International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"DNFNet: a brightness-adaptive lightweight RGB–thermal network for day–night UAV wildfire segmentation","authors":"Peng Cheng,&nbsp;Luli Zhang,&nbsp;Bin Wang,&nbsp;Junyi Liu,&nbsp;Haigang Sui","doi":"10.1016/j.jag.2026.105572","DOIUrl":"10.1016/j.jag.2026.105572","url":null,"abstract":"<div><div>Wildfires significantly threaten human safety and property, and unmanned aerial vehicles (UAVs) have become a crucial tool for regional fire monitoring. To reduce false positives and negatives caused by diurnal lighting changes and modality differences in day–night RGB–Thermal (RGB–T) UAV fire segmentation, we propose DNFNet, a brightness-adaptive, lightweight dual-branch, multi-level complementary fusion network. The method introduces a smooth sigmoid-gated brightness factor to explicitly adjust the fusion weights between RGB and thermal streams. At shallow levels, the Local Detail Enhance-Cross Modal Fusion (LDECMF) Module compensates for the spatial detail deficiency of thermal images; at deeper layers, the Global Semantic Enhance Cross-Modal Fusion (GSECMF) module, along with a Multi-Scale Feature Fusion (MSFF) module, enhances semantic consistency and cross-scale responsiveness. This design allows the collaborative optimization of local boundary accuracy and global semantic coherence. To validate our approach, we constructed DNFire, a UAV RGB–T fire dataset covering diverse scenes, viewpoints, and day-night conditions, and conducted comparative and ablation tests on DNFire and two public datasets against several state-of-the-art methods. Results show that DNFNet achieves an IoU of 0.854 and an F1 score of 0.921 on day–night data, while remaining computationally efficient (12.06 million parameters and 9.15 GFLOPs). The model runs at approximately 25 FPS on low-power edge devices, balancing high accuracy with real-time deployment potential. These results indicate that DNFNet offers a practical, efficient solution for day–night RGB–T UAV wildfire monitoring.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105572"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CGHU-CD: A contrast-guided hierarchical unsupervised method for detecting Martian anthropogenic changes","authors":"Jiang Long,&nbsp;Sicong Liu,&nbsp;Yuxuan Hu,&nbsp;Jiayi Wu,&nbsp;Tao Liu,&nbsp;Yonggang Xiong,&nbsp;Kaiyue Luo,&nbsp;Jie Zhang,&nbsp;Kecheng Du,&nbsp;Huan Xie,&nbsp;Xiaohua Tong","doi":"10.1016/j.jag.2026.105556","DOIUrl":"10.1016/j.jag.2026.105556","url":null,"abstract":"<div><div>Over the last six decades, Mars exploration missions (both successful and unsuccessful) have inevitably altered the Martian surface and environment. Consequently, accurately detecting non-salient anthropogenic changes resulting from these exploration activities plays an essential role in environmental evaluation. However, precise identification of changed areas and their semantic information on Mars is challenging due to interference from natural changes and complex terrain. To address this, we propose an efficient contrast-guided hierarchical unsupervised method, i.e., CGHU-CD, for sample-free Martian anthropogenic change detection (MACD) using high-resolution orbital images. The proposed approach first performs a global patch-level detection, employing feature description and adaptive thresholding, to maximize the removal of unchanged regions, yielding coarse-grained change candidate pairs. A local pixel-level detection method then exploits the contrastive features of anthropogenic changes, including brightness and texture differences relative to the surrounding Martian background, to progressively enhance change information and refine the detection results. Furthermore, an automatic classification method incorporates inherent object brittleness and size information to distinguish different change types. To evaluate the effectiveness of the designed approach, we constructed a MACD dataset collected from High-Resolution Imaging Science Experiment orbiter images, which include four Mars exploration missions. Extensive experiments comparing our CGHU-CD with seven advanced techniques demonstrate its superior capability in detecting non-salient anthropogenic changes of diverse sizes, shapes, and appearances across varied Mars scenarios. It exceeded these existing unsupervised methods by 2.08% to 78.43% in the F1-score measure. The CGHU-CD also achieved superior classification performance to existing techniques, with an increase of 2.77% to 23.28% in the Kappa value. At last, the sample-free CGHU-CD method exhibited superior potential for detecting anthropogenic changes at the Zhurong landing site across diverse sensors and spatial resolutions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105556"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methane detection and emission estimation from space: Benchmarking PRISMA and EnMAP with the GHGSat constellation","authors":"Daniele Settembre,&nbsp;Davide De Santis,&nbsp;Giovanni Schiavon,&nbsp;Fabio Del Frate","doi":"10.1016/j.jag.2026.105557","DOIUrl":"10.1016/j.jag.2026.105557","url":null,"abstract":"<div><div>The use of satellite observations to detect and quantify methane emissions from localized sources is constantly increasing. The comparison between results generated by different sensors aboard satellites can be challenging due to differences in spatial and spectral resolution, retrieval approaches, and acquisition timing. In this study, we evaluate the capability of the PRISMA hyperspectral satellite to detect methane plumes and reproduce plume structures, benchmarking its methane detection and estimates against corresponding data provided by the GHGSat constellation. Geolocation refinement is applied to PRISMA imagery, followed by methane retrievals using the MAG1C algorithm. Results show that PRISMA detects almost 60% of the emission sources identified by GHGSat and reproduces the main spatial trends of methane enhancements along the plume. The analysis along the plume centerline highlights consistent patterns of higher methane concentrations near the source, followed by gradual dilution along the plume tail. A multi-sensor comparison, including EnMAP, further illustrates the temporal evolution of a plume by showing the observed methane flux trend over a two hour interval. Overall, the correlation between PRISMA and GHGSat flux estimates showed a Pearson coefficient of 0.95, although PRISMA systematically seems to reveal lower methane enhancements with respect to GHGSat. The results obtained in this work confirm that hyperspectral satellite observations can provide a consistent representation of methane plume dynamics across different sensors, looking forward robust cross-sensor analysis of emission sources.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105557"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A physics-guided transfer learning approach for atmospheric correction of low-altitude remote sensing at extreme zenith angles","authors":"Ming Xu,&nbsp;Jun Chen","doi":"10.1016/j.jag.2026.105589","DOIUrl":"10.1016/j.jag.2026.105589","url":null,"abstract":"<div><div>Tower-based and other low-altitude hyperspectral remote sensing systems enable high-frequency water quality monitoring, complementing satellite observations. However, at extreme sensor zenith angles (&gt;80°), which are required for expanded spatial coverage, existing atmospheric correction (AC) methods lack validation and exhibit reduced accuracy, creating a critical gap in near-surface aquatic remote sensing. To address this challenge, we propose a physics-guided transfer learning atmospheric correction (PGTL-AC) framework that integrates 3.11 million coupled 6S–HydroLight simulations with field measurements to bridge the simulation-to-reality gap. Validated across heterogeneous environments, i.e., the turbid Bahe River in Xi’an, China, and the high-aerosol coastal waters of the Dongtou Archipelago in Wenzhou, China, PGTL-AC achieves a mean absolute percentage difference (MAPD) &lt; 8.5 % for visible-band (400–700 nm) remote sensing reflectance and maintains physically consistent performance (MAPD &lt; 17.5 %) across the full spectral range (400–865 nm). For operational water quality monitoring, Secchi disk depth retrieval achieves an MAPD &lt; 18 % in cross-regional validation. In summary, PGTL-AC addresses the AC bottleneck for tower-based aquatic remote sensing at extreme zenith angles, enabling real-time water-quality early warning and establishing high-quality validation sites for next-generation ocean color missions (e.g., NASA PACE and ESA Sentinel-3). The demonstrated accuracy is presently bounded by the validated conditions, namely two optically contrasting Case II water sites and aerosol loadings of AOT550 ≤ 0.8; performance outside this envelope requires further validation.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105589"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced retrieval of aerosol optical/microphysical parameters for Himawari-8 geostationary satellite measurements with data-driven deep learning method","authors":"Lina Xu,&nbsp;Siyu Liu,&nbsp;Minghui Tao,&nbsp;Jun Wang,&nbsp;Xincai Chang,&nbsp;Wenjing Man,&nbsp;Jie Ma,&nbsp;Huang Zhang,&nbsp;Jhoon Kim","doi":"10.1016/j.jag.2026.105530","DOIUrl":"10.1016/j.jag.2026.105530","url":null,"abstract":"<div><div>The high temporal resolution of Himawari-8 (H8) geostationary satellite has distinctive advantages in capturing diurnal variation of aerosol properties. However, the operational H8 aerosol products including Aerosol Optical Depth (AOD) are subject to considerable uncertainties due to assumptions and simplified surface reflectance. To fully exploit the H8 multi-spectral measurements, a data-driven retrieval framework based on a Deep Belief Network (DBN) was developed to simultaneously retrieve aerosol optical and microphysical parameters. By directly modeling H8 spectral reflectance at the top of the atmosphere (TOA) with matched AERONET products in 82 sites across diverse surface types and emission sources, four aerosol parameters including AOD, fine and coarse AOD (FAOD and CAOD), and single scattering albedo (SSA) are retrieved. Ground-based validation shows that the H8 DBN retrievals achieve high accuracies for AOD and FAOD, with correlation coefficients of 0.935 and 0.930, respectively, substantially outperforming the operational JAXA product (R = 0.703 for AOD). The retrieved CAOD and SSA also show good agreement with AERONET observations. Comparisons with MODIS and JAXA H8 aerosol products show very high consistency in both spatial and temporal variations. H8 DBN retrievals can clearly distinguish biomass burning smoke and dust plumes by their particle size and absorption. Additionally, hourly H8 DBN retrievals accurately reflect diurnal variations of aerosol properties. Consequently, the developed DBN-based approach provides a flexible and robust retrieval method for dynamic aerosol properties over East Asia.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105530"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148855156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based burned area mapping of California wildfires using Sentinel-2 and Landsat-8 imagery enhanced with super-resolution techniques","authors":"Youngmin Seo,&nbsp;Seung Hee Kim,&nbsp;Menas Kafatos,&nbsp;Jinsoo Kim,&nbsp;Yangwon Lee","doi":"10.1016/j.jag.2026.105526","DOIUrl":"10.1016/j.jag.2026.105526","url":null,"abstract":"<div><div>The increasing frequency of wildfires under a changing climate has led to extensive ecosystem destruction, highlighting the need for reliable burned area assessment using satellite imagery. Single-satellite data are constrained by observation gaps and interference from smoke and clouds, whereas multi-satellite data fusion can mitigate these limitations. Nonetheless, the fusion techniques still encounter challenges such as spatial information loss from resolution differences and cross-satellite domain mismatch. This study presents a burned area mapping framework that integrates super-resolution (SR) with transfer learning to address spatial and domain gaps in multi-satellite data. Specifically, Landsat-8 imagery is super-resolved to 7.5 m resolution, and the pretrained model is subsequently fine-tuned using Sentinel-2 imagery. Additionally, we investigated spectral index expansion to improve detection performance. Applied to California wildfires from 2013 to 2023, the input band expansion achieved a Burned Intersection over Union (IoU) of 0.821 when multiple spectral indices were incorporated. The SR-based transfer learning approach further improved performance with a Burned IoU of 0.850, representing a 4.55 % enhancement over the original Landsat-8 pre-trained model (0.813). Validation on 2025 wildfires demonstrated consistent performance and model robustness. These results highlight that the proposed SR-based approach offers benefits beyond simple resolution enhancement in multi-satellite data fusion. Furthermore, they confirm that spectral index-based input expansion contributes to accuracy improvements. The proposed framework is expected to support disaster response and environmental monitoring applications.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105526"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148855157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From 2D surface extent to 3D water levels: A SWOT-calibrated area–height framework for multidecadal reconstruction in hydraulically complex floodplain lakes","authors":"Xin Jiang,&nbsp;Lin Yan,&nbsp;Shunqiang Hu,&nbsp;Yuanyan Zhang,&nbsp;Zhenzhong Zeng","doi":"10.1016/j.jag.2026.105580","DOIUrl":"10.1016/j.jag.2026.105580","url":null,"abstract":"<div><div>Monitoring water levels in hydraulically complex floodplain lakes has long been constrained by a fundamental mismatch in satellite observations: optical sensors provide multidecadal records of surface water extent but lack vertical information, whereas conventional nadir altimetry retrieves precise elevations along sparse tracks that may not represent basin-wide water levels under strong surface gradients. This has limited the development of robust area–height (A–H) relationships and the reconstruction of historical water levels from archival imagery. The Surface Water and Ocean Topography (SWOT) mission helps address this limitation by providing spatially continuous two-dimensional water surface elevation fields from Ka-band radar interferometry. Here, we show how SWOT can serve as a geodetic anchor to calibrate a monotonicity-constrained cubic A–H model that converts long-term optical and synthetic aperture radar (SAR) water extent records into quasi-daily water level series. The framework is evaluated in Poyang Lake, China, a large river-connected floodplain lake with clear spatial gradients in water level. By fusing SWOT with Landsat, Sentinel, and MODIS archives, we reconstruct a quasi-daily water level time series for 2003–2025 that agrees closely with in situ gauges (RMSE = 0.44 m, MAE = 0.33 m, R² = 0.97). The reconstruction reveals a recent shift in hydrological regime, including an approximately 60-day advance in dry-season onset and an increase in extreme low-water events. This study presents a scalable virtual-station framework showing how short-term SWOT observations can extend long-term horizontal satellite archives into water level records for monitoring ungauged floodplain lakes worldwide.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105580"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving hourly PM2.5 estimation from geostationary satellite TOA reflectance through physically constrained multi-task learning","authors":"Ruijie Li,&nbsp;Zhe Zheng,&nbsp;Chenru Zou,&nbsp;Ke Li,&nbsp;Xidong Sun,&nbsp;Chaoshun Liu,&nbsp;Guangze Zhang,&nbsp;Kaixu Bai","doi":"10.1016/j.jag.2026.105546","DOIUrl":"10.1016/j.jag.2026.105546","url":null,"abstract":"<div><div>Geostationary satellite-based PM_2.5 mapping increasingly relies on data-driven models using top-of-atmosphere (TOA) reflectance. However, most existing approaches train a single estimator on temporally aggregated samples, implicitly assuming a stationary relationship between TOA radiative signals and near-surface PM_2.5. This assumption overlooks the pronounced diurnal variability captured by geostationary satellites and limits the representation of hour-dependent aerosol–radiation interactions. To address this limitation, we developed a physically constrained multi-task learning framework, termed the Multi-Temporal Decoupled Attention Network (MTDAN), for hourly PM_2.5 estimation from geostationary satellite TOA reflectance. The framework integrates three components: (1) a physically guided surface–atmosphere decoupling module that suppresses surface-background interference and enhances aerosol-sensitive information; (2) a multi-task attention architecture that jointly learns shared and hour-specific relationships between PM_2.5 and explanatory variables; and (3) a temporal-constrained loss function that exploits diurnal continuity to improve the consistency of sequential predictions. Experiments over eastern China using year-long Himawari-8 observations and ground-based PM_2.5 measurements demonstrated the effectiveness of the proposed framework. MTDAN achieved an overall site-based validation R² of 0.89 with an RMSE of 9.27 μg m⁻³, outperforming representative machine-learning and deep-learning models by reducing estimation errors by 21.7 %–27.8 %. The model successfully reproduced hourly pollution evolution, captured localized hotspots, and reconstructed season-dependent diurnal PM_2.5 dynamics. Ablation analyses further confirmed the complementary contributions of physical decoupling, adaptive feature weighting, and temporal-consistency constraints. These results demonstrate that explicitly modeling hour-dependent aerosol–radiation relationships can substantially improve geostationary satellite-based PM_2.5 estimation. The proposed framework provides an interpretable and robust solution for high-frequency air-quality monitoring and offers a general strategy for integrating physical constraints and temporal context into Earth observation applications.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105546"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic associations between meteorological drought and compound moisture sources-transport anomalies in the middle and lower Yangtze River","authors":"Hanlin Zheng,&nbsp;Tianliang Jiang,&nbsp;Yanping Qu,&nbsp;Qianfeng Wang,&nbsp;Yachao Zhang","doi":"10.1016/j.jag.2026.105551","DOIUrl":"10.1016/j.jag.2026.105551","url":null,"abstract":"<div><div>Moisture-transport anomalies are closely associated with meteorological drought, but the distinct and compound roles of source-region supply and pathway variability remain insufficiently quantified. Using CMFD meteorological data and FNL-driven FLEXPART simulations for 2005–2024, this study developed a multi-index conditional-probability framework for the middle and lower reaches of the Yangtze River (MLYR). Three standardized 10-day indices represented source-region precipitation contribution (SPC), moisture pathway intensity (MPI), and moisture pathway frequency (MPF). Gaussian copulas were used to estimate conditional drought probabilities under individual and paired moisture anomalies. Climatologically, Pathways 2 and 3 accounted for 45.4% of total pathway transport, while the Northwest Pacific and southern China–Indochina Peninsula together contributed 35.9% of source moisture. However, negative anomalies in the South China Sea and Pathway 2 were associated with higher individual drought probabilities than those in the Northwest Pacific and Pathway 3, despite their smaller climatological shares. Across 210 pair-matched configurations, compound conditional drought probability exceeded the stronger constituent single-anomaly probability in 181 cases (86.2%), with a median increase of 7.55 percentage points. After calendar-year-block permutation and BH-FDR correction, ΔP was significantly greater than zero in 164 pairs, and all nine top-ranked combinations exceeded 50% conditional probability. These results show that mean contribution magnitude was not a reliable indicator of anomaly-conditioned drought association and that elevated drought probability is frequently, but not universally, associated with concurrent weakening across moisture-transport components. The framework provides a process-oriented basis for diagnosing drought-relevant transport configurations and can be adapted to other basins after region-specific recalibration and independent validation.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105551"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying lake expansion probability under compound glacier–climate forcing using a GCL-CPF framework integrating multi-source Earth observation and vine copula theory","authors":"Xiaoyang Zhao,&nbsp;Nai’ang Wang,&nbsp;Liqiang Zhao,&nbsp;Dezhong Zhang,&nbsp;Na’na Zhai,&nbsp;Hang Zhao,&nbsp;Liqiong Bai","doi":"10.1016/j.jag.2026.105559","DOIUrl":"10.1016/j.jag.2026.105559","url":null,"abstract":"<div><div>Lakes across the Tibetan Plateau exhibit pronounced responses to climatic variability and glacier fluctuations. However, the compound effects of glacier and climate forcing on lake dynamics remain insufficiently quantified, owing to limited observational continuity and reliance on single-factor qualitative or regression-based methods that are not well suited to representing multivariate nonlinear relationships. In this study, a Glacier–Climate–Lake Coupled Probabilistic Framework (GCL-CPF) was developed by integrating remote sensing–based reconstruction of glacier–lake dynamics with glacier process modeling and Vine Copula theory to characterize multivariate dependence. The framework quantifies lake expansion probability under bivariate coupling among glacier melt runoff (GMR), temperature (Temp), and precipitation (Prec). Results show lake expansion is not a monotonic response to coupled forcing but exhibits probability maxima under specific meltwater conditions, indicating a regime-dependent rather than continuously increasing response. Meltwater–precipitation combinations are associated with higher probabilities of lake expansion when favorable joint conditions occur. Temperature conditions correspond to either lower or higher lake expansion probabilities depending on meltwater–precipitation conditions, indicating a context-dependent relationship. In June, lakes generally exhibit higher probabilities of contraction characterized by limited runoff and precipitation, while occasional expansion corresponds to conditions with higher temperature and precipitation. In July and August, increased precipitation and rising temperature-induced glaciers melt correspond to higher probabilities of rapid lake expansion, while extreme heat conditions in some years are associated with pronounced lake shrinkage. This study captures compound relationships and dependence structures among multiple drivers. The GCL-CPF framework provides a transferable basis for quantifying lake dynamics in glacierized regions and predicting hydrological responses under climate change.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105559"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}