on the TVDI of the wheat fields， while the irrigation resulted in minimum in the TVDI time series. In 2021， the irrigated area of winter wheat in the Hebei Plain with overwintering water， rejuvenation water， pulling water and grouting water was 10.23×10³ km²，14.31×10³ km²，9.79×10³ km²， and 7.14×10³ km²， respectively. The proportions of the areas with irrigation events of 0， 1， 2， 3， and 4 times for winter wheat in the Hebei Plain were 6%， 25%， 39%， 25%， and 5%， respectively. This study proposed a method to obtain the actual irrigated area of winter wheat， which provided technical support for guiding irrigation production in groundwater overexploitation areas.

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.

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.

Surface soil moisture is a key variable controlling water cycle， carbon cycle， and energy exchange between land and atmosphere. Currently， passive microwave observations in the L-band are considered the optimal wavelength for retrieving surface soil moisture information. However， the low spatial resolution of passive microwave observations is insufficient to meet the needs of applications such as hydrological modeling， weather forecasting， agricultural planning， and water resources management. The direct observation of passive microwave remote sensing is brightness temperature （TB）. Therefore， obtaining high-resolution brightness temperature is the foundation for obtaining high-resolution soil moisture. Addressing this limitation， our study assesses the potential of refining Soil Moisture Active Passive （SMAP） L-band data resolution by integrating it with X-band data from the Advanced Microwave Scanning Radiometer-2 （AMSR-2）. We employed a Time-Series Regression （TSR） approach， incorporating soil and vegetation descriptors such as the Microwave Vegetation Index （MVI）. The enhanced resolution of the downscaling was validated using ELBARA-III TB data， the results show that the precision of TSR-MVI downscaling TB can be consistent with the original SMAP data. The minimum Root Mean Square Error （RMSE） at V-polarization is 9.864 K， and the maximum correlation coefficient （R） is 0.861. The downscaling results based on TSR-MVI method are superior to those of Backus-Gilbert optimal interpolation， with more information and higher image clarity. Our findings suggest that the TSR approach， when combined with AMSR-2 TB data， can effectively downscale SMAP L-band data.This method can downscale the L-band SMAP brightness temperature， and the results are superior to those of the BG product.

China's independently developed HY-2 series of power satellites provide important data support for global ocean wind field observation and research. By fusing the sea surface wind field data acquired by multi-source remote sensing satellites to form wind field fusion data with high temporal and spatial resolution， it is of great significance to the study of typhoon disasters and the safeguarding of the safety of ship navigation and offshore operations. Cross-validation of wind field fusion data is a prerequisite for the large-scale application of this data. In this paper， the Northwest Pacific Ocean， which is prone to typhoon disasters and rich in fishery resources， is selected as the study area， and ERA5 reanalysis data， CCMP wind field data and CERSAT wind field data are utilized as the reference data. By obtaining the standard deviation and correlation coefficients of wind speed and direction between different datasets， the cross-validation analysis of HY-2 series satellite wind field fusion data between different seasons from December 2019 to November 2021 was carried out. The results show that： ① the HY-2 series satellite wind field fusion data match the ERA5 reanalysis data better than the CCMP wind field data and CERSAT wind field data. ② In different seasons， the wind speed and direction accuracy of the HY-2 series satellite wind field fusion data is higher in summer and fall， and relatively lower in winter and spring. ③ The wind speed and wind direction of the HY-2 series satellite wind field fusion data from December 2019 to November 2021 have high accuracy， with standard deviations of 1.54 m/s and 13.29°， and correlation coefficients of 0.92 and 0.88， respectively， and can be widely used in the Northwest Pacific Ocean.

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.

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.

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.

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.

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.

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.

A ccurately and rapidly extracting tobacco planting distribution information holds significant importance for optimizing crop planting structures and scientifically planning tobacco field layouts. Mianchi County， a key tobacco planting base in Henan Province， features a hilly terrain characterized by fragmented plots and complex mixed-crop patterns. These characteristics make it challenging to meet the demand for precise tobacco extraction in hilly areas using optical remote sensing features alone. Consequently， integrating multi-source remote sensing imagery， selecting optimal remote sensing phases for tobacco classification， identifying significant features for tobacco remote sensing classification， and exploring feature optimization methods are crucial for enhancing the accuracy and reliability of tobacco classification in such regions. This study leverages the Google Earth Engine （GEE） platform and utilizes Sentinel-1/2 imagery to extract spectral， polarization， index， and texture features of ground objects within the study area. Notably， the index features incorporate the red-edge index， derived from red-edge spectral calculations. Employing an object-oriented approach， six classification schemes were designed based on the Random Forest algorithm to investigate the impact of various feature type combinations on tobacco planting distribution information extraction. The research findings reveal the following key insights： Optimal Segmentation Scale： In the object-oriented method using the SNIC algorithm， a segmentation scale of 3 pixels yields the clearest and most complete land class details in the segmented imagery. Feature Reduction via J-M Distance： By applying the J-M distance separability metric， the number of classification features was reduced from 28 to 15， effectively retaining the essential information required for accurate classification. Superior Classification Scheme： Among the six tested schemes， the feature selection approach based on the J-M distance algorithm， which integrates multiple feature variables， demonstrates the best performance in tobacco extraction. This scheme achieves user accuracy and producer accuracy rates exceeding 90%， with an overall accuracy of 94.88% and a Kappa coefficient of 0.94.

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.