IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

{"title":"DHLNet: A Dynamic Hierarchical Lightweight Network for Enhanced Ship Detection in Remote Sensing Images","authors":"Jinyu Ou;Yijun Shen;Yanlian Du","doi":"10.1109/JSTARS.2025.3601579","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601579","url":null,"abstract":"Ship detection in remote sensing images is essential for maritime surveillance and environmental monitoring. Traditional methods often struggle to accurately identify ships in complex scenes or when targets are small, and recent deep learning approaches, while promising, still face tradeoffs between detection accuracy, inference speed, and computational complexity. To overcome these limitations, we propose dynamic hybrid convolutional network (DHLNet), a novel detection model comprising three specialized modules. DHLNet includes a dynamic hybrid block module that adaptively selects convolutional kernels for multiscale feature extraction, a faster hierarchical attention fusion block that integrates local details with global context through a multilevel attention mechanism, and a lightweight quality estimation BN head that balances spatial, channel, and scale features for efficient decoding. These innovations collectively enhance feature representation and improve detection performance without significantly increasing the computational cost. Extensive experiments on a self-collected ship dataset and public benchmarks (DOTA-Ship and VisDrone2019) validate the effectiveness of DHLNet. The proposed model outperforms state-of-the-art detectors (e.g., YOLOv8, YOLO-KAN, Mamba) in both mAP50 and F1-score metrics. For example, on our dataset, DHLNet achieves an mAP50 of 91.4%, which is 2.7% higher than that of YOLO-KAN. These results demonstrate that DHLNet effectively handles complex backgrounds and small targets, offering significant improvements in detection accuracy and efficiency for remote sensing-based ship detection.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21783-21806"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Runoff Dynamics and Its Regime Changes in the Major River Basins of Africa From GRACE and GRACE-FO Observations","authors":"Ayman M. Elameen;Shuanggen Jin;Isaac Sarfo","doi":"10.1109/JSTARS.2025.3601672","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601672","url":null,"abstract":"The major African basins supply freshwater to around 0.5 billion people, while monitoring runoff fluctuations in these basins is still challenging due to limited in-situ data and high costs. Hydrologic models are widely used for this purpose, but they have certain drawbacks with larger uncertainty and low accuracy in poorly gauged basins. This study attempts to address this issue by using Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) satellite measurements and remote sensing data to estimate runoff changes in five major African basins from 2003 to 2019. Moreover, a comprehensive framework was developed to quantify interannual and intraannual runoff regimes and their changes from the perspectives of magnitude, variability, and duration. Results showed that runoff changes estimated from GRACE/GRACE-FO in the major African basins were in good alignment with those from the Global Land Data Assimilation System, European Centre for Medium-Range Weather Forecasts Reanalysis 5, and Soil Conservation Service-Curve Number. Seasonal runoff increased in the Nile (0.12 to 0.46 mm/a, <italic>p</i> < 0.05) and Congo (0.52 to 0.76 mm/a, <italic>p</i> < 0.05) basins during 2003–2019, while it was decreased in the Zambezi (–1 to –0.34 mm/a, <italic>p</i> < 0.05) and Orange (–0.54 to –0.24 mm/a, <italic>p</i> < 0.05) basins during the same period. Further in-depth analysis showed that the impacts of climate change in the study area were the primary contributors to changes in runoff. Monthly runoff in the Nile, Congo, and Niger basins showed an increase in magnitude, duration, and variability. In contrast, the Zambezi and Orange basins experienced a decrease in runoff magnitude, along with reduced variability and duration. In addition, large-scale atmospheric circulations, such as El-Nino Southern Oscillation Index and Indian Ocean Dipole, have been found to be associated with changes in runoff within the study area, as demonstrated by correlation and wavelet analysis. Our findings provided valuable insights into long-term runoff changes in major African basins and enhanced the understanding of hydrologic processes in poorly gauged regions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22891-22926"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134550","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-Time Observation-Based Multiattitude ISAR Imaging Method for Moving Ships","authors":"Zhifeng Xie;Tao Lai;Qingyuan Shen;Xiaoqing Wang;Zhibing Wang","doi":"10.1109/JSTARS.2025.3601604","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601604","url":null,"abstract":"Long-time synthetic aperture radar observation facilitates the tracking and identification of moving targets. However, continuous changes in the attitude of a moving target during long-duration observations cause the azimuth signal’s time–frequency (TF) trajectory to curve. The curvature of the TF trajectory leads to image defocusing. The varying degrees of trajectory curvature for each scatterer caused by attitude changes prevent the traditional inverse synthetic aperture radar (ISAR) autofocusing method from achieving ideal focusing results. To address this issue, we propose an innovative ISAR imaging method based on TF trajectory extraction and compensation. This method divides the observation into subtime intervals for imaging, allowing the capture of various motion attitudes of the target. By transforming the TF trajectories with varying curvature into horizontal trajectories, the proposed method effectively handles nonstationary intervals, enabling attitude image acquisition during these periods. Experimental results demonstrate that our algorithm can produce multiple attitude images of ships during long-time observations, delivering clearer imagery even in complex motion scenarios.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21822-21839"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SD2GC-F: Enhancing Building Change Detection With Sequential Detection to Graph Comparison Framework","authors":"Ahram Song;Seula Park","doi":"10.1109/JSTARS.2025.3601823","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601823","url":null,"abstract":"Accurately detecting building changes based on high-resolution remote sensing imagery remains technically challenging owing to positional inconsistencies and geometric distortions. To address these limitations, this study proposes a novel framework that combines deep learning-based building detection with graph-based structure comparison. Detectron2, an object detection model, is employed to extract building instances and derive accurate node positions by computing the center points from rotated bounding boxes. The minimum spanning tree algorithm is then applied to create building graphs from these nodes based on the connectivity between adjacent buildings. Subsequent analysis of structural variations within this graph enables change detection and identifies which building changes will concomitantly alter their links to neighboring buildings. Experimental results across synthetic and real-world datasets (including off-nadir imagery) confirm that the proposed method effectively captures building changes in complex urban environments. Notably, it achieved high change-detection accuracy, particularly in scenarios involving relief displacement and perspective distortion, wherein conventional methods often yield high false positive rates. This approach offers practical utility for large-scale urban monitoring and addresses the key challenges posed by complex positional discrepancies and environmental variations in remote sensing imagery.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21840-21854"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Multisource Fusion and Processing of Satellite–UAV Remote Sensing Images and Ground Acoustic Signals for Cotton Water and Fertilizer Management","authors":"Jing Nie;Haochen Li;Yang Li;Hongwei Li;Sezai Ercisli","doi":"10.1109/JSTARS.2025.3601798","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601798","url":null,"abstract":"Cotton is one of the most important economic crops in the world, and effective water and fertilizer management is particularly important to optimize cotton yield and water and fertilizer use efficiency. Remote sensing technology and real-time processing of multisource data provide innovative solutions for accurate monitoring of cotton water and fertilizer status: through satellite remote sensing to observe cotton growth from a macroscopic point of view, combined with real-time transmission of ultrasonic signal data of cotton drought stress, water and fertilizer anomalies can be quickly identified; for the normalized vegetation index (NDVI)-similar areas, the simultaneous fusion of drone hyperspectral data and real-time monitoring data from ground meteorological stations is used to analyze changes in canopy structure through texture features. In addition, crop drought stress acoustic signals are dynamically collected based on acoustic monitoring equipment, and water-related acoustic features, such as amplitude–frequency–energy, are extracted in real time. This real-time synergistic processing of heterogeneous data from multiple sources achieves dynamic diagnosis of water and fertilizer status from the canopy to the root zone, and provides decision support for optimal water and fertilizer management at different fertility periods. By setting up different water and fertilizer gradient experiments, we comprehensively analyzed the correlation patterns between yield, water and fertilizer utilization, and multisource parameters. The results show that NDVI and contrast have significant application potential in plant status monitoring, while crop water status monitoring based on ground acoustic signals also shows a broad application prospect.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22287-22299"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adversarial Example Generation With Pseudo-Siamese Adversarial Generative Networks for Multimodal Remote Sensing Images","authors":"Haifeng Li;Hang Cao;Jiahao Cui;Jing Geng","doi":"10.1109/JSTARS.2025.3602278","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602278","url":null,"abstract":"In the field of remote sensing, the increasing diversity of remote sensing image modalities has made the integration of multimodal remote sensing image information a prevailing trend to increase classification accuracy. Concurrently, the study of adversarial samples for multimodal remote sensing images has emerged as a crucial area for enhancing network robustness. However, existing adversarial attack strategies designed for single-modal data often fail to extend effectively to multimodal adversarial attack tasks, mainly due to the following challenges: Multimodal correlation: Since multimodal data provide complementary auxiliary information, attacking a single modality alone cannot disrupt the correlated features across modalities; directional differences in multimodal adversarial samples: The adversarial perturbation directions exhibit substantial discrepancies and conflicts, which considerably diminish the overall attack efficacy. To address the first challenge, we propose a pseudo-Siamese generative adversarial network that employs modality-specific generators to simultaneously produce perturbations for each modality from the latent feature space, enabling simultaneous attacks on multiple modalities. To address the second challenge, we introduce a collaborative adversarial loss that enforces consistency in the perturbation directions across modalities, thereby mitigating the conflicts between multimodal perturbations and improving attack effectiveness on multimodal classification networks. Extensive experiments demonstrate the vulnerability of multimodal fusion models to adversarial attacks, even when only a single modality is attacked. Specifically, we show that our proposed pseudo-Siamese adversarial attack method considerably reduces the overall accuracy of the U-Net and Deeplabv3 models from 81.92% and 82.20% to 0.22% and 4.16%, respectively, thereby validating the efficacy of our approach.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24588-24601"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134792","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Retrieval of Cloud Droplet Number Concentration and Liquid Water Path From Satellite Spectral Data","authors":"Jessenia Gonzalez;Sudhakar Dipu;Gabriel Jimenez;Gustau Camps-Valls;Johannes Quaas","doi":"10.1109/JSTARS.2025.3601981","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601981","url":null,"abstract":"Accurate estimation of cloud microphysical properties, particularly the cloud droplet number concentration (<inline-formula><tex-math>$N_{mathrm{d}}$</tex-math></inline-formula>) and liquid water path (<inline-formula><tex-math>$L$</tex-math></inline-formula>), is essential for improving our understanding of aerosol-cloud interactions (ACI). Traditional satellite retrievals of these variables depend on assumptions that often lead to systematic errors. In this study, we present a machine learning (ML) framework that directly predicts <inline-formula><tex-math>$N_{mathrm{d}}$</tex-math></inline-formula> and <inline-formula><tex-math>$L$</tex-math></inline-formula> from satellite spectral reflectance and radiance data, thereby circumventing conventional assumptions in retrieval algorithms. We use data from ICOsahedral nonhydrostatic large Eddy simulations simulations and moderate resolution imaging spectroradiometer-like spectral channels to evaluate the relevance of spectral features using traditional statistical techniques and ML interpretability methods. Our results demonstrate that, using a neural network model, <inline-formula><tex-math>$L$</tex-math></inline-formula> can be accurately predicted using three spectral channels, achieving a coefficient of determination (<inline-formula><tex-math>$R^{2}$</tex-math></inline-formula>) of 0.93 and a normalized mean absolute error (nMAE) of approximately 16% . The prediction of <inline-formula><tex-math>$N_{mathrm{d}}$</tex-math></inline-formula> requires seven channels, achieving an <inline-formula><tex-math>$R^{2}$</tex-math></inline-formula> of 0.76 and an nMAE of approximately 26% . As expected, <inline-formula><tex-math>$N_{mathrm{d}}$</tex-math></inline-formula> requires a richer spectral representation than <inline-formula><tex-math>$L$</tex-math></inline-formula>. Our ML approach enables a more direct and flexible estimation of cloud properties by avoiding assumptions linked to intermediate retrieval variables. This framework offers new insights into spectral sensitivities and supports an alternative and potentially more robust assessment of ACI from satellite observations, potentially leading to improvements in climate model constraints.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21910-21922"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134551","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAM-Mamba: A Two-Stage Change Detection Network Combining the Adapting Segment Anything and Mamba Models","authors":"Yutian Li;Wei Liu;Erzhu Li;Lianpeng Zhang;Xing Li","doi":"10.1109/JSTARS.2025.3601739","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601739","url":null,"abstract":"Remote sensing change detection (RSCD) plays a crucial role in environmental monitoring, resource management, and land planning. However, remote sensing images typically involve multiple surface types, and the similarities and differences between these types make change detection (CD) in complex scenes even more challenging. Therefore, extracting and integrating effective features in complex scenes becomes a key issue in RSCD. In recent years, SAM2 and Mamba have made a significant impact in the field of computer vision, but further research is needed in the remote sensing domain to fully leverage their advantages. Thus, this article proposes a two-stage CD network named SAM-Mamba. Specifically, we use SAM2 as the encoder to extract key features from remote sensing images and perform preliminary decoding to generate mixed prompts, which are then passed to the guiding encoder (prompt encoder) to generate accurate and stable feature boundaries. These boundaries are then fused with the key features and passed to the decoder. In the decoding stage, we integrate the Mamba model, which is designed to efficiently model long sequences and capture spatiotemporal correlations. Furthermore, to enhance the ability to capture complex changes, we design the frequency-guided feature refinement module and the dynamic feature fusion module. Our SAM-Mamba outperforms other state-of-the-art methods on four datasets (e.g., Intersection over Union and F1-score are improved by 3.40%/2.96%, 1.02%/1.07%, and 2.52%/2.42% on LEVIR-CD+, WHU-CD and SYSU-CD datasets, respectively). The code of SAM-Mamba are publicly released at <uri>https://github.com/lytwxyyyds/Sam-Mamba</uri>.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21607-21619"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Spatio-Temporal Weighted Adjustment Network for Remote Sensing Image Change Detection","authors":"Yue Yin;Xuejie Zhang;Longbao Wang;Shufang Xu;Zhijun Zhou;Guanxiu Wang;Yadi Bi","doi":"10.1109/JSTARS.2025.3601996","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601996","url":null,"abstract":"With the rapid advancement of deep learning, substantial progress has been achieved in remote sensing change detection (CD). However, there are still two key challenges. First, the widespread scene context interference hinders the accurate detection of change regions; second, the existing methods are difficult to simultaneously detect change regions across different scales. To address these issues, this article presents a cross-spatio-temporal weight adjustment network (CWA-Net) with three core optimizations. First, we propose a cross-spatio-temporal differential fusion attention mechanism, which utilizes differential features extracted by the backbone network to enhance bitemporal features. Through the coordinated use of multiple attention mechanisms and channel exchange, the mechanism promotes deep interaction and fusion of bitemporal features, effectively reinforcing change region representations while mitigating scene interference. Second, we design a multiscale selection and aggregation module that adaptively selects and aggregates the optimal scale features from multiscale features, enhancing the model’s capability to capture change regions at different scales. In addition, we put forward a two-type change-feature complementarity strategy, which reweights change features extracted via subtraction and concatenation during the aggregation of multiscale feature maps, thereby enhancing feature complementarity and enriching change information. Finally, extensive experiments on four remote sensing CD datasets demonstrate that CWA-Net, based on a simple backbone network ResNet18, outperforms existing state-of-the-art SOTA methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22047-22066"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring Subglacial Lake Activity in the David Glacier Region, East Antarctica, Using a DInSAR Displacement Integration Approach","authors":"Taewook Kim;Hyangsun Han;Hoonyol Lee;Hyeontae Ju","doi":"10.1109/JSTARS.2025.3601588","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601588","url":null,"abstract":"Subglacial lakes significantly influence ice sheet dynamics, necessitating precise monitoring of their activity. This study presents a novel approach that integrates differential interferometric synthetic aperture radar (DInSAR) displacements from Sentinel-1 data to estimate high-resolution time series of ice sheet surface elevation changes associated with subglacial lake activity. Applying this approach to the David Glacier region in East Antarctica from 2016 to 2023, we identified and analyzed the activity of Lake David₂ and newly identified subglacial lakes, David_2-1 and David_A. The ice sheet elevation changes derived from the DInSAR displacement integration showed strong agreement with Ice, Cloud, and land Elevation Satellite-2 observations and allowed for detailed monitoring of subglacial lake activity. The ice sheet surface above Lake David_A showed a marked rise in early 2016 due to lake filling, followed by a stable period until 2020. A rapid surface decline occurred in late 2020 due to drainage. Since 2021, the surface decline has continued at slower rates, indicating gradual water level lowering. Lake David_2-1 filled by early 2021 and subsequently exhibited alternating drainage and filling. The ice sheet surface above Lake David₂ exhibited sustained uplift until 2020, attributed to continuous water filling, and then transitioned to a phase of drainage. Hydraulic potential-derived basal water pathways suggested that Lake David_A was likely supplied by undetected inflows, while Lake David₂ primarily received water from a southern pathway rather than from Lake David_2-1. This study demonstrates the effectiveness of time-series DInSAR for subglacial lake monitoring, overcoming limitations of altimetry-only observations and providing insights into subglacial hydrology and ice sheet dynamics.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22175-22192"},"PeriodicalIF":5.3,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11133742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}