Remote Sensing of Environment最新文献

{"title":"A method to model the directional reflectance for desert shrub scenes in a general framework of kernel-driven BRDF model","authors":"Henggang Zhang ,&nbsp;Xu Ma ,&nbsp;Huaguo Huang ,&nbsp;Ziti Jiao ,&nbsp;Fei Zhang","doi":"10.1016/j.rse.2025.115152","DOIUrl":"10.1016/j.rse.2025.115152","url":null,"abstract":"<div><div>In remote sensing, the kernel-driven model (KDM) is widely used for reflectance modeling due to its simple mathematical formulation and computational efficiency. However, in contrast to non-desert scenes, desert shrub canopies are characterized by woody components that are significantly larger than the leaf areas. This deviates the assumptions of Beer's law, which is based on translucent leaves. Moreover, desert environments present a special “background” composed of sand, gravel, saline land, and biological crust, further complicating reflectance modeling. These complexities pose significant challenges for the application of KDM in desert regions. To address these issues, this study introduces a volume-scattering kernel derived from the analytical Gutschick-Wiegel (G-W) solution with a single-angle configuration, aiming to better represent radiative transfer in sparse desert canopies. The model also incorporates different geometric-optical kernels designed to account for the structure of sparse shrubs and the heterogeneous biological crust in desert, using a cover parameter to adjust their respective weights. Furthermore, terrain factors and hotspot functions were integrated to account for coherent backscattering and anisotropic scattering effects. Based on these considerations, this study proposes a new KDM, i.e., the Hotspot Li-Sparse Roujean Terrain (HLSRT) model. The HLSRT model was extensively validated using field measurements and satellite observations. It achieved a low average in situ bias for the red and near-infrared band (NIR) bands (bias = 0.005) and a low root mean square error (RMSE = 0.0299) against satellite data. Compared to existing KDMs, the HLSRT model demonstrated superior performance in reflectance modeling. These results indicate that the HLSRT model offers a reliable semi-empirical tool for modeling radiative transfer and supporting inversion studies in complex desert environments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115152"},"PeriodicalIF":11.4,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145554949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inversion of total photosynthetic area index of oilseed rape based on a multilayer microwave scattering semiempirical model adapted to each phenological stage","authors":"Rongkun Zhao ,&nbsp;Shangrong Wu ,&nbsp;Yun Shao ,&nbsp;Xuexiao Wu ,&nbsp;Yan Zha ,&nbsp;Zhiqu Liu ,&nbsp;Ming Liu ,&nbsp;Yongli Guo ,&nbsp;Lang Xia ,&nbsp;Wenbin Wu ,&nbsp;Peng Yang ,&nbsp;Huajun Tang","doi":"10.1016/j.rse.2025.115151","DOIUrl":"10.1016/j.rse.2025.115151","url":null,"abstract":"<div><div>To accurately invert the canopy parameters (Total photosynthetic area index, TPAI) of oilseed rape, a microwave characteristic layered measurement experiment was designed, and a multilayer microwave scattering semiempirical model (MLMSSM) was constructed based on the radar response changes induced by the differences in the vertical structure and canopy components at different phenological stages. This model was then applied to the main oilseed rape production areas to conduct regional TPAI inversion. Microwave characteristic layered measurement experiments were performed at six-leaf, flowering, beginning ripening and fully ripening stages of oilseed rape in the laboratory of target microwave properties (LAMP). The MLMSSM was constructed based on the LAMP-measured data, corresponding to different plant structures containing two- and three-layer submodels, and the TPAI inversion model was derived based on the correlation between the MLMSSM parameters and the crop biophysical variables. Finally, regional TPAI inversion and validation were carried out in the main oilseed rape production area (Hengyang), using Sentinel-1 SAR data. Visualization results, MLMSSM parameters and TPAI inversion results based on LAMP data all revealed the occurrence of multiple scattering interactions among distinct oilseed rape structural layers and verified the effectiveness of the measurement scheme and the model. The regional validation results showed high TPAI inversion accuracy throughout entire oilseed rape growth stages, with R² = 0.78, RMSE = 1.03, and MAE = 0.74 under VV polarization, and R² = 0.84, RMSE = 0.75, and MAE = 0.52 under VH polarization. The MLMSSM was found to significantly outperform the modified water cloud model (MWCM), increasing R² by 0.05 and 0.18 under VV and VH polarization respectively, while reducing the RMSE and MAE by 0.49–0.72. These results prove the accuracy and applicability of the MLMSSM for regional TPAI inversion of oilseed rape.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115151"},"PeriodicalIF":11.4,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preface: The role of remote sensing to improve modeling of carbon quantities and quality of carbon credits","authors":"","doi":"10.1016/j.rse.2025.115141","DOIUrl":"https://doi.org/10.1016/j.rse.2025.115141","url":null,"abstract":"No Abstract","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"70 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil laboratory and satellite spectral data filtering: A Spectral Quality Protocol (SQuaP)","authors":"Bruno dos Anjos Bartsch ,&nbsp;José A.M. Demattê ,&nbsp;Nikolaos Tziolas ,&nbsp;Jorge Tadeu Fim Rosas ,&nbsp;Gabriel Pimenta Barbosa de Sousa ,&nbsp;Nicolas Augusto Rosin ,&nbsp;Giannis Gallios ,&nbsp;Borges Marfrann Dias Melo","doi":"10.1016/j.rse.2025.115144","DOIUrl":"10.1016/j.rse.2025.115144","url":null,"abstract":"<div><div>Soil spectroscopy is a powerful technique for soil monitoring. Hundreds of soil spectral datasets, both open-access and private, are used for various applications, despite potential errors. This study presents a filtering protocol to enhance the quality of soil spectral datasets from laboratory and satellite sources, here termed the <strong>Spectral Quality Protocol (SQuaP)</strong>. At the beginning, we utilized a database of 9261 Brazilian soil samples, with paired laboratory hyperspectral (350–2500 nm), and Sentinel-2 multispectral data. Afterwards, we tested SQuaP on world-wide open datasets and on bare soil satellite observations identified by the Geospatial Soil Sensing System (GEOS3). A filtering approach is presented where we applied rule-based checks (crop residues, reflectance trend) flagged outliers via PCA-Mahalanobis, enforced contextual consistency with soil line regression, detecd cluster-aware anomalies using Isolation Forest within clusters, and validated property spectrum coherence via Random-Forest residuals. Results demonstrated at SQuaP enhances spectral data reliability, and consequently modeling performance of clay and soil organic carbon (SOC) using a Random Forest algorithm. For the hyperspectral data SQuaP increased R² of 17.55 % for clay and 1.58 % for SOC, and reduced RMSE by 10.41 g kg⁻¹ and 2.06 g kg⁻¹, respectively. For satellite data, the improvements were even more pronounced, with an increase of 15.76 % for clay and 13.38 % for SOC on R², and a reduction in RMSE. At the world scale, predictive performance improved after the implementation of SQuaP in nearly all continents, with gains in R² ranging from −2.15 % to 13.40 % across evaluated scenarios. These findings highlight SQuAP's effectiveness in noise filtering and improving predictive accuracy, particularly for multispectral data. Furthermore, these improvements were achieved without compromising data variability, as confirmed by Kernel Desnsity Estimation (KDE) analysis, indicating that the protocol successfully removed outliers while preserving the statistical integrity of the dataset. SQuaP can be adapted to suit specific datasets and applications, facilitating more accurate soil property predictions and advancing in digital soil mapping and precision agriculture.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115144"},"PeriodicalIF":11.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145554948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pan-Arctic winter sea ice classification using Sentinel-1 dual-polarized SAR images","authors":"Yan Dai ,&nbsp;Xiao-Ming Li ,&nbsp;Haotian Yuan","doi":"10.1016/j.rse.2025.115140","DOIUrl":"10.1016/j.rse.2025.115140","url":null,"abstract":"<div><div>Sea ice type information is important for climate change research and human activities in polar regions. Synthetic Aperture Radar (SAR) data, particularly from the Sentinel-1 (S1) mission, has become a key source for high-resolution sea ice mapping. With the growing volume of S1 SAR data, deep learning (DL) methods have been explored for sea ice classification. However, the limited availability of fine labels and overlapping backscatter intensity among ice types remain major obstacles to achieving automated fine-scale sea ice classification across the pan-Arctic region. This paper proposes a model, named IceDeepLab, for the automatic mapping of sea ice type across the pan-Arctic region in winter using S1 dual-polarized SAR images, based on the Deep Learning framework DeepLabv3+. The IceDeepLab can identify open water, young ice, first-year ice and old ice, generating pixel-wise classification maps at 400 m resolution. A sea ice type dataset, comprising 174 finely labelled S1 images from November 2019 to March 2020 and covering the pan-Arctic region with diverse sea ice conditions, is established for model training and validation. In particular, radar incidence angle is included as an input to mitigate its effects on HH-polarized SAR data. The model integrates the spatially optimised ConvNeXt backbone, the newly proposed Enhanced Atrous Spatial Pyramid Pooling module, and tailored training and inference schemes. These designs make it more suitable for processing whole SAR images, effectively reducing ambiguity between different sea ice types and eliminating stitching lines, thereby improving overall performance. Experiments on the test dataset show that IceDeepLab achieves an overall accuracy of 91.9%, a mean intersection over union of 82.3%, and a mean F1-score of 90.2%, significantly outperforming the traditional DeepLabv3+ by 16.0%, 10.0%, and 11.1%, respectively. When applied to 4153 S1 images collected from November 2020 to March 2021, IceDeepLab maintains over 90% agreement with operational ice charts in the pan-Arctic region, demonstrating the model's robustness across different years. Ablation experiments, feature visualizations, and statistical analyses further validate the model's effectiveness and offer insights for future improvements in sea ice classification algorithms. Furthermore, evaluations using both the radiometer-derived sea ice concentration product and a year-round ice type dataset are conducted to explore its potential for broader applications in operational sea ice monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115140"},"PeriodicalIF":11.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction and interpretation of high-temperature heat damage risk zones in the Eastern Tibetan Transport Corridor based on a location-information-fusion convolution neural network","authors":"Zhe Chen ,&nbsp;Si Guo ,&nbsp;Huadong Guo ,&nbsp;Zhengbo Yu ,&nbsp;Xiangqi Lei ,&nbsp;Qingyun Ji ,&nbsp;Ziqiong He ,&nbsp;Ruichun Chang ,&nbsp;Zhongchang Sun ,&nbsp;Xiangjun Pei ,&nbsp;Zhongli Zhou ,&nbsp;Lorenzo Picco","doi":"10.1016/j.rse.2025.115155","DOIUrl":"10.1016/j.rse.2025.115155","url":null,"abstract":"<div><div>High-temperature heat damage (HTHD) is a common geological hazard in underground engineering, and it poses a huge threat to the lives of the construction personnels and the safety of the project. The Eastern Tibetan Transport Corridor is an HTHD prone area. To address this hazard, an HTHD spatial database was built based on multi-source data such as Sustainable Development Goals Satellite-1 (SDGSAT-1) image. The spatial location relationship between geothermal samples, which is often overlooked in traditional HTHD risk zone prediction, was explored. An interpretable location-information-fusion convolutional neural network (LI-CNN) model was proposed to accurately predict and classify HTHD risk zones. The proposed LI-CNN model achieved a consistently superior performance across all evaluation metrics, with an AUC value of 0.81. The model significantly outperformed five traditional models, including logistic regression (AUC: 0.72), decision tree (AUC: 0.72), k-nearest neighbour (AUC: 0.72), extreme gradient boosting (AUC: 0.77), and convolutional neural network (AUC: 0.77). Finally, two deep learning interpretation models were combined to conduct both global and local interpretation. The results revealed that fault density, land surface temperature, and river density exerted the greatest influence on HTHD occurrence. Overall, our work contributes to the development of sustainable and disaster-resistant infrastructure in accordance with Sustainable Development Goal 9.1 to support economic development and improve human well-being.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115155"},"PeriodicalIF":11.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of irrigation timing using thermal satellite observations, a data-driven approach","authors":"Ehsan Jalilvand ,&nbsp;Sujay V. Kumar ,&nbsp;Charles Truong ,&nbsp;Erin Haacker ,&nbsp;Sarith Mahanama","doi":"10.1016/j.rse.2025.115153","DOIUrl":"10.1016/j.rse.2025.115153","url":null,"abstract":"<div><div>Irrigation is the major consumer of freshwater resources on Earth and represents the largest human intervention in the water cycle. Most irrigation modeling frameworks rely on simplified assumptions regarding the timing of irrigation that result in significant error in the irrigation water use estimation. In this study, we developed a generalized data-driven approach for estimating these irrigation timing attributes using a change point detection algorithm applied to thermal remote sensing data. Land surface temperature at cropland pixels was compared with hydrologically similar natural land cover pixels nearby to extract irrigation attributes. The approach was evaluated over two areas: Nebraska (NEB) and Mahabad, Iran (MAH), for which we had the in situ irrigation data. The method detected the start and end of the irrigation season with reasonable accuracy, exhibiting errors of 18 % and 15 % in estimating the duration of season, in NEB and MAH respectively. The cloud cover either at the start or the end of season was the primary source of error in both cases. Irrigation event detection accuracy across 10 NEB sites yielded F1-scores (Precision &amp; recall combined score) of 0.59–0.74, varying with change point detection algorithm parameters. To optimize these parameters, extensive hyperparameter tuning was performed, leading to specific suggestions tailored for different irrigation practices. The results presented here demonstrate that the LST-based approach can be effective in characterizing interannual variations in irrigation timing attributes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115153"},"PeriodicalIF":11.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil moisture retrieval under different land cover conditions based on Sentinel-1 SAR","authors":"Zhihao Shen ,&nbsp;Qisheng He ,&nbsp;Chenghui Yang ,&nbsp;Zihao Cheng","doi":"10.1016/j.rse.2025.115147","DOIUrl":"10.1016/j.rse.2025.115147","url":null,"abstract":"<div><div>Soil moisture plays a crucial role in regulating land-atmosphere interactions, directly influencing hydrological and ecological processes. Synthetic Aperture Radar (SAR) has significant advantages in soil moisture retrieval; however, the accuracy of retrieval under different land cover conditions requires further investigation. This study systematically evaluates the performance of optical and radar-derived Vegetation Indices (VIs) in the WCM-Oh coupling model using Sentinel-1C-band dual-polarization SAR data, covering five land cover types (grassland, crops, shrubland, forest and desert). Additionally, a new radar-based vegetation index, SVIdp, is proposed and tested for the first time. This index is based on the interaction of dual-polarization scattering components. The results show: (1) The WCM-Oh model significantly improved retrieval accuracy in typical vegetation areas (grasslands and crops), with the correlation coefficient (R) increasing by 0.10 and the root mean square error (RMSE) decreasing by 2 %–14 %, compared to the independent WCM model; (2) Under different land cover types, the WCM-Oh2004 coupling model based on GNDVI demonstrated high prediction accuracy (<em>R</em> = 0.66–0.85, ubRMSE = 0.0392–0.0698 m³/m³) in areas with moderate vegetation cover (e.g., grassland A, herbaceous vegetation A/B, wheat/corn rotation areas). However, areas such as alpine grasslands, artificial lawns, sparse vegetation, and tea plantations exhibited asymmetric errors, with underestimations in low-value regions and overestimations in high-value regions; (3) Performance of different vegetation indices: Biochemical optical indices (e.g., GNDVI, CVI) performed best in herbaceous vegetation areas, while structural radar indices (e.g., DpSVI, RVI) demonstrated superior stability in dense or structurally complex vegetation areas; (4) SVIdp outperformed DpRVI and NDVI in shrublands and grasslands. This study provides new insights into SAR-based soil moisture modeling and supports large-scale SAR soil moisture spatial mapping.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115147"},"PeriodicalIF":11.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145536411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions","authors":"Fujiang Ji ,&nbsp;Ting Zheng ,&nbsp;Alexey N. Shiklomanov ,&nbsp;Ruqi Yang ,&nbsp;Philip A. Townsend ,&nbsp;Fa Li ,&nbsp;Dalei Hao ,&nbsp;Hamid Dashti ,&nbsp;Kyle R. Kovach ,&nbsp;Hangkai You ,&nbsp;Junxiong Zhou ,&nbsp;Min Chen","doi":"10.1016/j.rse.2025.115149","DOIUrl":"10.1016/j.rse.2025.115149","url":null,"abstract":"<div><div>Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall <em>R</em>^<em>2</em> values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4 % to 8.4 % for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. These findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115149"},"PeriodicalIF":11.4,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145536236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil moisture retrieval from Sentinel-1: Lessons learned after more than a decade in orbit","authors":"Mehdi Rahmati ,&nbsp;Anna Balenzano ,&nbsp;Michel Bechtold ,&nbsp;Luca Brocca ,&nbsp;Anke Fluhrer ,&nbsp;Thomas Jagdhuber ,&nbsp;Kleanthis Karamvasis ,&nbsp;David Mengen ,&nbsp;Rolf H. Reichle ,&nbsp;Seung-bum Kim ,&nbsp;Ruhollah Taghizadeh-Mehrjardi ,&nbsp;Jeffrey Walker ,&nbsp;Liujun Zhu ,&nbsp;Carsten Montzka","doi":"10.1016/j.rse.2025.115146","DOIUrl":"10.1016/j.rse.2025.115146","url":null,"abstract":"<div><div>Soil moisture is a critical variable for hydrology, agriculture and climate. However, large-scale soil moisture observation remains difficult due to sparse in situ networks and the inability of optical sensors to capture it under cloud cover. Synthetic aperture radar (SAR) missions, e.g., Sentinel-1, yield unique all-weather, day and night observations with a fine spatial and temporal resolution that makes them of interest for development of global soil moisture monitoring. Consequently, this review discusses the application of C-band SAR observations from the Sentinel-1 satellite mission to estimate high-resolution near-surface soil moisture. First, the importance of SAR backscatter monitoring from Sentinel-1 is emphasized. Next, the current state-of-the-art in soil moisture retrieval from Sentinel-1 is presented. Although considerable progress has been made in near-surface soil moisture retrieval, several limitations remain. Factors such as the effects of vegetation and surface roughness on the signal, sensor and scattering model limitations, spatial and temporal constraints, and uncertainties, e.g. in data assimilation, pose challenges to its usage. While Artificial Intelligence (AI)-based retrieval methods have shown promise, their interpretability, dependence on large datasets, vulnerability to data quality, and computational burden have been major challenges. Beyond methods that rely on backscatter, there have been recent works indicating that SAR interferometric observables have the potential to estimate soil moisture, especially in arid and semi-arid regions where these are particularly sensitive to moisture changes. To address these challenges, this paper recommends integrating Sentinel-1 with other satellite mission data for a multi-sensor data integration approach (e.g., Sentinel-2 and Soil Moisture Active Passive - SMAP data), refining physical and semi-empirical models, developing advanced AI techniques able to consider physical principles, and combining with emerging data from other high temporal resolution SAR missions (e.g., NASA-ISRO SAR). The review concludes with identification of key research priorities, including standardization of retrieval frameworks, improved validation efforts on standardized reference sets, and cloud processing for real-time user cases. Overall, the review provides a thorough foundation for understanding, refining, and advancing Sentinel-1 based soil moisture retrieval methods.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115146"},"PeriodicalIF":11.4,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}