Remote Sensing of Environment最新文献

{"title":"Mapping wide-area land subsidence from groundwater use in the North China plain by machine learning-based InSAR adjustment","authors":"Mi Jiang ,&nbsp;Zhou Wu ,&nbsp;Xudong Wang ,&nbsp;Lin Bai ,&nbsp;Zhiwei Li ,&nbsp;Zhong Lu","doi":"10.1016/j.rse.2025.115226","DOIUrl":"10.1016/j.rse.2025.115226","url":null,"abstract":"<div><div>Since 2014, a series of China's water resource management policies have been implemented to mitigate groundwater-induced land subsidence in the North China Plain (NCP). While previous studies have demonstrated the benefits of Synthetic Aperture Radar interferometry (InSAR) in providing policy-relevant insights into the spatio-temporal dynamics of subsidence and groundwater recovery, most have focused on localized regions, leaving the long-term impact of these measures across the entire NCP insufficiently evaluated. A key challenge is the variation of long-wavelength errors in each SAR frame, which results in inconsistencies in the subsidence velocity field over large areas. To address this issue, this paper proposes a machine learning-based adjustment approach for routinely wide-area subsidence mapping and then fully evaluating land subsidence and associated groundwater depletion in the NCP from the end of 2014 to 2022. The novelty of this method lies in the adaptive selection of the optimal model for each SAR frame, which minimizes the varying long-wavelength errors, rather than relying on a unified model for all SAR frames as commonly used in state-of-the-art approaches. Additionally, we mitigated the difference of InSAR measurement in the overlap regions between consecutive tracks caused by the varying incidence angles by incorporating GNSS data and a plate motion model. Using synthetic and real Sentinel-1 data, we validated the performance of the proposed method against prevalent approaches through an independent GNSS validation dataset, demonstrating accuracy improvements from 3.8-17.5 mm/yr to 2.0 mm/yr. The results indicated that approximately 56,882 km² of the NCP area experienced land subsidence greater than 20 mm/yr. The central alluvial and coastal plains were the primary areas of subsidence, with a maximum cumulative subsidence of up to 2 m. The average subsidence velocity peaked in 2018 at 38.5 mm/yr. Subsidence has been alleviated after 2021. Our results revealed the lower bound of groundwater loss from the confined aquifer in the NCP, totaling 24.9 billion m³ between the end of 2014 and 2022. Of this total, 20.2 billion m³ (81 %) was lost from October 2014 to the end of 2020, with the loss decreasing to 4.7 billion m³ (19 %) during the period from January 2021 to December 2022. This study provides new evidence for China's groundwater management practices in addressing land subsidence in the NCP.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115226"},"PeriodicalIF":11.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902873","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":"A radiometrically and spatially consistent super-resolution framework for Sentinel-2","authors":"Cesar Aybar,&nbsp;Julio Contreras,&nbsp;Simon Donike,&nbsp;Enrique Portalés-Julià,&nbsp;Gonzalo Mateo-García,&nbsp;Luis Gómez-Chova","doi":"10.1016/j.rse.2025.115222","DOIUrl":"10.1016/j.rse.2025.115222","url":null,"abstract":"<div><div>Deep learning-based super-resolution (SR) models offer a promising approach to enhancing the effective spatial resolution of optical satellite images. However, existing SR implementations have shown that, while these models can reconstruct fine-scale details, they often introduce undesirable artifacts, such as nonexistent local structures, reflectance distortions, and geometric misalignment. To mitigate these issues, fully synthetic data approaches have been explored for training, as they provide complete control over the degradation process and allow precise supervision and ground-truth availability. However, challenges in domain transfer have limited their effectiveness when applied to real satellite images. In this work, we propose <em>SEN2SR</em>, a new deep learning framework trained to super-resolve Sentinel-2 images while preserving spectral and spatial alignment consistency. Our approach harmonizes synthetic training data to match the spectral and spatial characteristics of Sentinel-2, ensuring realistic and artifact-free enhancements. SEN2SR generates 2.5-meter resolution images for Sentinel-2, upsampling the 10-meter RGB and NIR bands and the 20-meter Red Edge and SWIR bands. To ensure that SR models focus exclusively on enhancing spatial resolution, we introduce a low-frequency hard constraint layer at the final stage of SR networks that always enforces spectral consistency by preserving the original low-frequency content. We evaluate a range of deep learning architectures, including Convolutional Neural Networks, Mamba, and Swin Transformers, within a comprehensive assessment framework that integrates Explainable AI (xAI) techniques. Quantitatively, our framework achieves superior PSNR while maintaining near-zero reflectance deviation and spatial misalignment, outperforming state-of-the-art SR frameworks. Moreover, we demonstrate maintained radiometric fidelity in downstream tasks that demand high-fidelity spectral information and reveal a significant correlation between model performance and pixel-level model activation. Qualitative results show that SR networks effectively handle diverse land cover scenarios without introducing spurious high-frequency details in out-of-distribution cases. Overall, this research underscores the potential of SR techniques in Earth observation, paving the way for more precise monitoring of the Earth’s surface. Models, code, and examples are publicly available at <span><span>https://github.com/ESAOpenSR/SEN2SR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115222"},"PeriodicalIF":11.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903476","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":"Hyperspectral BRDF based on UAV measurements can characterize optical properties of flat desert surfaces: A comprehensive comparison with laboratory and satellite data","authors":"Yuechao Sheng ,&nbsp;Xiuqing Hu ,&nbsp;Xingwei He ,&nbsp;Lin Chen ,&nbsp;Zhongqiu Sun ,&nbsp;Shan Lu","doi":"10.1016/j.rse.2025.115228","DOIUrl":"10.1016/j.rse.2025.115228","url":null,"abstract":"<div><div>Desert is a crucial component of terrestrial ecosystems, and its optical reflection properties are essential for understanding land surface radiative balance. Combining the hyperspectral and angular measurements has been considered as an effective method to characterize optical reflection properties of desert surfaces. However, the ground and satellite-based instruments cannot obtain both hyperspectral and angular reflectance of desert surfaces at appropriate scales due to the limitations of their measurement configurations. We employed an unmanned aerial vehicle (UAV) equipped with a spectrometer to perform hyperspectral BRDF measurements of six desert areas in Northwest China to overcome the limitations. In the comparison with those obtained from laboratory and satellite observations at the same geometries, it is found that UAV-measured hyperspectral reflectance has a spectral shape similar to that of spectra measured in laboratory settings, and UAV-measured angular reflectance shows a good match of BRDF characterization as the satellite observations in the hemispherical space. Moreover, we compared the modeled angular reflectance, which are derived from BRDF models, with measured angular reflectance of deserts at selected wavelengths from MODIS. The BRDF models using UAV-measured hyperspectral BRDF have superior performance in spectral BRDF simulation. The uncertainty analysis based on a Bayesian inversion further confirmed the reliability of the UAV data in effectively characterizing optical properties of the desert surface. These results demonstrate the robustness of UAV-based spectral and angular measurements in quantifying the hyperspectral BRDF, which provide comprehensive in-situ optical properties of desert surfaces. These data can serve as valuable validation for future studies on radiative balance of desert surfaces.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115228"},"PeriodicalIF":11.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897259","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":"An extended vector inclination method for inferring detailed slip surfaces beneath landslides from SAR and optical satellite remote sensing image","authors":"Ya Kang ,&nbsp;Zhong Lu ,&nbsp;Chaoying Zhao ,&nbsp;Yufen Niu ,&nbsp;Liquan Chen ,&nbsp;Wei Qu ,&nbsp;Menghua Li","doi":"10.1016/j.rse.2025.115225","DOIUrl":"10.1016/j.rse.2025.115225","url":null,"abstract":"<div><div>The slip-surface geometry of a landslide is crucial for stability analysis and early warning, making its accurate and detailed characterization essential for hazard assessment and mitigation. However, traditional methods for obtaining slip-surface geometry are resource-intensive, requiring significant manpower and materials. Some studies have used the three-dimensional (3D) deformation from remote sensing data to infer detailed slip-surface geometry of landslides using the mass conservation method. However, this method usually requires prior data to calibrate the model parameters, and its underlying assumptions may not be valid for all landslide scenarios. In this study, we designed an Extended Vector Inclination Method (EVIM) to determine detailed landslide slip-surface geometry and established a framework for deriving landslide thickness based on 3D deformation derived from SAR and optical remote sensing data. The experiments based on actual events (Hooskanaden, Shangxintian, and Daopo landslide) and simulations demonstrate that EVIM can reliably estimate landslide thickness in a manner consistent with in-situ measurements. Furthermore, simulations indicate that the magnitude of deformation and the error in the constrained 3D deformation affect the accuracy of our method. We conclude that in cases where the mass conservation method is difficult to apply (e.g., landslides lacking prior information), the EVIM may serve as an alternative method. The proposed framework enables rapid slip-surface inversion, making it well-suited for regional-scale landslide stability assessments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115225"},"PeriodicalIF":11.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897541","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":"Large-scale tree-level mapping of forest structure including species type with remote sensing data and ground measurements","authors":"J. Kostensalo ,&nbsp;P. Packalen ,&nbsp;M. Kuronen ,&nbsp;L. Mehtätalo ,&nbsp;S. Tuominen ,&nbsp;M. Myllymäki","doi":"10.1016/j.rse.2025.115223","DOIUrl":"10.1016/j.rse.2025.115223","url":null,"abstract":"<div><div>Remote-sensing based tree maps can be used to calculate various diversity indices, but the detection probability of trees depends on size and species. We propose a novel approach combining individual tree detection (ITD) with resampling corrections (+R) which aims to simultaneously correct the size, species, and spatial distribution of trees using scalable algorithms. Using airborne laser scanning, optical data, and ground measurements, we demonstrate the compatibility of ITD+R with two different types of forests and ITD algorithms, as well as its scalability to areas exceeding 3000 km². The tree maps were evaluated using plot-level variables and benchmarked against area-based <span><math><mi>k</mi></math></span> nearest neighbors (<span><math><mi>k</mi></math></span>-NN). The ITD+R improved ITD results for most studied metrics, with the Shannon index being an exception, and even outperformed <span><math><mi>k</mi></math></span>-NN in predicting dominant height in managed stands, though <span><math><mi>k</mi></math></span>-NN still outperformed for stem density and volume. The ITD+R approach was shown to be adaptable to various diversity indices which it has not been specifically trained on, with 254 m² plot-level predictions correlating at <span><math><mi>r</mi></math></span>=0.42–0.91. While ITD trees could be classified with OA=82.0%–86.6% to pine, spruce, and deciduous, further research is needed to account for rare tree species, as low prevalence results in a large number of false detections which cannot be sufficiently addressed with classification alone.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115223"},"PeriodicalIF":11.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897654","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":"Comparing the performance of different hyperspectral satellite imaging spectroscopy in mapping methane point-source emissions","authors":"Fei Li ,&nbsp;Keer Lin ,&nbsp;Yingqi Yan ,&nbsp;Shengxi Bai ,&nbsp;Qidan Huang ,&nbsp;Chenxi Feng ,&nbsp;Shiwei Sun ,&nbsp;Shaohua Zhao ,&nbsp;Wei Zhou ,&nbsp;Chunyan Zhou ,&nbsp;Jun Lin ,&nbsp;Xinwei Zhang ,&nbsp;Yongguang Zhang","doi":"10.1016/j.rse.2025.115224","DOIUrl":"10.1016/j.rse.2025.115224","url":null,"abstract":"<div><div>Accurate detection and quantification of methane point sources are critical for climate change mitigation. Recent advances in spaceborne hyperspectral imaging spectrometers offer new opportunities for global monitoring, yet systematic evaluation across missions remains limited. Here, we assess the spectral and radiometric performance of six operational hyperspectral satellites for methane plume detection. Results show that EMIT, EnMAP, and the GF5 series provide superior capabilities. EMIT achieved the best retrieval precision (<em>σ</em> = 18 ppb), followed by GF5–02-AHSI (<em>σ</em> = 31 ppb), EnMAP (<em>σ</em> = 33 ppb), and GF5-01 A-AHSI (<em>σ</em> = 47 ppb), outperforming PRISMA (<em>σ</em> = 84 ppb) and ZY1-02E-AHSI (<em>σ</em> = 71 ppb). These differences arise from finer spectral sampling distances (SSD: 7.4–8.4 nm), higher signal-to-noise ratios (SNR: 170–250), and more stable central wavelength shifts (0.3–1.3 nm) within the 2300 nm methane absorption region, which collectively enhance plume discrimination against surface backgrounds. In contrast, PRISMA and ZY1-02E-AHSI exhibit lower sensitivity due to larger wavelength shifts (up to 2.8 nm) and lower SNRs (140–150). Case studies illustrate successful detection of methane plumes from diverse sources, including oil and gas (O&amp;G) infrastructure, coal mines, and a landfill site, highlighting both straightforward and challenging retrieval scenarios. This cross-sensor comparison underscores the importance of spectral fidelity and radiometric performance for methane monitoring. The findings provide a quantitative basis for prioritizing existing assets and guiding the design of future missions, emphasizing that high SNR and stable spectral calibration are key for advancing global detection of point-source methane emissions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115224"},"PeriodicalIF":11.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894482","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":"Space-based assessment of NOx emissions from global oil and gas fields: Bridging the gap in current emission inventories","authors":"Piyushkumar N. Patel ,&nbsp;Ritesh Gautam ,&nbsp;Mark Omara","doi":"10.1016/j.rse.2025.115229","DOIUrl":"10.1016/j.rse.2025.115229","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Identifying air pollutant sources and quantifying related emissions provides crucial information towards improving global air quality and public health. However, emission inventories for oil and gas (OG) activities inadequately represent nitrogen oxide (NO&lt;sub&gt;x&lt;/sub&gt;) emissions—a key precursor to tropospheric ozone and secondary aerosols—with notable discrepancies identified. Satellite remote sensing provides a unique vantage point to map and quantify these emissions consistently on a global scale. Here, we quantify annual NO&lt;sub&gt;x&lt;/sub&gt; emissions from 44 major OG basins distributed globally, utilizing TROPOspheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO&lt;sub&gt;2&lt;/sub&gt;) observations with the divergence flux method. In addition, we use the spaceborne Visible Infrared Imaging Radiometer Suite (VIIRS) natural gas flaring detections to further constrain satellite-derived NO&lt;sub&gt;x&lt;/sub&gt; emissions. The divergence flux method, which addresses 3D topography corrections and chemical loss of NO&lt;sub&gt;x&lt;/sub&gt; while accounting for wind-induced flux smearing, provides a robust approach for estimating NO&lt;sub&gt;x&lt;/sub&gt; emissions. This top-down approach resolves emissions at the facility and basin scale (0.01° x 0.01°), enabling direct quantification of a major gap in current bottom-up inventories. Our findings reveal significant differences between satellite observations and established inventories, which systematically underestimate OG sector emissions. A direct comparison of our TROPOMI-derived NO&lt;sub&gt;x&lt;/sub&gt; emissions against the existing inventories suggests that EDGARv6.1 underestimates onshore emissions by 61 % and offshore emissions by 26 %. The discrepancy is even more pronounced for the CAMS-GLOB-ANT_v5.3 inventory, which underestimates onshore and offshore emissions by 78 % and 92 %, respectively. These findings hold significance for global emission assessments, demonstrating that current inventories are missing a substantial source of NO&lt;sub&gt;x&lt;/sub&gt; pollution, particularly from OG fields. Furthermore, our approach provides detailed spatial emission maps that enhance the granularity in depicting NO&lt;sub&gt;x&lt;/sub&gt; distribution with error analysis (uncertainties: 32 %-54 %) supporting that inventory discrepancies are statistically significant, representing structural deficiencies rather than measurement error. We further explore the variations in the correlation between NO&lt;sub&gt;x&lt;/sub&gt; emissions with OG production volumes and CH&lt;sub&gt;4&lt;/sub&gt; concentrations across different OG basins in North America. The observed co-locations and correlations provide important insights into co-pollution emission characteristics of methane and NO&lt;sub&gt;x&lt;/sub&gt;, that are respectively a potent greenhouse gas and a reactive air quality pollutant, linked to be originating from oil and gas activity. These findings have important implications for regulatory monitoring and verification, particularly for addressing emissions transparency requirements under internationa","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115229"},"PeriodicalIF":11.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881024","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":"Bridging the thermal gap: Generating 10 m, 3-day land surface temperature via Landsat–Sentinel-2 fusion","authors":"Yuan Qi ,&nbsp;Bo Huang ,&nbsp;Min Zhao ,&nbsp;Xiaolu Jiang ,&nbsp;Wenfei Mao","doi":"10.1016/j.rse.2025.115227","DOIUrl":"10.1016/j.rse.2025.115227","url":null,"abstract":"<div><div>High spatiotemporal resolution land surface temperature (LST) is essential for climate-impact studies, particularly for urban thermal environment analyses and vegetation phenology tracking. However, current satellite sensors exhibit inherent hardware trade-offs: Sentinel-2 (S2) provides high-resolution (10 m/5 day) optical observations without thermal capability, whereas Landsat-8/9 (L8/9), equipped with both optical and thermal sensors, suffers from coarser resolution (30 m/16 day). This configuration fails to meet the demand for fine spatial and dense temporal surface thermal monitoring. To address this gap, we propose a novel fusion framework for high spatiotemporal resolution LST generation (<span><math><mi>f</mi></math></span>HiSTR-LST). It first applies a deep-learning-based spatiotemporal fusion to densify reflectance data, then performs a spatial-spectral fusion to generate high-resolution LST. By synergizing L8/9 and S2 data, our approach reliably produces 10 m spatial-resolution LST across three overpass scenarios (joint, L8/9-only, S2-only), thus achieving an effective ∼3-day temporal resolution. Cross-validations between upscaled LST predictions and native L8/9 LST demonstrate <span><math><mi>f</mi></math></span>HiSTR-LST's robust performance across eight study areas worldwide (mean <em>R</em> = 0.90, RMSE = 1.17 K). More significantly, ground-truth validation—previously unaddressed—confirms its satisfactory accuracy (mean <em>R</em> = 0.97, RMSE = 3.45 K). The combined validation shows that <span><math><mi>f</mi></math></span>HiSTR-LST outperforms the state-of-the-art by 13 % in R and reduces RMSE by 9 %. Finally, we illustrate two applications—small-area vegetation-phenology tracking and fine-scale urban thermal-pattern delineation—which collectively showcase <span><math><mi>f</mi></math></span>HiSTR-LST's capability to resolve subtle surface thermal variations. Our study bridges a critical gap in generating high spatiotemporal resolution LST from satellite imagery, a capability crucial for investigating the nuanced effects of global warming.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115227"},"PeriodicalIF":11.4,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881023","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":"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":"2025-12-31","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":"2025-12-30","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}