Remote Sensing of Environment最新文献

{"title":"A kernel-driven BRDF model by accounting for urban building structures: Model development and preliminary application with satellite data","authors":"Tiejun Ye,&nbsp;Tao He,&nbsp;Hongxin Xu","doi":"10.1016/j.rse.2025.115217","DOIUrl":"10.1016/j.rse.2025.115217","url":null,"abstract":"<div><div>The bidirectional reflectance distribution function (BRDF) is a fundamental variable in urban surface energy balance and is crucial for applications requiring surface anisotropy information, such as aerosol monitoring, land cover classification, and so on. However, challenges remain in obtaining accurate BRDF information over urban areas using remotely sensed data. While some existing kernel-driven models provide timely and efficient BRDF retrieval and have been widely used in deriving global products, they were primarily designed for vegetation-covered surfaces with reduced accuracy over urban surfaces. To address these limitations, this study developed the Urban Analytical Kernel-driven Model (UAKM), which incorporates urban structures to better capture illumination and shadow patterns. Comparative experiments were conducted using simulated data from the Discrete Anisotropic Radiative Transfer (DART) model and Landsat surface reflectance to evaluate UAKM's performance compared with RossThick-LiSparse Reciprocal (RTLSR) and RossThick–Roujean (RTROU) models. In DART-based experiments with dense angular sampling, UAKM achieved the lowest RMSEs (&lt;0.008) and uniquely captured two key urban-specific BRDF characteristics: (1) a reduced growth rate in BRDF at the hotspot along the principal plane rather than a peak; and (2) a BRDF minimum displaced approximately 45° in azimuth from the forward-scattering principal plane. In satellite-based retrievals, UAKM also yielded the lowest RMSEs—below 0.016 in visible bands and below 0.026 in the near-infrared band—while exhibiting stronger robustness to uncertainties in solar viewing geometry. Overall, UAKM contributes to advancing BRDF modeling by integrating urban structural features, providing an efficient, accurate, and robust tool for urban monitoring with satellite data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115217"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881021","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":"Assessment and intercomparison of 23 global satellite and model-based soil moisture products using cosmic ray neutron sensing observations over Europe","authors":"Pariha Helili ,&nbsp;Xiaojun Li ,&nbsp;Jean-Pierre Wigneron ,&nbsp;Gabrielle De Lannoy ,&nbsp;Jian Peng ,&nbsp;Frédéric Frappart ,&nbsp;Jiangyuan Zeng ,&nbsp;Yao Xiao ,&nbsp;L. Karthikeyan ,&nbsp;Patricia de Rosnay ,&nbsp;Zanpin Xing ,&nbsp;Ardeshir Ebtehaj ,&nbsp;Andreas Colliander ,&nbsp;Preethi Konkathi ,&nbsp;Ke Zhang ,&nbsp;Lei Fan","doi":"10.1016/j.rse.2025.115207","DOIUrl":"10.1016/j.rse.2025.115207","url":null,"abstract":"<div><div>Comprehensive evaluation of satellite and model-based soil moisture (SM) products is essential for their further development and application. With the advent of Cosmic Ray Neutron Sensing (CRNS), which has an observation radius of 130–240 m, the spatial representativeness mismatch between these grid-based SM products and ground single-point observations during the evaluation process can be feasibly relieved. In this study, we systematically evaluated 23 gridded SM products, including single-sensor satellite, multi-sensor merged, and model-based products, using 68 CRNS measurement sites across the Europe. Our evaluation revealed that the SMAP-INRAE-BORDEAUX (SMAP-IB) SM retrievals showed the superior consistency with CRNS measurements among all analyzed products, demonstrating both high correlation (<em>R</em> = 0.80) and low unbiased root mean square error (ubRMSE = 0.050 m³/m³). The CCI/C3S combined active-passive SM products ranked second in performance (<em>R</em> &gt; 0.75, ubRMSE &lt;0.060 m³/m³). In the bias analysis, 17 products had negative bias (−0.003 m³/m³ to −0.190 m³/m³) against CRNS measurements, while AMSR2-LPRM at C1 and C2 bands and CCI/C3S at active and passive products had positive bias (0.011 m³/m³ to 0.161 m³/m³). It was also found that the capabilities of all SM products retrievals degraded in terms of R and ubRMSE with increasing vegetation density, topographic complexity and soil wetness. Most products showed the lowest ubRMSE and highest R values in cropland compared to other land cover types. Our study emphasizes the substantial potential of cosmic field-scale SM observations for the validation of satellite- and model-based SM products, and our findings have the potential to advance algorithm refinement, product improvement, and hydrometeorological applications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115207"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881022","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":"Gap-free GNSS-R wind field reconstruction: A neural mapping scheme and initial validation","authors":"Hao Du ,&nbsp;Ronan Fablet ,&nbsp;Thi Thuy Nga Nguyen ,&nbsp;Weiqiang Li ,&nbsp;Estel Cardellach ,&nbsp;Bertrand Chapron","doi":"10.1016/j.rse.2025.115218","DOIUrl":"10.1016/j.rse.2025.115218","url":null,"abstract":"<div><div>Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) has been widely demonstrated as an effective method for ocean wind speed retrieval. This study explores the feasibility of using track-wise GNSS-R wind products to generate gap-free wind fields. A physics-informed neural mapping scheme, 4DVarNet, is adapted to reconstruct wind fields. Results indicate that the root mean square errors (RMSEs) of the 1-hour, 3-hour, and 6-hour 4DVarNet winds are 1.13 m/s, 1.16 m/s, and 1.24 m/s compared to European Center for Medium-Range Weather Forecast (ECMWF) ERA5 wind products, while 1.40 m/s, 1.41 m/s, and 1.48 m/s are referred to Advanced Microwave Scanning Radiometer-2 (AMSR2) all-weather winds. Spatial and temporal error analyses further confirm the robustness of 4DVarNet-derived winds, with daily RMSEs remaining below 1.6 m/s. Error decomposition reveals discrepancies between ECMWF and GNSS-R winds, which may support future recalibration of GNSS-R wind products or enhancements to ECMWF forecasts. A case study of Super Typhoon Surigae proves that 4DVarNet winds closely align with the International Best Track Archive for Climate Stewardship (IBTrACS) track data. The reconstructed winds detect the peak intensity temporally consistent with IBTrACS data, whereas ECMWF forecasts exhibit a two-day lag. Moreover, asymmetries in Tropical Storm Kompasu are observed, with the radius of maximum wind (<span><math><msub><mi>R</mi><mtext>max</mtext></msub></math></span>) over the Northeast quadrant 38% larger than that over the Northwest quadrant. Despite the absence of background wind field inputs, 4DVarNet effectively learns wind patterns from ECMWF data and integrates GNSS-R observations to generate gap-free wind mappings, exhibiting strong agreement with ECMWF wind fields. The reconstruction performance is degraded at high winds due to the underestimation of referenced ECMWF ERA5 winds and the small quantity of observations. This limitation could be alleviated through denser GNSS-R observations from multiple missions such as Fengyun-3, Tianmu-1, recently launched HydroGNSS, etc., and other training references with more high winds for improving the representation of 4DVarNet at high winds.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115218"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845300","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":"Constrained negentropy optimisation (CoNE-opt): Using independent components to merge satellite data products","authors":"Suraj Shah ,&nbsp;Yi Liu ,&nbsp;Seokhyeon Kim ,&nbsp;Ashish Sharma","doi":"10.1016/j.rse.2025.115170","DOIUrl":"10.1016/j.rse.2025.115170","url":null,"abstract":"<div><div>Merging multiple uncertain datasets cancels random errors, resulting in a merged product that is equivalent to or better than the best individual parent product used. Merging proceeds via a weighted average of individual products, with weights derived using second-order error statistics (variance/covariance), which cannot fully capture skewed or heavy-tailed error structures, and often restrictive assumptions (e.g., zero error cross-correlation, ECC), which are frequently violated when products share retrieval algorithms or calibration data. We propose here a novel alternative for merging geophysical data, Constrained Negentropy Optimisation (CoNE-opt), which assumes the true dataset exhibits the greatest departure from Gaussianity, an assumption commonly invoked in performing Independent Component Analysis (ICA) to derive source signals. CoNE-opt maximises negentropy, which is a measure of non-Gaussianity, and uses it as the objective function while incorporating a linear error model constraint. This formulation allows joint estimation of the full error spectrum and the merging weights. The method is validated through synthetic experiments followed by merger of three global satellite-derived surface soil moisture products, SMAP, SMOS, and SMOS-IC. Comparison against reference datasets demonstrate that CoNE-opt outperforms existing merging alternatives that rely on second order statistics instead. Notably, CoNE-opt improves error magnitudes in the presence of high ECC and outliers, where most Ordinary Least Squares (OLS)-based merging methods struggle. Superior performance was observed in soil moisture merging, where the overall normalised RMSE equals 0.15 (vs. 0.34 for the best OLS-based alternative), consistently surpassing input products and existing methods. These findings support the potential of CoNE-opt as a robust framework for generating global soil moisture datasets, thereby enhancing land surface and hydrological modelling in data-sparse regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115170"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689083","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":"High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model","authors":"Shuohao Cai ,&nbsp;Yijia Xu ,&nbsp;Zhengwei Yang ,&nbsp;Wade T. Crow ,&nbsp;Zhou Zhang ,&nbsp;Jiali Shang ,&nbsp;Jiangui Liu ,&nbsp;Peter La Follette ,&nbsp;Chris Reberg-Horton ,&nbsp;Harry Schomberg ,&nbsp;Steven Mirsky ,&nbsp;Brian Davis ,&nbsp;Sarah Seehaver ,&nbsp;Alexis Correira ,&nbsp;Andrea Basche ,&nbsp;Ashley Waggoner ,&nbsp;Charles Ellis ,&nbsp;Dara Park ,&nbsp;Danielle D. Treadwell ,&nbsp;David Campbell ,&nbsp;Jingyi Huang","doi":"10.1016/j.rse.2025.115167","DOIUrl":"10.1016/j.rse.2025.115167","url":null,"abstract":"<div><div>Accurate and high spatiotemporal resolution soil moisture (SM) monitoring in cropland is important for water resource management, drought forecasting, and nutrient transport estimation at the field scale for sustainable crop production. Although recent research has applied machine learning (ML) to downscale coarse-resolution satellite SM products, most of this past work has focused only on surface SM estimation, and the performance of rootzone SM products has not been intensively evaluated in cropland. This study introduces a novel framework that integrates multi-source satellite-based ML models with the Layered Green and Ampt Infiltration with Redistribution (LGAR) model to produce high-resolution (100 m, hourly) SM products for both the surface layer (0–5 cm) and rootzone (0–100 cm) across cropland in the contiguous United States (CONUS). First, six ML models were trained using multiple high-resolution remote sensing datasets (Sentinel-1, Sentinel-2, and Landsat) to predict surface and rootzone SM. These ML predictions were then assimilated into the LGAR model using the ensemble Kalman filter (EnKF). The framework was developed and validated using an eight-fold cross-validation scheme with in-situ data from 431 cropland sites across CONUS, sourced from three networks (SCAN, USCRN, and PSA). The 100-m hourly SM data from this framework surpasses existing products (9-km SMAP L4, SMAP-based 1-km thermal hydraulic disaggregation of SM product) in spatial and temporal resolution and captures rootzone SM that is not available in the SMAP-HydroBlocks SM product. It achieves good performance, with median bias-corrected root mean squared error (ubRMSE) of 0.053 m³/m³ and median Kling-Gupta efficiency (KGE) of 0.379 in the surface layer, and median ubRMSE of 0.027 m³/m³ and median KGE of 0.302 in the rootzone. While the framework demonstrates strong performance, its accuracy varies across climatic regimes, with surface SM performing better in non-humid areas (median KGE = 0.375 versus median KGE = 0.416) and rootzone SM in humid regions (median KGE = 0.313 versus median KGE = 0.127). This high-resolution cropland SM product can potentially benefit multiple agricultural applications, such as irrigation management and nutrient leaching estimation, and provide valuable insights to support farmers and land managers in decision-making processes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115167"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689084","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":"RayExtract: A fast, scalable method for tree volume reconstruction from terrestrial laser scanning","authors":"Timothy Devereux ,&nbsp;Thomas Lowe ,&nbsp;Joshua Rivory ,&nbsp;Rafael Bohn Reckziegel ,&nbsp;Kim Calders ,&nbsp;Raja Ram Aryal ,&nbsp;Glen Eaton ,&nbsp;Zane Cooper ,&nbsp;Shaun Levick ,&nbsp;Stuart Phinn ,&nbsp;William Woodgate","doi":"10.1016/j.rse.2025.115162","DOIUrl":"10.1016/j.rse.2025.115162","url":null,"abstract":"<div><div>Accurate tree volume and structure are crucial for forest biomass estimation and ecosystem investigations. While terrestrial laser scanning (TLS) offers non-destructive pathways for the detailed three-dimensional tree reconstruction, current methods overestimate small branch volumes and often require tree segmentation and leaf–wood separation as a priori. This study introduces and validates RayExtract, a novel method for reconstructing woody volume from TLS data, utilising tools from the <em>RayCloudTools</em> library, to automate the extraction of tree structural metrics from point clouds. Our method incorporates two key morphological rules — Self-Similarity and Leonardo’s Rule — to aid branch radius and taper calculations. Likewise, it enables rapid and automated plot-scale reconstruction by integrating tree segmentation and woody structure modelling without requiring leaf point classification. In this study, RayExtract demonstrated high accuracy across four high-quality destructive harvest reference sets with concordance correlation coefficient (CCC) values ranging from 0.82 to 0.97 (n=124). To explore algorithm behaviours under different leaf conditions and point densities, we implement a framework using TLS simulation of highly realistic synthetic trees. Results from the simulation framework show consistent high accuracy of total woody volume, with CCC ranging from 0.97 to 0.98 (n=18) across four distinct scanning configurations. Fine-scale volumetric analysis revealed that incorporating simple morphological rules can effectively inform branch taper and reduce woody volume overestimation, particularly in smaller components. Furthermore, it identifies a limitation in volumetric accuracy in trees exhibiting significant taper in the lower stem. Analysis of RayExtract’s computational efficiency demonstrates that runtime and memory usage scale predictably with input data size, primarily driven by point count and the associated structural complexity within the point cloud, positioning the algorithm as well suited for large-scale applications. RayExtract represents a significant advancement in forest reconstruction, biomass estimation, and vegetation structural analysis. The method’s efficiency, accuracy, and robustness across varied forest conditions mark a substantial improvement in forest structural assessment techniques using laser scanning and have broad implications for improving regional biomass estimations, and contributing to the calibration and validation of broad-scale remote sensing observations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115162"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689085","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":"Emulation-based self-supervised SIF retrieval in the O2-A absorption band with HyPlant","authors":"Jim Buffat ,&nbsp;Miguel Pato ,&nbsp;Kevin Alonso ,&nbsp;Stefan Auer ,&nbsp;Emiliano Carmona ,&nbsp;Stefan Maier ,&nbsp;Rupert Müller ,&nbsp;Patrick Rademske ,&nbsp;Uwe Rascher ,&nbsp;Hanno Scharr","doi":"10.1016/j.rse.2025.115203","DOIUrl":"10.1016/j.rse.2025.115203","url":null,"abstract":"<div><div>The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery requires accurate atmospheric compensation to correctly disentangle its small contribution to the at-sensor radiance from other confounding factors. In spectral fitting SIF retrieval approaches this compensation is estimated in a joint optimization of free variables when fitting the measured at-sensor signal. Due to the computational complexity of Radiative Transfer Models (RTMs) that satisfy the level of precision required for accurate SIF retrieval, fully joint estimations are practically unachievable with exact physical simulation. We present in this contribution an emulator-based spectral fitting method neural network (EmSFMNN) approach integrating RTM emulation and self-supervised training for computationally efficient and accurate SIF retrieval in the O₂-A absorption band of HyPlant imagery. In a validation study with in-situ top-of-canopy SIF measurements we find improved performance over traditional retrieval methods. Furthermore, we show that the model predicts plausible SIF emission in topographically variable terrain without scene-specific adaptations. Since EmSFMNN can be adapted to hyperspectral imaging sensors in a straightforward fashion, it may prove to be an interesting SIF retrieval method for other sensors on airborne and spaceborne platforms.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115203"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823485","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":"The role of cross-polarization in producing high-resolution pan-Arctic sea ice motion from the RADARSAT Constellation Mission over several years","authors":"Alexander S. Komarov ,&nbsp;Mathieu Plante ,&nbsp;Jean-François Lemieux ,&nbsp;Stephen E.L. Howell ,&nbsp;Mike Brady","doi":"10.1016/j.rse.2025.115175","DOIUrl":"10.1016/j.rse.2025.115175","url":null,"abstract":"<div><div>We introduce a new pan-Arctic Environment and Climate Change Canada (ECCC) HIgh-Resolution sea Ice Tracking System (HIRITS) operating with the RADARSAT Constellation Mission (RCM) HH and HV synthetic aperture radar (SAR) images resampled at a resolution of 80 m. The spacing between neighbour output vectors was often around 1 km. When combining HH and HV, the resulting ice displacements (over 2 h to 3.5 days) derived for June 1, 2022 – May 31, 2025 were in an excellent agreement with International Arctic Buoy Programme data with the overall root-mean square error (RMSE) of 1.48 km, and correlation of 0.996 for the <span><math><mi>x</mi></math></span> and <span><math><mi>y</mi></math></span> components. Ice motion vectors provided by HV had consistently greater tracking cross-correlation coefficients (with the average value of 0.56) than those derived from HH (average value of 0.45), implying a higher confidence. The RCM HH + HV ice motion agreed well with the existing passive microwave products from the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSI SAF) with RMSEs of 4.19 km/d and 5.03 km/d respectively. We introduced an aggregated sea ice motion pan-Arctic gridded product at 2 km resolution that combines individual RCM ice motion products (HH and HV) derived over 3- and 7-day rolling time windows. A greater number of vectors was derived from HV compared to HH across the pan-Arctic domain, except for the situations where the HV signal is low, such as over smooth land fast ice. The new RCM HH + HV products generated since mid-May 2022 will substantially benefit various applications that require sea ice motion at high spatial resolution including accurate computation of sea ice deformation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115175"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689427","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":"Enhancing cloud detection across multiple satellite sensors using a combined Swin Transformer and UPerNet deep learning model","authors":"Shulin Pang ,&nbsp;Zhanqing Li ,&nbsp;Lin Sun ,&nbsp;Biao Cao ,&nbsp;Zhihui Wang ,&nbsp;Xinyuan Xi ,&nbsp;Xiaohang Shi ,&nbsp;Jing Xu ,&nbsp;Jing Wei","doi":"10.1016/j.rse.2025.115206","DOIUrl":"10.1016/j.rse.2025.115206","url":null,"abstract":"<div><div>Cloud detection is crucial in many applications of satellite remote sensing data. Traditional cloud detection methods typically operate at the pixel level, relying on empirically tuned thresholds or, more recently, machine learning classification schemes based on training datasets. Motivated by the success of the Transformer with its self-attention mechanism and convolutional neural networks for enhanced feature extraction, we propose a new encoder-decoder method that captures global and regional contexts with multi-scale features. This new model takes advantage of two advanced deep-learning techniques, the Swin Transformer and UPerNet (named STUPmask), demonstrating improved cloud detection accuracy and strong adaptability to diverse imagery types, spanning spectral bands from visible to thermal infrared and spatial resolutions from meters to kilometers, across a wide range of surface types, including bright scenes such as ice and desert, globally. Training and validation of the STUPmask model are conducted using data obtained from the Landsat 8 and Sentinel-2 Manually Cloud Validation Mask datasets on a global scale. STUPmask accurately estimates cloud amount with a marginal difference against reference masks (0.27 % for Landsat 8 and −0.81 % for Sentinel-2). Additionally, the model captures cloud distribution with a high overall classification accuracy (97.51 % for Landsat 8 and 96.27 % for Sentinel-2). Notably, it excels in detecting broken, thin, and semi-transparent clouds across diverse surfaces, including bright surfaces like urban and barren lands, especially with acceptable accuracy over snow and ice. These encompass the majority of challenging scenes encountered by cloud identification methods. It also adapts to cross-sensor satellite data with varying spatial resolutions (4 m–2 km) from both Low-Earth-Orbit (LEO) and Geostationary-Earth-Orbit (GEO) platforms (including GaoFen-2, MODIS, and Himawari-8), with an overall accuracy of 94.21–97.11 %. The demonstrated successes in the automatic identification of clouds with a variety of satellite imagery of different spectral channels and spatial resolutions render the method versatile for a wide range of remote sensing studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115206"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938803","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":"Remote sensing of the global cryosphere: Status, processes, and trends","authors":"Guoqing Zhang ,&nbsp;Hongjie Xie ,&nbsp;Alfonso Fernandez ,&nbsp;Christophe Kinnard ,&nbsp;Stef Lhermitte","doi":"10.1016/j.rse.2025.115220","DOIUrl":"10.1016/j.rse.2025.115220","url":null,"abstract":"<div><div>Driven by rapid technological advances in cryospheric science and the emergence of new generations of remote sensing observations, this special issue of <em>Remote Sensing of Environment</em>, entitled “Remote sensing of the global cryosphere: status, processes, and trends”, brings together 23 studies published between 2023 and 2025. Collectively, these papers showcase how multi-sensor satellite observations, high-resolution digital elevation models (DEMs), and cutting-edge deep learning techniques are revolutionizing the monitoring of glaciers, snow, glacial lakes, permafrost, sea ice, and ice shelves across the Earth's three poles: the Arctic (including Greenland), Antarctica, and High Mountain Asia (the Third Pole). These studies integrate diverse datasets – including multisource DEMs, optical, thermal, and passive microwave imageries, as well as RADAR, LiDAR, and GRACE observations - to quantify glacier mass balance, map glacial lakes, assess permafrost thermal conditions, classify sea-ice types, and detect icebergs. We organize the publications by major cryospheric themes and their distribution across polar regions and summarize the dominant remote sensing datasets and methodologies employed. Finally, we outline future directions, emphasizing multi-sensor data fusion, physics-informed modeling, and AI-driven approaches to improve predictions of cryospheric change under a warming climate.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115220"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947720","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}