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

{"title":"SUMMIT: A SAR foundation model with multiple auxiliary tasks enhanced intrinsic characteristics","authors":"Yuntao Du ,&nbsp;Yushi Chen ,&nbsp;Lingbo Huang ,&nbsp;Yahu Yang ,&nbsp;Pedram Ghamisi ,&nbsp;Qian Du","doi":"10.1016/j.jag.2025.104624","DOIUrl":"10.1016/j.jag.2025.104624","url":null,"abstract":"<div><div>Synthetic Aperture Radar (SAR) is a crucial tool in remote sensing, yet existing deep learning methods are primarily limited in visual representation, neglecting the intrinsic characteristics of SAR and the need for strong generalization across multiple tasks. To address this, we propose SUMMIT (SAR foUndational Model with Multiple auxiliary tasks enhanced Intrinsic characterisTics), a foundational model tailored for SAR image understanding. SUMMIT is pre-trained on the Multi-sensor SAR Image Dataset (MuSID), which contains over 560,000 SAR images. To enhance its feature extraction capability, we introduce a masked image modeling (MIM) framework with self-supervised auxiliary tasks (SSATs): (1) MIM for learning robust structural representations, (2) self-supervised denoising to improve the model’s noise resistance, and (3) space scattering feature enhancement to preserve geometric consistency. Furthermore, we design an auxiliary task coordination module (ATCM) to balance these tasks and ensure effective feature fusion. The resulting self-supervised framework enables SUMMIT to integrate deep learning with SAR’s physical attributes effectively. Extensive experiments across seven datasets and three downstream tasks demonstrate that SUMMIT achieves state-of-the-art performance, particularly in SAR classification, detection, and segmentation. Code and pre-trained model of the proposed SUMMIT will be available at <span><span>https://github.com/Yunsans/SUMMIT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104624"},"PeriodicalIF":7.6,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RoofMapNet: Utilizing geometric primitives for depicting planar building roof structure from high-resolution remote sensing imagery","authors":"Jiaqi Wang ,&nbsp;Guanzhou Chen ,&nbsp;Xiaodong Zhang ,&nbsp;Tong Wang ,&nbsp;Xiaoliang Tan ,&nbsp;Qingyuan Yang ,&nbsp;Wenlin Zhou ,&nbsp;Kun Zhu","doi":"10.1016/j.jag.2025.104630","DOIUrl":"10.1016/j.jag.2025.104630","url":null,"abstract":"<div><div>The accurate extraction of building roof structures from aerial imagery represents a fundamental task for urban digital twin systems, facilitating critical applications such as 3D city modeling and solar potential assessment. Despite recent advancements in geospatial artificial intelligence, existing methods frequently encounter challenges posed by real-world complexities. These include structural heterogeneity caused by diverse architectural styles, discontinuities in roof structures due to occlusions from vegetation and other obstacles, and the limited generalization ability of models stemming from the scarcity of specialized annotated datasets. In this paper, we introduce an end-to-end network called RoofMapNet, specifically designed for extracting roof structures. First, we propose a strategy for roof junction extraction that integrates dynamic Gaussian heatmaps with quadratic coordinate calibration. This strategy enhances the model’s robustness in junction prediction under heterogeneous sample distribution scenarios. To address the loss or blurring of roof lines caused by occlusion and shadow, we propose an adaptive occlusion-aware module. This module employs a bidirectional mapping between geometric and feature spaces to refine candidate lines accurately, thus improving the model’s generalization ability and robustness in roof line detection. Additionally, to comprehensively evaluate the performance of roof structure detection models, we meticulously annotated a diverse, large-scale remote sensing imagery dataset for roof structure extraction, named RoofMapSet. Comprehensive evaluations on the VWB and RoofMapSet datasets demonstrate state-of-the-art performance, with mean <span><math><mrow><mi>s</mi><mi>A</mi><mi>P</mi></mrow></math></span> improvements of 4.13% and 2.85% over competitors, respectively. Further analyses confirm the resilience to varying spatial resolutions and complex occlusion patterns. Our code and data are available at: <span><span>https://github.com/CVEO/RoofMapNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104630"},"PeriodicalIF":7.6,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A critical review on the applications of Sentinel satellite datasets for soil moisture assessment in crop production","authors":"Anela Mkhwenkwana ,&nbsp;Trylee Nyasha Matongera ,&nbsp;Ciara Blaauw ,&nbsp;Onisimo Mutanga","doi":"10.1016/j.jag.2025.104647","DOIUrl":"10.1016/j.jag.2025.104647","url":null,"abstract":"<div><div>Understanding soil moisture dynamics in crop production is critical for optimising water resource management. The Sentinel satellite missions have significantly contributed to soil moisture monitoring by providing high-resolution, multi-sensor data. This review examines advancements in soil moisture assessment using Sentinel datasets, particularly in crop production. It highlights key challenges, evaluates their impact on monitoring accuracy, and explores potential methodological improvements. Findings indicate that Sentinel-1′s synthetic aperture radar (SAR) data, particularly VV and VH polarizations, and Sentinel-2′s multispectral indices, such as NDVI and NDMI, are widely integrated with machine learning algorithms to enhance soil moisture estimation. However, dense vegetation and complex topography reduce retrieval accuracy, necessitating sensor fusion and calibration for improved reliability. Sentinel-3 provides valuable surface temperature and land condition data for indirect soil moisture estimation, but its application remains limited due to higher uncertainty compared to SAR and multispectral approaches. Emerging trends suggest that machine and deep learning techniques, such as RF, SVR, and CNN, can enhance data fusion across Sentinel missions. Additionally, preprocessing steps such as RTC, speckle filtering, and the integration of multipolar and polarimetric data with physical backscattering models show promise in mitigating radar backscatter interference. Further development of robust retrieval models that incorporate topography, soil roughness, and texture are essential for improving soil moisture accuracy in diverse agricultural landscapes. This review underscores the need for continued methodological advancements to maximise the potential of Sentinel datasets for soil moisture monitoring in precision agriculture and water resource management.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104647"},"PeriodicalIF":7.6,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial impact evaluation of a low-cost agricultural intervention for enhancing environmental resilience","authors":"Pratap Khattri ,&nbsp;Rachel Sayers ,&nbsp;Kunwar K. Singh ,&nbsp;Ryan Slapikas ,&nbsp;Chet Bahadur Tamang ,&nbsp;Dinee Tamang ,&nbsp;Brad Sagara ,&nbsp;Ariel BenYishay","doi":"10.1016/j.jag.2025.104657","DOIUrl":"10.1016/j.jag.2025.104657","url":null,"abstract":"<div><div>Land degradation poses a significant threat to ecosystems and livelihoods, particularly in disaster-prone regions. In these settings, the promotion of certain agricultural practices with economic incentives, such as sugarcane (<em>Saccharum officinarum</em>) farming, offers a potential solution for enhancing economic stability and mitigating environmental degradation. Despite its promise, the effectiveness of sugarcane as an agricultural intervention remains understudied, especially regarding its environmental benefits. Our study evaluates the impact of sugarcane cultivation in western Nepal, a region highly vulnerable to soil erosion and riverbank degradation due to the presence of flood-prone landscapes. We conducted a geospatial impact evaluation (GIE), which integrated remote sensing data and econometric techniques, including optimal full matching (OFM) and difference-in-differences (DID). We assessed the causal impact of a program promoting sugarcane farming on its adoption and environmental outcomes, measured using multi-temporal satellite imagery, crop phenology, and clustering algorithms, along with machine learning and visual interpretation methods. Our results show that sugarcane adoption increased significantly in both treated and spillover communities, highlighting its potential as a sustainable agricultural practice. However, while uptake was evident, the expected environmental outcomes, such as soil erosion control and riverbank stabilization, did not materialize. This study demonstrates the potential of GIE in evaluating low-cost interventions for sustainable development and provides insights into the role of sugarcane cultivation in promoting climate resilience. The findings underscore the need for complementary interventions and extended timeframes to realize long-term environmental benefits, contributing valuable evidence for policymakers and development practitioners.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104657"},"PeriodicalIF":7.6,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inversion of snow geophysical parameters using the VHR PAZ X-band dual polarimetric SAR data: first known experiments in the Himalayan region","authors":"Hemant Singh,&nbsp;Divyesh Varade","doi":"10.1016/j.jag.2025.104653","DOIUrl":"10.1016/j.jag.2025.104653","url":null,"abstract":"<div><div>Snow plays a vital role in mountain hydrology, water resources, and Earth’s planetary budgets. Therefore, monitoring snow geophysical parameters (SGPs), such as snow density, Snow depth (SD), and snow water equivalent (SWE), is imperative hydrological dynamics and forecasting water availability. Moreover, radar remote sensing offers significant capabilities for estimating these parameters. In this study, we utilized PAZ X-band dual-polarimetric data to estimate SGPs. To the best of our knowledge, this is the first known experiment using PAZ data for SGP estimation. In the present work, we utilized the copolar phase difference (CPD) for SD and Integral Equation model (IEM) for snow density. In this study, we proposed an improved algorithm for SD inversion, instead of relying on a single in-situ snow density value, we incorporated a range of snow densities (0.15 to 0.27 g/cm3), optimizing the axial ratio between 1.13 and 1.17. This density range and optimized axial ratio were used to minimise the error between <span><math><msub><mrow><mi>C</mi><mi>P</mi><mi>D</mi></mrow><mrow><mi>O</mi><mi>b</mi><mi>s</mi></mrow></msub></math></span> and the average <span><math><msub><mrow><mi>C</mi><mi>P</mi><mi>D</mi></mrow><mrow><mi>M</mi><mi>o</mi><mi>d</mi></mrow></msub></math></span>. The algorithm yielded high-resolution modelled SD and density at a 2.5 m spatial resolution, which were later used to estimate SWE. Algorithm validation was performed using in-situ data of Gulmarg region of Kashmir, India, with statistical metrics such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and percentage error (PE). SD estimates showed high correlation, with R2 = 0.85, RMSE = 3.18 cm, PE = 1 %, and MAE = 2.85 cm. Similarly, SWE estimates had an R2 of 0.77, RMSE = 1.032 cm, PE = 5 %, and MAE = 0.814 cm, demonstrating the model’s accuracy and reliability.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104653"},"PeriodicalIF":7.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extracting individual trajectories from text by fusing large language models with diverse knowledge","authors":"Le Liu ,&nbsp;Tao Pei ,&nbsp;Zidong Fang ,&nbsp;Xiaorui Yan ,&nbsp;Chenglong Zheng ,&nbsp;Xi Wang ,&nbsp;Ci Song ,&nbsp;Wenfei Luan ,&nbsp;Jie Chen","doi":"10.1016/j.jag.2025.104654","DOIUrl":"10.1016/j.jag.2025.104654","url":null,"abstract":"<div><div>Individual trajectories offer insights into human mobility, with data either passively recorded, such as GPS, or actively recorded, such as natural language text. While the former provides detailed movement data, it lacks important context such as personal experiences, which can be obtained from the latter. Extracting trajectories from text can enhance travel experience optimization, historical analysis, and pandemic management. However, existing trajectory extraction methods rely on rule-based frameworks that fail to capture contextual semantics, resulting in limited generalizability and loss of trajectory semantics. While general-purpose large language models (LLMs) demonstrate potential for contextual reasoning capabilities, their deficient domain-specific knowledge pertinent to trajectory patterns hinders efficient and precise trajectory extraction. To address these limitations, we propose T2TrajLLM, a novel framework that fuses LLMs with domain knowledge through three components: (1) a lightweight trajectory model for structured guidance, (2) a text-to-trajectory transformation model enabling multi-step reasoning, and (3) labelled text-trajectory samples for learning domain-adaptive constraint rules. Central to T2TrajLLM is a prompt method that dynamically fuses these components with LLMs while avoids rigid rule dependency. Evaluated across three heterogeneous datasets, T2TrajLLM achieves ∼8 % higher accuracy than existing methods, demonstrating strong transferability across datasets and extensibility to diverse application requirements. Overall, T2TrajLLM effectively extracts trajectories from diverse textual sources, providing robust support for the analysis and understanding of individual mobility.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104654"},"PeriodicalIF":7.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of different downscaling schemes for obtaining regional high-resolution soil moisture data","authors":"Yulin Shangguan ,&nbsp;Cheng Tong ,&nbsp;Zhou Shi ,&nbsp;Hongquan Wang ,&nbsp;Xiaodong Deng","doi":"10.1016/j.jag.2025.104652","DOIUrl":"10.1016/j.jag.2025.104652","url":null,"abstract":"<div><div>Obtaining regional fine-scale daily Soil Moisture (SM) data is crucial for better understanding carbon and water cycles. Currently, downscaling from passive microwave SM products has become the most commonly utilized approach for generating regional high-resolution SM data, while retrieving SM based on disaggregated brightness Temperature (TB) data gains litter attention. Besides, the relative potentials of these two downscaling approaches remains largely unknown. Therefore, this study comprehensively compared the relatively performances of the two downscaling schemes namely the “retrieving-then-downscaling” and “downscaling-then-retrieving” over the Qinghai-Tibet Plateau (QTP). Evaluation results showed that retrieving SM using disaggregated TB significantly outperformed the popular passive microwave SM downscaling method. The averaged R and ubRMSE metrics for downscale-first based SM were 0.62/0.74 and 0.051/0.038 m³/m³ at station/network scales, and were 0.58/0.70 and 0.056/0.041 m³/m³ for the retrieval-first based SM, respectively. Spatially, the downscale-first based SM had lower uncertainty than the retrieval-first based SM over nearly 70 % areas of the QTP. However, due to the strong vegetation scattering effect, it showed two times larger uncertainty than the retrieval-first based SM over densely vegetated regions in the east and southeast. In addition, satisfactory TB downscaling performance could be achieved by leveraging machine learning algorithms and multiple covariables, but need to further reduce additional errors. The superiority of “downscaling-then-retrieving” scheme was independent of models selected for downscaling. In conclusion, this study demonstrates the great potential of “downscaling-then-retrieving” method and provides a new insight for generating regional SM data at fine scale.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104652"},"PeriodicalIF":7.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RADARSAT constellation mission compact polarisation SAR data for burned area mapping with deep learning","authors":"Yu Zhao,&nbsp;Yifang Ban","doi":"10.1016/j.jag.2025.104615","DOIUrl":"10.1016/j.jag.2025.104615","url":null,"abstract":"<div><div>Monitoring wildfires has become increasingly critical due to the sharp rise in wildfire incidents in recent years. Optical satellites like Sentinel-2 and Landsat are extensively utilised for mapping burned areas. However, the effectiveness of optical sensors is compromised by clouds and smoke, which obstruct the detection of burned areas. Thus, satellites equipped with Synthetic Aperture Radar (SAR), such as dual-polarisation Sentinel-1 and quad-polarisation RADARSAT-1/-2 C-band SAR, which can penetrate clouds and smoke, are investigated for mapping burned areas. However, there is limited research on using compact polarisation (compact-pol) C-band RADARSAT Constellation Mission (RCM) SAR data for this purpose. This study aims to investigate the capacity of compact polarisation RCM data for burned area mapping through deep learning. Compact-pol m-<span><math><mi>χ</mi></math></span> decomposition and Compact-pol Radar Vegetation Index (CpRVI) are derived from the RCM Multi-Look Complex product. A deep-learning-based processing pipeline incorporating ConvNet-based and Transformer-based models is applied for burned area mapping, with three different input settings: using only log-ratio dual-polarisation intensity images, using only compact-pol decomposition plus CpRVI, and using all three data sources. The training dataset comprises 46,295 patches, generated from 12 major wildfire events in Canada. The test dataset includes seven wildfire events from the 2023 and 2024 Canadian wildfire seasons in Alberta, British Columbia, Quebec and the Northwest Territories. The results demonstrate that compact-pol m-<span><math><mi>χ</mi></math></span> decomposition and CpRVI images significantly complement log-ratio images for burned area mapping. The best-performing Transformer-based model, UNETR, trained with log-ratio, m-<span><math><mi>χ</mi></math></span> m-decomposition, and CpRVI data, achieved an F1 Score of 0.718 and an IoU Score of 0.565, showing a notable improvement compared to the same model trained using only log-ratio images (F1 Score: 0.684, IoU Score: 0.557). This is the first study to demonstrate that RCM C-band SAR data and its derived features are effective for burned area mapping.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104615"},"PeriodicalIF":7.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PROSPECT-DP: A coupled leaf optical properties model for improving leaf spectra simulation in the red-edge domain by excluding the ChlF effect","authors":"Shanshan Du ,&nbsp;Dianrun Zhao ,&nbsp;LinLin Guan ,&nbsp;Mengjia Qi ,&nbsp;Xinjie Liu ,&nbsp;Liangyun Liu","doi":"10.1016/j.jag.2025.104651","DOIUrl":"10.1016/j.jag.2025.104651","url":null,"abstract":"<div><div>Plenty of leaf optical spectra datasets have been employed in the calibration and validation of leaf optical or empirical models. However, no experiment has been conducted that measures the true leaf reflectance and transmittance spectra while isolating chlorophyll fluorescence (ChlF) contributions, which results in a superimposed effect on measured reflectance or transmittance. As a result, to date, the simulated accuracy of leaf spectra in the red-edge domain remains limited due to the absence of true leaf spectra used in calibration of leaf models. This study aims to enhance the accuracy of simulating leaf spectral characteristics in the red-edge domain by combining an existing leaf physical model (PROSPECT-D) with the data-driven method that is typically used to establish leaf empirical models. A new leaf spectra dataset without ChlF contributions, including reflectance and transmittance data for 849 leaves, was first measured and replace the existing leaf spectra datasets in the modelling process. A coupled leaf optical properties model (PROSPECT-DP) was then established, in which a principal component analysis (PCA) approach was employed to model leaf spectra in red-edge domain using the spectral vectors derived from the training dataset, while the coefficients of spectral vectors were determined by leveraging the PROSPECT-D simulated leaf spectra except for the red-edge domain. Finally, validation of the PROSPECT-DP model with an independent validation dataset of 203 leaf samples showed that it performed much better than the PROSPECT-D model for spectral simulation in the red-edge domain. Furthermore, the PROSPECT-DP model exhibited better performance in leaf trait inversions with a closer relationship between measured and PROSPECT-DP-derived leaf chlorophyll <em>a</em> + b and carotenoid pigments compared to the PROSPECT-D model. Therefore, the novel coupled leaf model presented in this study will be highly beneficial for the integration of leaf models into canopy models and for applications in remote sensing to assess plant traits.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104651"},"PeriodicalIF":7.6,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking the InSAR potential for managing underground gas storage in salt caverns","authors":"Gabriele Fibbi ,&nbsp;Roberto Montalti ,&nbsp;Matteo Del Soldato ,&nbsp;Stefano Cespa ,&nbsp;Alessandro Ferretti ,&nbsp;Riccardo Fanti","doi":"10.1016/j.jag.2025.104656","DOIUrl":"10.1016/j.jag.2025.104656","url":null,"abstract":"<div><div>Natural gas plays a key role in the ongoing transition to renewable energy, bridging the gap between conventional fossil fuels and cleaner alternatives. Despite the increasing emphasis on renewables, total reliance on electricity is still out of reach today, necessitating a transition period. The role of natural gas is particularly evident in addressing the challenges posed by seasonal fluctuations in consumption, a persistent issue for the global energy industry. In this context, Underground Gas Storage (UGS) facilities offer a flexible strategy to create global reserves and stabilise supply against demand fluctuations. Among the exploitable geological structures that are available for UGS, underground salt caverns allow high injection and withdrawal rates. However, these operations can induce subsidence due to cavern convergence and seasonal ground displacement resulting from operational cycles. This study employs multi-temporal Interferometric Synthetic Aperture Radar (InSAR) analysis, using Sentinel-1 data and the SqueeSAR algorithm, to monitor surface displacement at two UGS sites in Lower Saxony, Germany. The analysis revealed a vertical displacement velocity of up to 28 mm/year, forming a distinct cone-shaped deformation. Time series analysis showed a strong correlation between the injection/withdrawal cycles and the InSAR data. The threshold method identified UGS affected areas, offering a standardised framework for UGS monitoring. The study also introduced the RTK cross-correlation analysis to refine displacement interpretations, enhancing the ability to isolate gas storage induced deformation from unrelated surface processes. These results contribute for optimising injection and withdrawal strategies, mitigating subsidence risks and allowing the long-term sustainability of UGS operations. This approach can support robust management practices that prioritise safety and operational efficiency.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104656"},"PeriodicalIF":7.6,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}