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

{"title":"Synergistic use of multi-sensor satellite data for mapping crop types and land cover dynamics from 2021 to 2023 in Northeast Thailand","authors":"Savittri Ratanopad Suwanlee ,&nbsp;Surasak Keawsomsee ,&nbsp;Emma Izquierdo-Verdiguier ,&nbsp;Álvaro Moreno-Martínez ,&nbsp;Sarawut Ninsawat ,&nbsp;Jaturong Som-ard","doi":"10.1016/j.jag.2025.104673","DOIUrl":"10.1016/j.jag.2025.104673","url":null,"abstract":"<div><div>Accurate and timely information on the spatiotemporal distribution of crops is essential for sustainable agricultural practices and ensuring food security. The significant challenges persist in accurately classifying crop types in highly fragmented cropland regions characterized by small field sizes, complex landscapes, and highly frequent cloud cover. This study presents a novel classification workflow designed to generate archaic/historic and reliable land cover (LC) maps from integrating time series data from multiple EO sources—Sentinel-1, Sentinel-2, and the Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM)—with the Random Forest (RF) classifier and cloud computing technology. To the evaluate the effectiveness of this approach, Northeast (NE) Thailand was selected as a case study region, focusing on the classification of 14 crop types between 2021 and 2023. Different combinations of EO datasets and a RF classifier were evaluated using a substantial dataset of 13,453 reference points. The crop type/LC transitions from 2021 to 2023 were then analysed and a temporal transfer model was employed to map historical crop fields. The combined all EO datasets in this work achieved high overall accuracy and F1 scores (&gt;85 %) with the high spatial consistency of crop fields when compared to the use of combined both datasets. Results demonstrated the high potential and excellent efficiency of the RF, utilising an extensive reference dataset and the continuous temporal monthly information of gap-filled data. The most dominant crops were rice, followed by cassava, sugarcane and rubber trees throughout the three study years. The transfer learning RF model proved effective in mapping historical crop types and LC even when ground data was limited. Transitions of 7,287 km² (∼5%) appeared from 2021 to 2022, with major crop decreases in rice and sugarcane. From 2022 to 2023, cropland changes totaled 8,466 km² (∼6%), primarily as reductions in sugarcane and rubber trees. Our findings highlight the effectiveness of integrating multiple EO datasets in this study for mapping crop types across large areas and confirm the benefit of using monthly temporal data to obtain historic LC maps, providing valuable insights for a large range of stakeholders.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104673"},"PeriodicalIF":7.6,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279229","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":"Estimating building height using scene classification and spatial geometry","authors":"Yonghua Jiang ,&nbsp;Jingxin Chang ,&nbsp;Yunming Wang ,&nbsp;Shaodong Wei ,&nbsp;Deren Li","doi":"10.1016/j.jag.2025.104675","DOIUrl":"10.1016/j.jag.2025.104675","url":null,"abstract":"<div><div>Building height significantly influences urban development and evolution. Previous studies on building height estimation using digital surface models (DSMs) have predominantly addressed simple, single-environmental scenarios, often yielding unsatisfactory results across diverse environments. This study introduces a novel method for estimating building height by integrating scene classification with spatial geometric relationships. Initially, raw data are processed to derive the various data types required for this approach. Environmental scene classification, based on vegetation and shadows analysis, is then performed. Subsequently, the building height is estimated either directly from the DSM or through road height prediction. The proposed method is validated using a scene image from Wuhan, Hubei Province, China. The results demonstrate that the estimated building height maintains high accuracy in complex environments with significant vegetation and shadow coverage, achieving a mean absolute error of 1.84 m. Furthermore, the proposed method outperforms existing DSM-based techniques. This approach is adaptable for high-precision building height estimation across various environments and holds substantial application potential, facilitating further research in urban-related scenarios.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104675"},"PeriodicalIF":7.6,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288789","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":"Enhancing water scarcity resilience in Egypt through machine learning-driven phenological crop mapping and water use efficiency analysis","authors":"Surendra Maharjan ,&nbsp;Wenzhao Li ,&nbsp;Shahryar Fazli ,&nbsp;Aqil Tariq ,&nbsp;Rejoice Thomas ,&nbsp;Cyril Rakovski ,&nbsp;Hesham El-Askary","doi":"10.1016/j.jag.2025.104668","DOIUrl":"10.1016/j.jag.2025.104668","url":null,"abstract":"<div><div>Agriculture forms the backbone of Egypt’s economy, with the Nile Valley and Delta serving as key production zones for crops like wheat, rice, and clover. However, the sector faces mounting pressure from water scarcity, as it depends almost entirely on the Nile for irrigation, making it necessary to map major crops for assessing Water Use Efficiency (WUE) and informing agricultural planning. In this study, we used machine learning (ML) techniques—specifically Support Vector Machine (SVM) to time-series phenological data and optical indices (Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Plant Senescence Reflectance Index (PSRI)) to map major crop types—specifically rice (a summer crop),wheat and clover (winter crops) —across entire Nile Basin in Egypt. Training and testing showed satisfactory performance, with testing accuracy ranging from 0.73 to 0.82 and training accuracy from 0.70 to 0.90. In addition, this study evaluates responsiveness of crop WUE to Vapor Pressure Deficit (VPD) and other meteorological and biophysical factors—including solar radiation, precipitation, maximum temperature, gross primary productivity, and evapotranspiration. Our findings confirm VPD as dominant factor affecting WUE, with a 3.5 kPa threshold beyond which WUE no longer responds, signaling a physiological limit for water management. The projected VPD trend, based on ensemble analysis of Coupled Model Intercomparison Project Phase 6 models under SSP245 and SSP585 scenarios, indicates an increase in number of months with high VPD in future, reinforcing the need for adaptive irrigation strategies in the region.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104668"},"PeriodicalIF":7.6,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288852","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 high-resolution remote sensing land use/land cover classification method based on multi-level features adaptation of segment anything model","authors":"Hui Yang ,&nbsp;Zhipeng Jiang ,&nbsp;Yaobo Zhang ,&nbsp;Yanlan Wu ,&nbsp;Heng Luo ,&nbsp;Peng Zhang ,&nbsp;Biao Wang","doi":"10.1016/j.jag.2025.104659","DOIUrl":"10.1016/j.jag.2025.104659","url":null,"abstract":"<div><div>Land use/land cover (LULC) classification based on deep learning techniques is a significant research area for analyzing high-resolution remote sensing(HRRS) images. However, due to the limitation of available samples and model feature extraction capability, the current deep learning methods suffer from weak generalization ability for widespread and effective application across diverse HRRS scenarios. To address this problem, we propose an innovative network model named multi-level feature adaptation-segment anything Model (MLFA-SAM). The model employs a three-level fine-tuning strategy to adapt the SAM foundation model for remote sensing LULC classification.<!--> <!-->The proposed MLFA-SAM significantly enhances high-precision classification performance across diverse HRRS scenarios. Specifically,<!--> <!-->the domain distribution shift adaptation (DDSA) level is designed to adjust the input image modality for SAM and initially extract features and overcome the domain distribution shift between remote sensing images and the natural images used by the SAM. Then, we designed depthwise low-rank adaptation (DLRA) strategy to optimally fine-tune the frozen SAM parameters. Finally, we improved SAM’s mask decoder to generate high-quality multi-class masks required for LULC classification. Experimental results demonstrate that the MLFA-SAM model surpasses several existing state-of-the-art(SOTA) methods on the HRLC dataset and the ISPRS Potsdam dataset. Quantitative evaluations demonstrate that MLFA-SAM, with its concise yet efficient architecture, achieves 66.77% mIoU and 86.02% OA on the HRLC dataset. Notably, the integration of near-infrared (Nir) bands further enhances its performance to 68.43% mIoU and 87.91% OA. The generalization test on the LoveDA dataset, along with four test HRRS images exhibiting spatiotemporal and semantic scene differences, further demonstrate that MLFA-SAM possesses a stronger generalization ability compared to existing methods and shows greater potential for practical applications.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104659"},"PeriodicalIF":7.6,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272142","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 cloud-based framework for the quantification of the spatially-explicit uncertainty of remotely sensed benthic habitats","authors":"Spyridon Christofilakos ,&nbsp;Alina Blume ,&nbsp;Avi Putri Pertiwi ,&nbsp;Chengfa Benjamin Lee ,&nbsp;Dimosthenis Traganos ,&nbsp;Peter Reinartz","doi":"10.1016/j.jag.2025.104670","DOIUrl":"10.1016/j.jag.2025.104670","url":null,"abstract":"<div><div>The significant advances of cloud-based remote sensing frameworks have allowed researchers to develop large-scale analytics for better understanding, monitoring of, and decision-making around sensitive and valuable coastal ecosystems like seagrass meadows. However, an information gap related with the spatially-explicit accuracy of Machine Learning (ML) products has been identified. The goal of this study is to estimate the per pixel uncertainty of a Random Forest classification of four benthic habitats and exploit it to retrain the model through training data selection by bootstrapping and producing an ensemble model. The calculation of the spatially-explicit uncertainty is based on the Shannon Entropy equation and the probability values of a successful prediction according to the ML model. The remote sensing data for this study are sourced from the European Union Copernicus Sentinel-2 twin satellite system and Planet’s cubesat satellite constellation respectively, and have been processed and analyzed through the Google Earth Engine cloud-based platform. The national extent of The Bahamas and the regional extent of the Wakatobi archipelago in Indonesia comprise our study sites. Our results indicate the potential of the presented uncertainty workflow for optimizing the classification and the usefulness of the produced uncertainty map to aid policy-makers through our provided spatially-explicit accuracy metrics. More precisely in the case of the Bahamas, the percentile differences for seagrass user and producer accuracies are improved in the ranges of 1.16–4.77 % and 4.36–8.54 %, respectively, in comparison with a standard supervised classification. In conclusion, spatially-explicit uncertainty information can and should be used as unique and vital geospatial information suitable for ML classification optimization and as a tool for better decision-making and field expedition planning, and understanding of benthic ecosystems.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104670"},"PeriodicalIF":7.6,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272143","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":"DMRS: Long-tailed remote sensing recognition via semantic-aware mixing and diversity experts","authors":"Yifan Wang ,&nbsp;Fan Zhang ,&nbsp;Qihao Zhao ,&nbsp;Wei Hu ,&nbsp;Fei Ma","doi":"10.1016/j.jag.2025.104623","DOIUrl":"10.1016/j.jag.2025.104623","url":null,"abstract":"<div><div>Long-tailed class distributions pose a significant challenge in remote sensing scene recognition, where certain scene categories appear far less frequently than others. However, existing long-tailed learning approaches often overlook the unique spatial hierarchies and contextual semantic relationships inherent in remote sensing imagery, limiting their effectiveness in this domain. To address this, we propose Diversity-Mix Remote Sensing (DMRS), a foundation model-based framework designed for long-tailed remote sensing scene recognition. DMRS introduces two key innovations: (1) multi-low-rank adaptation diversity experts, which achieves balanced classification by specializing different experts for different regions of the class distribution, and (2) a semantic-aware mixing strategy, which incorporates textual semantic information typically unused in traditional classification to enhance perception across diverse remote sensing scenes. Extensive experiments on NWPU-RESISC45 and RSD46-WHU datasets demonstrate the effectiveness of DMRS, achieving 6.7% and 2.0% improvements in overall accuracy, respectively, while significantly enhancing the recognition of tail classes. These results highlight the potential of DMRS in tackling long-tail challenges in remote sensing scene classification. The data and codes used in the study are detailed in: <span><span>https://github.com/wyfhbb/DMRS</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 104623"},"PeriodicalIF":7.6,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272144","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":"Detection and monitoring of Melampsora spp. Damage in multiclonal poplar plantations coupling biophysical models and Sentinel-2 time series","authors":"Carlos Camino ,&nbsp;Alexey Valero-Jorge ,&nbsp;Erika García Lima ,&nbsp;Ramón Álvarez ,&nbsp;Pieter S.A. Beck ,&nbsp;Flor Álvarez-Taboada","doi":"10.1016/j.jag.2025.104663","DOIUrl":"10.1016/j.jag.2025.104663","url":null,"abstract":"<div><div>Climate change is dramatically shifting the distribution and prevalence of pests and diseases, posing significant threats to global forest ecosystems. Poplar plantations, particularly multiclonal ones, are highly vulnerable to pathogen-driven diseases such as leaf rust caused by <em>Melampsora spp</em>. In this study, we developed three machine learning (ML) detection models (DMs) for identifying rust-affected poplar trees coupling Sentinel-2 time series and the PROSAIL radiative transfer model. For each DM, three ML algorithms (support vector machines, random forests, and neural networks) were trained using in situ leaf rust inspections as reference data, and the following inputs: (i) inverted plant traits retrieved from the PROSAIL model, (ii) key spectral indices derived from Sentinel-2 time series, and (iii) a combination of both plant traits and indices from Sentinel-2 images. The best-performing DM, which combined plant traits and spectral indices, achieved an overall accuracy of 89.5 % (Kappa = 0.78) across three tested ML algorithms. Relative importance analysis highlighted chlorophylls (21 %), carotenoids (16 %), and leaf water content (11 %) as the most critical variables for rust detection. This study shows the potential of combining biophysical models with Sentinel-2 imagery for precise and scalable rust detection in multiclonal poplar plantations. Our approach also highlights how key plant traits, such as chlorophyll, carotenoids, and leaf water content, vary across poplar clones, offering valuable insights for forest management and conservation strategies in the context of climate change. The framework we propose is adaptable and transferable to different regions and conditions, enhancing disease monitoring and forest health management. Its robustness is further supported by external validation using the ANGERS spectral database, confirming the physiological relevance of the retrieved traits.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104663"},"PeriodicalIF":7.6,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261676","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":"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}