Satellite altimetry enables non-contact，large-scale Earth observation，providing technical support for monitoring changes in water levels of lakes where there is a lack of ground-based hydrological stations. The ICESat-2 laser altimeter features small footprints and high measurement accuracy，enjoying advantages in monitoring small-to medium-sized lakes. Therefore，this study extracted water level data from October 2018 to August 2022 for 1248 lakes across China based on ICESat-2 ATL08 data. The extracted data were validated using measured water level data from 18 lakes and Hydroweb data from 36 ones. Subsequently，based on the division of China's five major lake regions，this study analyzed variations in water levels of 957 lakes that were observed for over two years in at least four campaigns. The results show that the root mean square errors （RMSEs） between ICESat-2-derived and measured lake levels showed a minimum of 0.097 m. The cross-validation with Hydroweb data yielded a correlation coefficient of 0.95 and a minimum RMSE of 0.085 m. These results demonstrate the high precision and accuracy of the water level retrieval based on the ICESat-2 data. The lake levels on the Tibetan Plateau exhibited a slow rising trend，while those in northwestern China showed a declining trend. In eastern China，the water levels of large lakes displayed no significant variation trend，whereas those of small lakes showed pronounced fluctuations. Overall，the lake levels across China exhibited a gently rising trend. This study achieved high-precision measurement and monitoring of variations in lake levels across China，providing a scientific basis for water resource protection，ecological management，and the exploration of the responses of lake levels to human activities and climate change.

Changes in lake area and water volume exert significant impacts on the ecological environment of arid regions. Targeting East Dabuxun Lake，Golmud River Basin，Qinghai Province，this study developed a multi-index random forest algorithm based on the Google Earth Engine （GEE） cloud platform and Landsat imagery to extract the lake area from 1987 to 2021. Then，an area-water level relationship was established using laser altimetry data from ICESat and CryoSat satellites to estimate changes in water volume. Finally，the impacts of natural factors and human activities on the lake were evaluated，using ERA5-Land climate data and records of potash mining，along with correlation analysis and random forest-based contribution assessment. The results indicate that the temporal changes in lake area over time can be divided into five stages：expansion，shrinkage，recovery，re-shrinkage，and rapid recovery. Spatially，the lake exhibited a pattern of shrinkage in the south and expansion towards the northwest. From 2003 to 2021，the water volume of East Dabuxun Lake showed an upward trend. Temperature，glacier and permafrost melting，and solar radiation were identified as the main natural factors influencing lake area，with contribution rates of 31.0%，29.4%，and 15.5%，respectively. In terms of human activities，potash mining emerged as a major driver of lake area changes after 2010. Based on predictions by the auto-regressive moving average model （ARIMA），the lake area is projected to decline to 302.78 km² by 2030.

The Zhuzikou delta，located on the east branch of the Ouchi River，has been one of the fastest-growing and largest marshlands in the Dongting Lake area over the past century. Based on the historical maps and aerospace remote sensing data from 20 periods since 1933，this paper analyzed the spatiotemporal evolution of the delta through remote sensing interpretation and historical comparative analysis. The results showed that over the past 90 years，the Zhuzikou delta had been persistently advancing towards the lake area，with the deposited high-level bottomland being reclaimed into embankments. The old river channels had been abandoned and evolved into inner lakes of a blocked river type within the embankments，while the new river channels had been continuously extended towards the lake area along changing paths caused by multiple course changes. The channels of the lower reaches in the Zhuzikou delta have extended 38.99 km towards the lake area at an average annual rate of 428.46 m/a. The sedimentation area in the delta has expanded to 340.19 km² at an average annual rate of 3.74 km²/a. Reclamation of the delta has been carried out in parallel with sedimentation，with a cumulatively reclaimed area of 230.42 km²，accounting for nearly 50% of the total sedimentation area. The expansion rate has been varying across periods，including a slow expansion period from 1933 to 1954，a rapid expansion period from 1954 to 1998，a period of relative stability from 1998 to 2010，and a slight shrinkage period from 2010 to 2024. The delta has started to shrink since 2010，marking an end of its expansion history since 1933. The research findings provide original data for the scientific protection and restoration of wetland resources in the Dongting Lake.

Natural lakes，as a precious natural resource in Anhui Province，are defined as large water bodies formed by natural water accumulation in surface depressions. Different from artificial water bodies such as reservoirs and ponds，they are formed and evolved under the control of geological，climatic，and hydrological conditions. Moreover，characterized by relatively stable forms and ecosystems，they play important roles in regional ecological balance，economic development，and socio-cultural activities. Therefore，investigating their spatiotemporal changes and driving forces is highly significant for the protection of natural lakes in Anhui Province. This study collected data from the Thematic Mapper （TM） onboard the Landsat-5 satellite and the Operational Land Imager onboard the Landsat-8 satellite. Then，the natural lakes in Anhui Province were extracted from these data using a human-computer interaction method. This study investigated spatiotemporal changes of the lakes using the dynamic degree and land use transfer matrix. The driving factors of the changes were examined from two aspects：natural factors and social factors. From 2002 to 2022，the natural lakes in Anhui Province showed a phased change in both area and number，characterized by an initial decrease followed by an increase. In terms of lake area，the total area of the lakes decreased by 366.5 km². Specifically，from 2002 to 2012，the lake area decreased rapidly，reaching its lowest value of 2850.12 km² in 2012. From 2012 to 2022，the area gradually recovered，rebounding to 2960.97 km² in 2022. In terms of the lake number，there was a cumulative reduction of four lakes over the period. Specifically，the period from 2002 to 2007 saw a rapid decline，with an average annual decrease of 1.4 lakes. From 2007 to 2022，the number gradually rebounded，with an average annual increase of 0.2 lakes，reaching a total of 105 lakes in 2022. The changes in land use around typical lakes predominantly involved the conversion to arable land and other water bodies. In Anhui Province，the natural lake areas were influenced by both natural and social factors. The decreased runoff of the Huaihe River resulted in a decrease in inflow into the lakes，which serves as a key natural factor contributing to the reduction of the natural lake area in Anhui Province. Human activities and land use changes around the lakes are identified as important social factors for the reduction of the lake area.

To protect the water quality of the Xinfengjiang Reservoir and monitor the eutrophication risk，this study developed an analytical model for remote sensing inversion of chemical oxygen demand （COD_Mn） based on the underwater radiative transfer process. This model comprehensively takes into account three water quality parameters that influence the underwater light field：chlorophyll a，total suspended matter，and COD_Mn. The model was applied to conduct multi-temporal monitoring of eutrophication in the reservoir and its surrounding rivers. Through accuracy verification，the model achieved a root mean square error of 0.68 and a mean absolute percentage error of 25.22%，demonstrating its reliability in complex water environments. The spatiotemporal analysis of the water quality in Xinfengjiang Reservoir revealed the consistent good quality of the main body over the long term. However，due to extensive aquaculture and anthropogenic discharges，the Zhongxin River exhibited frequent eutrophication，which may pose a potential threat to the overall water quality of the reservoir. It is recommended to enhance monitoring of the Zhongxin River，promptly address illegal discharges，and implement ecological engineering measures such as vegetative drainage ditches in the watershed. These efforts can effectively reduce agricultural non-point source pollution，contributing to the restoration and improvement of the ecological environment of Xinfengjiang Reservoir.

Coastlines serve as one of the most essential basic geographic elements. However，conventional methods generally face challenges in the accurate detection of their location，due to instantaneous remote sensing imaging and dynamic tidal phenomena. In response to this，this study developed a novel coastline extraction model that incorporates information on surface moisture content derived from long-time-series satellite remote sensing imagery. First，all available remote sensing images covering the study area during the target period were acquired to construct a high-quality remote sensing image stack. Second，the wetness components indicative of the surface moisture content were obtained using the tasseled cap transformation （TCT），from which a wetness index stack was constructed. Then，the wetness components were subjected to maximum value synthesis using the maximum spectral index composite （MSIC） algorithm，generating a maximum water surface composite image. Finally，the composite image was segmented using the OTSU algorithm to extract accurate coastline information. Validation experiments were conducted on Zhoushan Island using the Google Earth Engine （GEE） cloud computing platform and remote sensing imagery from the operational land imager （OLI） onboard the Landsat 8 satellite. The results indicate that the proposed model can precisely locate different types of coastlines with high spatial accuracy. Compared to visual interpretation，the model exhibited a mean deviation and a root mean square error （RMSE） of 3.42 m and 6.79 m，respectively，with 99.42% of validation points falling within one pixel width. This study provides an effective technical framework for high-accuracy coastline extraction，holding great significance for scientific management and sustainable development of coastal resources.

In optical remote sensing images with complex scenes and rich land cover information，the sea-land segmentation faces challenges such as low positioning accuracy and blurred edges. Therefore，this paper proposed a deep convolutional network model and a sea-land segmentation method that integrate contextual semantic information and edge features. First，the rich target semantic information was extracted from remote sensing images using the FusionNet semantic segmentation network module. Then，multi-scale and hierarchical contextual semantic features were extracted from the segmentation network using the enhanced atrous spatial pyramid pooling （ASPP） module and contextual attention module. Additionally，an edge extraction sub-network was built to extract multi-scale edge features. Finally，the semantic features and edge features were combined through a fusion module，thereby achieving accurate sea-land segmentation. This method was tested with two typical representative datasets. The results showed that this method achieved an overall prediction accuracy of 98.21%，an F1 score of 97.64%，and a boundary F1 score of 89.36%，all significantly outperforming other models. Particularly in complex backgrounds，this method can effectively improve the accuracy of segmentation and edge detection，demonstrating definite advantages in the segmentation of artificial coastlines and ports.

The current high-resolution remote sensing images involve complex scenes that are difficult to analyze. Meanwhile，owing to the diverse scenes，there is a lack of accurate reference obtained from the sample database. Therefore，this paper proposed a self-learning segmentation method for high-resolution remote sensing images，with reference to the visual dual-drive cognition mechanism. Based on the principle of visual perception，this method interpreted the typical ground objects in the scene through unsupervised adaptive analysis. In addition，it achieved self-learning identification of typical ground objects by integrating a neural network. Finally，the segmentation results were self-checked and corrected by combining unsupervised analysis and neural network learning. Using real high-resolution remote sensing image data containing complex ground scenes，the comparative experiments were conducted between the proposed method and two popular deep neural network segmentation methods：mask region-based convolutional neural network （Mask R-CNN） and scalable vision transformer （ScalableViT）. The results showed that the proposed method can maintain robust and reliable segmentation accuracy，and outperformed others in terms of ground object cognition，generalization performance，and anti-interference ability. As such，it proved to be a cost-effective and practical approach.

Landslide disasters are frequent and widespread in southwestern China. The accurate identification and mapping of landslides using remote sensing imagery are of great significance for disaster prevention and mitigation. However，in complex environments，traditional remote sensing detection methods are often prone to misidentification due to background noise in the imagery. This paper proposed a dual-fusion landslide detection network （DLDNet） to improve landslide detection accuracy under challenging conditions. First，based on existing landslide samples，landslide simulation was conducted in complex environments using data augmentation techniques. Second，the ConvNeXt was adopted as the feature extraction backbone of DLDNet to capture more complex landslide features. Then，an attention module enhanced with deformable convolution was introduced to better focus on landslide-related information. Finally，a dual-fusion feature pyramid network （DFPN） was designed to thoroughly integrate feature information across different scales and receptive fields. The experimental results show that the proposed DLDNet achieved average precision （AP） scores of 56.9% for bounding box detection and 52.5% for segmentation，10.4 and 10.7 percentage points higher than those of the baseline model （Mask R-CNN）. Compared with other landslide detection models，the DLDNet demonstrates higher detection accuracy and a lower false alarm rate. The method，characterized by accurate landslide detection in complex environments，can support rapid landslide identification and emergency response.

Hyperspectral remote sensing image classification has attracted widespread attention，yet the performance of classification methods remains greatly limited by challenges such as spectral variability （same object with different spectra），spectral confusion （different objects with similar spectra），and limited availability of training samples. To fully exploit the spatial-spectral features of hyperspectral images，this study proposed an improved network integrating residual convolution and neighborhood attention mechanisms. The proposed method consists of：（1） a residual-based spectral feature extraction module combining residual connections and a 3D convolutional neural network （3D-CNN）；（2） a spatial-spectral feature fusion module using mixed convolutions；and （3） a neighborhood attention module designed to enhance the model's ability to focus on homogeneous regions. Experiments were conducted on three public hyperspectral datasets-Indian Pines，Pavia University，and Houston 2013. The results demonstrate that the proposed method achieves higher classification accuracy compared to recent state-of-the-art approaches. Using less than 10% of the samples for training，it attains overall accuracies of 99.39%，99.67%，and 98.64%，respectively，confirming its capability for high-accuracy classification under small-sample conditions.

The deep learning-based change detection of remote sensing images has seen rapid advances in the past few years. However，it still faces challenges for change detection in complex scenes，such as incomplete recognition and high false detection rates. In response to these challenges，this paper proposed the FTUNet，a network based on SNUnet that integrates the fast Fourier transform （FFT） and efficient multi-head self-attention （EMHSA）. Specifically，the FFT module in the network enabled style unification of dual-temporal images，reducing false detection caused by “pseudo changes” due to external factors such as light variations. Additionally，the EMHSA was introduced in the feature extraction stage to fully extract the contextual information from the feature maps，thereby enhancing the segmentation integrity of target changes. Experiments on the LEVIR-CD and SYSU-CD public datasets showed that the FTUNet exhibited increases of 1.42 and 1.53 percentage points in F1 score，as well as increases of 2.31 and 2.07 percentage points in intersection over union （IoU），compared to the SNUNet.

In response to the challenges posed by substantial parameters and the loss of building details during downsampling，this study，inspired by lightweight networks，designed a building extraction network （SD-BASNet） incorporating depthwise separable residual blocks and dilated convolution. First，a depthwise separable residual block was designed in the prediction module of the deep supervision encoder-decoder. Depthwise separable convolution was incorporated into the backbone ResNet to prevent oversized convolutional kernels and reduce the number of network parameters. Second，to mitigate the potential decline in accuracy due to network lightweighting，dilated convolution was integrated into the encoder layer of the post-processing optimization module. This strategy effectively expands the receptive field of feature maps，thereby capturing broader contextual information and enhancing the accuracy of building feature extraction. Experiments on the WHU building dataset showed that the proposed network achieved an mIoU of 92.25%，an mPA of 96.59%，a Recall of 96.50%，a Precision of 93.79%，and a F1-score of 92.61%. Compared with current semantic segmentation networks，including PSPNet，SegNet，DeepLabV3，SE-UNet，and UNet++，the SD-BASNet demonstrated significantly improved accuracy and better completeness of building extraction. Compared with the baseline BASNet，the SD-BASNet also exhibited reductions in both parameter count and runtime，demonstrating its effectiveness.

In the field of multivariate alteration detection （MAD） of remote sensing images，change vector analysis in posterior probability space （CVAPS） is a widely used method. However，the CVAPS，which employs support vector machines to estimate the posterior probability vectors of remote sensing image pixels，is susceptible to various factors such as different objects with the same spectrum，the same object with different spectra，and mixed pixels in remote sensing images. These factors make it difficult to accurately estimate the magnitude and direction of the posterior probability vectors of complex pixels，consequently affecting the accuracy of multivariate alteration detection. Therefore，under the framework of CVAPS，this paper proposed a MAD method using angle thresholds，which employed the fuzzy C-means clustering to decompose mixed pixels and coupled a context-sensitive Bayesian network. When the angle is less than a certain threshold，the pixel is identified as the change type represented by the standard change vector. Experimental results show that the proposed algorithm exhibited superior alteration detection performance，achieving higher change detection accuracy than other algorithms.

Time-series interferometric synthetic aperture radar （TS-InSAR） technology has been widely used in ground deformation monitoring and prediction. However，current research remains insufficient in the correlation and temporal lag between groundwater and ground deformation. Moreover，InSAR-based prediction models for ground deformation mostly rely on a single InSAR data，which limits the prediction accuracy and generalization ability of the models. To address these challenges，this study proposed a combination-long short-term memory （C-LSTM） model that integrates groundwater level，rainfall，and InSAR deformation data. This model was employed to evaluate the prediction and accuracy of single-factor and multi-factor models，respectively. The results revealed a temporal lag between ground deformation and changes in groundwater level. The optimal feature combination，obtained through model training using groundwater and rainfall data，exhibited significant improvements in prediction accuracy compared to single-factor predictions，with the coefficient of determination （R²） increasing by 2.45%，1.52%，4.16%，8.08%，5.08%，and 1.45% respectively. The model enhances the prediction accuracy of ground deformation by incorporating model feature combinations with high correlation with ground deformation.

Multispectral （MS） and panchromatic （PAN） images serve as primary data sources for visible-near-infrared optical remote sensing imagery. In a typical land cover classification workflow，the spatial resolution of MS images is generally enhanced using pixel-level fusion methods，followed by image classification. However，the pixel-level fusion process is characterized by considerable time consumption and inconsistency with the optimization objectives of land cover classification，failing to meet the demand for end-to-end remote sensing image classification. To address these challenges，this paper proposed a dual-stream fully convolutional neural network，DSEUNet，which obviates the need for pixel-level fusion. Specifically，two branches were constructed based on the EfficientNet-B3 network to extract features from PAN and MS images，respectively. It was followed by feature-level fusion and decoding，thus outputting the ultimate classification results. Considering that PAN and MS images focus on different features of land cover elements，a spatial attention mechanism was incorporated in the PAN branch to enhance the perception of spatial information，such as details and edges. Moreover，a channel attention mechanism was incorporated in the MS branch to improve the perception of reflectance differences across multiple bands. Experiments on the 10-meter land cover dataset and ablation studies of the network structure demonstrate that the proposed network exhibited higher classification accuracy and faster inference speed. With the same backbone network，DSEUNet outperformed traditional pixel-level fusion-based classification methods，with an increase of 1.62 percentage points in mIoU，1.36 percentage points in mFscore，and 1.49 percentage points in Kappa coefficient，as well as a 17.69% improvement in inference speed.

The time-series monitoring and prediction of tailings dam stability have always been a major concern in China’s mine safety research. Focusing on a tailings dam in Anhui province，this study obtained 26 periods of longitudinal deformation data from six characteristic monitoring points on the dam surface，using InSAR and GNSS technologies. Based on the data，a least-squares adjustment model with restricted parameters was established. Combined with the initial three-dimensional coordinates of the monitoring points as polynomial correction parameters，the InSAR and GNSS data were fused to improve the data accuracy. Then，time-series prediction of deformation data was conducted for the monitoring points using the back propagation （BP） neural network，thus obtaining their future deformation data. Experiments were carried out to compute and compare the deformation data and corresponding root mean square error （RMSE） of each period before and after fusion，wherein the fused GNSS and InSAR data were evaluated with the root mean square error （RMSE） as the accuracy standard. The results showed that the post-fusion RMSE decreased by up to 70.61% and by at least 4.34% （average：25.91%），compared to pre-fusion data. Furthermore，the neural network model was used to repeatedly train the fused InSAR data from periods 1 to 22，with periods 23 to 26 serving as the test set，ultimately outputting the data of each point for periods 23 to 26. Compared to the GNSS data，the RMSE of the outputs were less than 1.5 mm. These results can provide reliable technical support for the time-series monitoring and prediction of tailings dam stability.

Land use change is a primary driver of carbon storage changes in terrestrial ecosystems. Investigating its impact on carbon storage is crucial for optimizing territorial spatial planning and reducing regional carbon emissions. Focusing on Xianyang City，this study analyzed changes in land use and carbon storage over the past two decades （2000—2020） based on corresponding land-use data from 2000，2010，and 2020，using the patch-generating land use simulation （PLUS） and integrated valuation of ecosystem services and tradeoffs （InVEST） models. Moreover，it predicted the distribution of carbon storage in 2030 under four scenarios：natural growth，urban development，cropland protection，and ecological protection. The results indicate that in 2000，2010，and 2020，Xianyang City exhibited carbon storage of 10 047.534×10⁴ t，10 120.754×10⁴ t，and 10 030.210×10⁴ t，respectively，characterized by a pattern of an initial increase followed by a decrease. The conversion of grassland to forest and cropland to construction land was identified as the main factor contributing to the increase and decrease in carbon storage，respectively. Among the four scenarios for 2030，cropland protection and ecological protection scenarios displayed increased carbon storage，while the urban development scenario experienced the most significant decline in carbon storage due to the rapid expansion of construction land. Areas with high carbon storage were mainly concentrated in northern Xianyang，whereas those with low carbon storage were distributed in the southern economic centers. Looking ahead，the future planning in Xianyang should fully consider the impacts of land use on carbon storage，ecological land protection，and restriction of extensive construction land expansion. By doing so，the city can achieve dual optimization of land use and carbon emissions. The findings provide a scientific basis and data reference for enhancing ecosystem carbon sink capacity and optimizing terrestrial spatial planning in Xianyang City.

The leaf area index （LAI） serves as an important parameter for investigating the global carbon cycle，water cycle，energy exchange，and climate change. At present，there are multiple LAI products with different time series and resolutions. Comparative analysis of these products can not only reveal their suitability in various regions，but also provide suggestions for optimizing their algorithms. Focusing on the typical areas in Anhui province，this study compared and assessed the spatiotemporal consistency of MuSyQ LAI，MODIS LAI，and GLASS LAI products from the perspective of their capacity to characterize the spatiotemporal characteristics of vegetation. The results indicate that the spatial distribution of LAI obtained from the three products was consistent with the spatial distribution of vegetation，revealing good spatial consistency. However，there existed differences in LAI values and spatial heterogeneity. To be specific，the MODIS LAI displayed generally higher values than the other two products. The MuSyQ LAI exhibited lower values than the GLASS LAI in cultivated land and deciduous broad-leaved forests，but higher values in evergreen forests. As spatial resolution increases，the three products all showed better spatial details，with the MuSyQ LAI featuring the most pronounced spatial heterogeneity in land cover distribution. As the elevation varies，the MODIS LAI and GLASS LAI values vary in a consistent pattern，while the MuSyQ LAI value varies in a different pattern. The three products presented altitude-varying LAI values at low-altitude areas，whereas they showed varying change patterns at high-altitude areas across different sample areas. Temporally，the three products presented relatively complete time-series curves of the annual average LAI value over the years and similar seasonal trends，which can effectively characterize the phenological characteristics of crops and the seasonal variations of different plants. Overall，the three products exhibited good spatiotemporal consistency，all of which can reflect the spatial distribution and temporal changes of vegetation. However，they were different in the LAI value and spatial heterogeneity. Among them，the MuSyQ LAI is more suitable for investigating inter-annual changes in areas featuring complex terrains and high heterogeneity in land cover distribution，while the GLASS LAI is more suitable for long-time-series studies in large areas.

Hyperspectral remote sensing （HRS） technology，with its high spectral resolution and extensive spectral coverage，demonstrates significant potential in geological prospecting. Focusing on the Qianhongquan gold deposit in the Beishan orogenic belt，Gansu Province，this study conducted altered mineral mapping and component analysis，using HRS data from the AHSI sensor on the ZY-1 02D satellite and the self-developed hyperspectral mineral mapping technique，GeoAHSI，revealing their spatial distribution characteristics. Besides，ground-based spectral measurements were conducted on typical profiles to validate the spectral data，thereby assessing the reliability of the hyperspectral mineral mapping results. The results indicate that the primary altered minerals in the Qianhongquan gold deposit and its surrounding rocks include sericites （low-aluminum，medium-aluminum，high-aluminum，and iron-rich muscovites），calcites，dolomites，epidotes，and chlorites. Their distribution is closely related to ductile shear zones，with the distribution of sericites，chlorites，and epidotes being particularly significant within these zones. This spatial correlation provides critical indicators for regional prospecting. Additionally，it was observed that the 2 200 nm absorption feature of sericites and the 2 250 nm absorption feature of chlorites exhibit marked enrichment in silicon （Si） and iron （Fe） around ore bodies，which is closely correlated to the chemical compositions of the minerals. By enhancing the identification of weak spectral features，this study successfully applied HRS technology to mineral identification and spatial distribution analysis. These findings provide a scientific basis for further exploration of the Qianhongquan gold deposit and offer valuable references and guidance for the application of HRS in similar deposits.

Changes in the vegetation index can reflect variations in vegetation cover and growth in the region to some extent. Monitoring the changes in vegetation index time-series data plays a significant role in local agricultural management. However，existing methods for vegetation index time-series data reconstruction face challenges such as a single data source input and low spatial resolution of reconstruction results. In response to this，this paper proposes a reconstruction method for vegetation index time-series data that integrates the satellite data standardization method and the crop reference curve method. Consequently，it reconstructed vegetation index time-series data with high spatiotemporal resolution for winter wheat in the study area in 2021，including normalized differential vegetation index （NDVI） and enhanced vegetation index （EVI）. The results show that after reflectance normalization，the coefficient of determination （R²） for GF-1 satellite and VIIRS surface reflectance data in red，green，infrared，and near infrared bands generally increased by 0.05%，with a few exceeding 0.1%. The root mean square error （RMSE） was reduced，with the majority decreasing by 0.01. In contrast，the relative root mean square error （rRMSE） showed a reduction of about 2%. Most data from the GF-6 satellites exhibited an increase of about 0.12 in R²，a decrease of 0.03 in RMSE，and a general decline in rRMSE ranging from 3% to 4%. In contrast，the data from the Sentinel-2 satellite show an overall increase of about 0.05 in R²，as well as a decrease of around 0.001 and 2% in RMSE and rRMSE，respectively. The accuracy assessment results for the reconstructed high-resolution vegetation index time-series data indicate that the NDVI time-series reconstruction results presented high R² values in the validation period，with five validation images reaching 0.49 and above. The RMSE was less than 0.1 in all validation periods，while the relative error （RE） was less than 15% in most cases，with only one validation image reaching 18%. Similarly，the EVI time-series reconstruction results also exhibited high R² values，with five validation images above 0.44. Both RMSE and rRMSE values were less than 0.15 and 20%，respectively.

On December 18，2023，a M6.2 earthquake struck Jishishan Bonan，Dongxiang，and Salar Autonomous County，Gansu Province，China，with a maximum seismic intensity of VIII，causing severe environmental damage. This study aims to determine the basic parameters of the seismogenic fault and analyze its movement. To this end，this study，based on the two-pass differential interferometric synthetic aperture radar （D-InSAR） technique，obtained the coseismic deformation field of this earthquake using ascending and descending orbit from the Sentinel-1A satellite，as well as digital elevation model （DEM） data before and after the earthquake. Based on the dislocation theory in a homogeneous elastic half-space （the Okada dislocation model），a mapping model was established to link coseismic deformation and fault movement. The coseismic deformation field was used to fit the seismogenic fault，followed by the inversion for basic parameters of the seismogenic fault and the simulation of fault slip distribution. The results show that the maximum deformation from the ascending orbit data was about 6.65 cm，while that from the descending orbit data was about 7.12 cm. The seismogenic fault exhibited a strike of 308.14°，a dip angle of 61.57°，and a slip angle of 71.42°. Moreover，the fault presented a maximum slip of approximately 0.29 m located approximately 8 m below the surface and a moment magnitude of M_w6.17. The fault is characterized by thrust movement，with a minor left-lateral strike-slip movement. Based on the regional geological structure，the seismogenic fault is speculated to be the southern margin fault of Laji Mountain，with surface rupture caused by the earthquake.

Forest above-ground biomass （AGB） is recognized as an important indicator of forest productivity. Rapid and accurate estimation of forest AGB is crucial for sustainable forest management and carbon cycle research. Based on spaceborne light detection and ranging （LiDAR） data from the global ecosystem dynamic investigation （GEDI） and Sentinel-2 optical data，this study extracted GEDI L2B，Sentinel-2 remote sensing features，and topographic factors （elevation，aspect，and slope） in the study area. Among them，variables were determined through Pearson correlation analysis. Then，this study constructed the partial least squares regression （PLSR），gradient boosting regression tree （GBRT），and random forest （RF） models for forest AGB inversion. Consequently，this study estimated these models’ potential for forest AGB estimation and analyzed the spatial distribution differences of forest AGB. The results indicate that the estimation using multi-source data consistently outperformed that using single-source data. Among them，the RF model based on GEDI and Sentinel-2 data exhibited the best performance （R²=0.76，root mean square error （RMSE）=23.02 t/hm²），followed by the GBRT model，while the PLSR model performed the worst （R²=0.26）. In terms of spatial distribution，within the elevation range of 1 200~1 800 m，forest AGB density increased with elevation. Slope variation had little effect on forest AGB density，but a pronounced decrease in AGB density was observed on steep slopes. Aspect analysis showed that semi-shaded and sunny slopes exhibited high forest AGB density，while shaded and semi-sunny slopes presented similar values. Slope-aspect interaction analysis revealed that sunny and semi-sunny slopes displayed the highest total forest AGB on gentle and moderate slopes，respectively. In contrast，forest AGB significantly decreased across all orientations on flat and steep slopes，with a more significant decline observed on shaded and semi-shaded slopes. These findings provide a scientific basis for formulating forest protection and cultivation policies at the provincial level.

Investigating the regional economic development of Yunnan Province-a radiation center facing Southeast Asia-and Myanmar-a country along the Belt and Road Initiative-is of great significance for promoting the construction of a China-Myanmar community with a shared future. Based on NPP/VIIRS nighttime light data，as well as spatial analysis methods including the centroid model，standard deviation ellipse，and Moran's I index，this study analyzed the spatiotemporal characteristics of economic development in the Yunnan-Myanmar region from 2013 to 2022. The results indicate a significant correlation between nighttime light and gross domestic product （GDP） data in the Yunnan-Myanmar region. From 2013 to 2022，the total nighttime light intensity in the Yunnan-Myanmar region showed a steadily increasing trend. From the perspective of the characteristics of economic development direction in the region，the economic centroid generally shifted southwestward first and then northeastward. The area of the standard deviation ellipse trended upward from 2013 to 2020 but trended downward in 2022. The long axis of the ellipse showed an increasing trend before 2020 but decreased slightly thereafter，while the short axis showed a stable increasing trend. The azimuth remained largely unchanged. In terms of the spatial correlation of economic development in the region，areas with high nighttime light intensity were primarily concentrated in the central Yunnan urban agglomeration，while those with low nighttime light intensity were mainly distributed in the eastern and western parts of Myanmar. This study can provide a valuable reference for economic and trade exchanges between China and Myanmar，as well as for the implementation of the Belt and Road Initiative.

The alpine gorges in northwest Yunnan，important ecological reserves in China，are facing increasingly prominent environmental problems due to accelerated urbanization. Insights into the spatiotemporal changes in eco-environmental quality are of great significance for eco-environmental protection and construction in the alpine gorges of Northwest Yunnan. This study selected Landsat TM/OLI remote sensing images from 1990，1995，2001，2008，2015，and 2022 as the data source to extract four ecological indices：normalized difference vegetation Index （NDVI），wetness （WET），normalized difference bare soil index （NDBSI），and land surface temperature （LST）. Consequently，a remote sensing ecological index （RSEI） was constructed to assess and monitor the eco-environmental quality of the alpine gorges in northwest Yunnan from 1990 to 2022. The results indicate that from 1990 to 2022，the average RSEI in the study area showed a trend of an initial decline followed by an increase. Specifically，the RSEI reached its lowest value of 0.450 in 1995 and then increased continuously from 0.450 in 1995 to 0.604 in 2022. Over this period，the proportion of areas with excellent and good eco-environmental quality increased by 22.03%，while those classified as poor and very poor eco-environmental quality decreased by 14.49%. These variations were predominantly composed of improvements，covering 62.42% of the study area. Spatially，areas with very poor quality were primarily concentrated in agricultural areas，urban construction land，along the Jinsha River，low-altitude areas with sparse vegetation，and the slopes of landform intermontane basins （Bazi） in Heqing County. In contrast，areas with excellent quality were mainly distributed in high-altitude mountainous regions characterized by lush vegetation and minimal human disturbance. Moreover，the land use type was identified as the main driving factor influencing the eco-environmental quality in the study area. The strongest interaction was observed between elevation （X1） and land use （X6），exerting the greatest impacts on eco-environmental quality in the study area. Besides，areas with clay soils were dominated by poor and very poor quality. The magmatic rock areas displayed a clear trend of ecological deterioration，while the sedimentary rock area presented significant improvements. Conversely，the metamorphic and complex rock areas maintained relative stability.

Amid global changes，China’s vegetation ecosystem has undergone profound transformations. However，there is an urgent need to thoroughly explore the mechanisms underlying the ecological evolution of vegetation in different ecological subregions and historical periods，as well as their differences. Therefore，based on normalized difference vegetation index （NDVI） data，this study investigated the spatiotemporal evolution of vegetation across six major ecological subregions in China and its driving mechanisms using methods such as the gravity center model，lag analysis，geographical detectors，and partial correlation analysis. The results indicate that from 2000 to 2020，mainland China witnessed a decreasing trend in vegetation coverage from east to west. Vegetation coverage increased in all six ecological subregions，with the highest increase （slope of change） observed in the south-central part of China （0.003 9） and the lowest in eastern China （0.002）. From 2000 to 2010，regions with increased vegetation coverage accounted for 92%，and this proportion dropped to 71% from 2010 to 2020. Heterogeneous lag times were observed across different vegetation types in varying regions. Specifically，cultivated vegetation and shrubland generally exhibited a 1 to 3-month lag in response to precipitation；cultivated vegetation and coniferous forests presented a lag limited to the current month in relation to temperature，and broadleaf forests generally displayed a 1 to 2-month lag in response to temperature. Precipitation is identified as the dominant factor driving vegetation changes in North China and the northeastern，northwestern，and southwestern parts of China. In eastern China，land use and gross domestic product （GDP） represent the primary driving force behind vegetation change. In the south-central part of China，both precipitation and land use serve as dominant factors. The results of this study can provide significant data support for vegetation restoration and protection in different ecological regions.

This study aims at investigating variations in soil moisture and vegetation net primary productivity （NPP） in the Qingling River Irrigation Area，Yunnan （elevation 1 515~1 876 m），a typical subtropical alpine climate region. To this end，initially，this study recognized land surface temperature （LST） and normalized difference vegetation index （NDVI） as explanatory variables，leveraging remote sensing technology for rapid and long-term sequential monitoring. Subsequently，the SMAP L4 soil moisture product was downscaled to a 30 m spatial resolution using the random forest adaptive window regression algorithm. Then，the water stress parameter of the CASA model was modified using the land surface water index （LSWI），which integrated multi-source remote sensing data，such as surface reflectance，to estimate NPP. Following spatial resampling，a 30 m resolution NPP spatial distribution was achieved. Finally，multiple land cover scenarios，including forest land，paddy fields，and irrigated farmland，were established. The Pearson correlation coefficient was introduced for the quantitative evaluation of the spatial relationship between soil moisture and NPP in the study area. In terms of the spatial distribution of soil moisture，the study area exhibited higher values in the north and lower values in the south during summer，while lower values in the northwest and higher values in the southeast and south during winter. Compared to field measurements，the inverted NPP results showed a R²>0.7 and a RMSE<0.3. Both summer，winter，and annual average NPP values at the pixel level showed an increasing trend over time. Spatially，scenarios such as paddy fields and forested land presented correlation coefficients exceeding 0.5. Among these，forest land was least sensitive to water stress，while paddy fields and irrigated farmland were most affected. This study establishes a monitoring and feedback mechanism for the soil moisture-NPP balance from seasonal and spatial perspectives in the study area.