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Remote Sens., Volume 17, Issue 21 (November-1 2025) – 41 articles

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23 pages, 18947 KB  
Article
IOPE-IPD: Water Properties Estimation Network Integrating Physical Model and Deep Learning for Hyperspectral Imagery
by Qi Li, Mingyu Gao, Ming Zhang, Junwen Wang, Jingjing Chen and Jinghua Li
Remote Sens. 2025, 17(21), 3546; https://doi.org/10.3390/rs17213546 (registering DOI) - 26 Oct 2025
Abstract
Hyperspectral underwater target detection holds great potential for marine exploration and environmental monitoring. A key challenge lies in accurately estimating water inherent optical properties (IOPs) from hyperspectral imagery. To address these limitations, we propose a novel water IOP estimation network to support the [...] Read more.
Hyperspectral underwater target detection holds great potential for marine exploration and environmental monitoring. A key challenge lies in accurately estimating water inherent optical properties (IOPs) from hyperspectral imagery. To address these limitations, we propose a novel water IOP estimation network to support the interpretation of bathymetric models. We propose the IOPs physical model that focuses on the description of the water IOPs, describing how the concentrations of chlorophyll, colored dissolved organic matter, and detrital material influence the absorption and backscattering coefficients. Building on this foundation, we proposed an innovative IOP estimation network integrating a physical model and deep learning (IOPE-IPD). This approach enables precise and physically interpretable estimation of the IOPs. Specially, the IOPE-IPD network takes water spectra as input. The encoder extracts spectral features, while dual parallel decoders simultaneously estimate four key parameters. Based on these outputs, the absorption and backscattering coefficients of the water body are computed using the IOPs physical model. Subsequently, the bathymetric model is employed to reconstruct the water spectrum. Under the constraint of a consistency loss, the retrieved spectrum is encouraged to closely match the input spectrum. To ensure the IOPE-IPD’s applicability across various scenarios, multiple actual and Jerlov-simulated aquatic environments were used. Comprehensive experimental results demonstrate the robustness and effectiveness of our proposed IOPE-IPD over the compared method. Full article
(This article belongs to the Special Issue Advances in Positioning, Navigation and 3D Mapping of Underwater Environments (Second Edition))
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21 pages, 7913 KB  
Article
A Novel MIMO SAR Scheme with Intra–Inter-Pulse Phase Coding and Azimuth–Elevation Joint Processing
by Wulin Peng, Wei Wang, Yongwei Zhang, Yihai Wei and Zixuan Zhang
Remote Sens. 2025, 17(21), 3544; https://doi.org/10.3390/rs17213544 (registering DOI) - 26 Oct 2025
Abstract
Echo separation has long been a challenging and prominent research focus for Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO SAR) systems. Digital beamforming (DBF) plays a critical role in achieving effective echo separation, but it often comes at the cost of high system complexity. [...] Read more.
Echo separation has long been a challenging and prominent research focus for Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO SAR) systems. Digital beamforming (DBF) plays a critical role in achieving effective echo separation, but it often comes at the cost of high system complexity. This paper proposes a novel MIMO SAR scheme based on phase-coded waveforms applied to both inter-pulses and intra-pulses. By introducing phase coding in both dimensions and performing joint azimuth–elevation processing, the proposed method effectively suppresses interference arising during the echo separation process, thereby significantly improving separation performance. Additionally, the approach allows for a significantly simplified array configuration, reducing both hardware requirements and computational burden. The effectiveness and practicality of the proposed scheme are validated through numerical simulations and distributed scene experiments, highlighting its strong potential for application in MIMO SAR systems—particularly in cost-sensitive scenarios and systems with limited elevation channels. Full article
25 pages, 3905 KB  
Article
An Enhanced Method for Optical Imaging Computation of Space Objects Integrating an Improved Phong Model and Higher-Order Spherical Harmonics
by Qinyu Zhu, Can Xu, Yasheng Zhang, Yao Lu, Xia Wang and Peng Li
Remote Sens. 2025, 17(21), 3543; https://doi.org/10.3390/rs17213543 (registering DOI) - 26 Oct 2025
Abstract
Space-based optical imaging detection serves as a crucial means for acquiring characteristic information of space objects, with the quality and resolution of images directly influencing the accuracy of subsequent missions. Addressing the scarcity of datasets in space-based optical imaging, this study introduces a [...] Read more.
Space-based optical imaging detection serves as a crucial means for acquiring characteristic information of space objects, with the quality and resolution of images directly influencing the accuracy of subsequent missions. Addressing the scarcity of datasets in space-based optical imaging, this study introduces a method that combines an improved Phong model and higher-order spherical harmonics (HOSH) for the optical imaging computation of space objects. Utilizing HOSH to fit the light field distribution, this approach comprehensively considers direct sunlight, earthshine, reflected light from other extremely distant celestial bodies, and multiple scattering from object surfaces. Through spectral reflectance experiments, an improved Phong model is developed to calculate the optical scattering characteristics of space objects and to retrieve common material properties such as metallicity, roughness, index of refraction (IOR), and Alpha for four types of satellite surfaces. Additionally, this study designs two sampling methods: a random sampling based on the spherical Fibonacci function (RSSF) and a sequential frame sampling based on predefined trajectories (SSPT). Through numerical analysis of the geometric and radiative rendering pipeline, this method simulates multiple scenarios under both high-resolution and wide-field-of-view operational modes across a range of relative distances. Simulation results validate the effectiveness of the proposed approach, with average rendering speeds of 2.86 s per frame and 1.67 s per frame for the two methods, respectively, demonstrating the capability for real-time rapid imaging while maintaining low computational resource consumption. The data simulation process spans six distinct relative distance intervals, ensuring that multi-scale images retain substantial textural features and are accompanied by attitude labels, thereby providing robust support for algorithms aimed at space object attitude estimation, and 3D reconstruction. Full article
(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)
35 pages, 10020 KB  
Article
The Evolution of the Mars Year (MY) 35 Anomalous Spring Dust Storm and Its Influence on the Chryse and Utopia Plains
by Huining He, Zhaopeng Wu, Zhaojin Rong, Fei He, Xuan Cheng, Yuqi Wang, Jiawei Gao and Yong Wei
Remote Sens. 2025, 17(21), 3542; https://doi.org/10.3390/rs17213542 (registering DOI) - 26 Oct 2025
Abstract
Dust storms have a significant impact on the Martian atmosphere and climate. Previous studies have found that regional and global dust storms mainly occur in the Mars perihelion season. However, an anomalous spring regional dust storm occurred in the aphelion season of Martian [...] Read more.
Dust storms have a significant impact on the Martian atmosphere and climate. Previous studies have found that regional and global dust storms mainly occur in the Mars perihelion season. However, an anomalous spring regional dust storm occurred in the aphelion season of Martian year 35 (MY 35). The occurrence and evolution of this new type of large dust storm and its impact on the Martian atmosphere are not yet fully understood. Using Mars Climate Sounder (MCS) dust observations, this study investigates the evolutionary characteristics of the MY 35 anomalous spring storm during its pre-storm, onset, expansion, and decay phases, by comparing it with other types of regional dust storms. The evolution of the MY 35 anomalous spring dust storm is more similar to that of the MY 35 C storm, showing north–south mirror symmetry relative to the equator, suggesting that the two storms may have similar evolutionary mechanisms. Additionally, we analyze the effects of the anomalous MY 35 storm on the atmospheric thermal and dynamical structures using a combination of MCS temperature observations and LMD-GCM wind simulation results. Eastward winds in the high latitudes of both hemispheres and westward winds in the low-to-mid latitudes are significantly enhanced during the storm, corresponding to the change in the atmospheric thermal structure and the global circulation. Finally, we performed a preliminary analysis of changes in the wind field during the spring dust storm in the Chryse and Utopia plains, which are two potential landing areas for China’s Tianwen-3 Mars sample-return mission. The vertical profiles of the simulated horizonal wind in the two plains show that, during the E storm peak time, the change in daily mean wind speed is significant above 20 km, but relatively small in the atmospheric boundary layer below ~5 km. Within the boundary layer, the horizontal wind speed shows remarkable diurnal variation, remaining relatively low during the midday hours (10:00 a.m. to 4:00 p.m.). These results can provide necessary environmental parameters related to spring dust storms for China’s Tianwen-3 mission. Full article
(This article belongs to the Special Issue Planetary Remote Sensing and Applications to Mars and Chang’E-6/7)
20 pages, 35019 KB  
Article
Explainable Shape Anomaly Detection of Space Targets from ISAR Image Sequences
by Zi Wang, Jia Duan and Lei Zhang
Remote Sens. 2025, 17(21), 3541; https://doi.org/10.3390/rs17213541 (registering DOI) - 26 Oct 2025
Abstract
Shape anomaly detection of satellites is critical to ensuring their safe operation. With the intrinsic range-Doppler projection mechanism, the inverse synthetic aperture radar (ISAR) image sequence has a high potential for localizing and detecting satellites’ shape anomalies. In this manuscript, we propose a [...] Read more.
Shape anomaly detection of satellites is critical to ensuring their safe operation. With the intrinsic range-Doppler projection mechanism, the inverse synthetic aperture radar (ISAR) image sequence has a high potential for localizing and detecting satellites’ shape anomalies. In this manuscript, we propose a Fully Convolutional Data Description (FCDD) joint temporal sequential classification network to extract both spatial and temporal information for shape anomaly detection of space targets. The explainable FCDD network is initially built to generate explainable heatmaps of anomalies. An attention-based GRU is used to learn context information between heatmap sequences by converting detection into sequential binary classification. In this way, the joint temporal and spatial information extraction proposal can not only detect shape anomalies with high precision and low false alarm rate but also retain the capability of generating explainable heatmaps to localize satellite shape anomaly components. Extensive experimental results confirm the superiority of the proposal. Full article
(This article belongs to the Special Issue New Insights in Remote Sensing Image Interpretation with Deep Learning)
23 pages, 5266 KB  
Article
Satellite-Based Assessment of Intertidal Vegetation Dynamics in Continental Portugal with Sentinel-2 Data
by Ingrid Cardenas, Manuel Meyer, José Alberto Gonçalves, Isabel Iglesias and Ana Bio
Remote Sens. 2025, 17(21), 3540; https://doi.org/10.3390/rs17213540 (registering DOI) - 26 Oct 2025
Abstract
Vegetated intertidal ecosystems, such as seagrass meadows, salt marshes, and macroalgal beds, are vital for biodiversity, coastal protection, and climate regulation; however, they remain highly vulnerable to anthropogenic and climate-induced stressors. This study aims to assess interannual changes in intertidal vegetation cover along [...] Read more.
Vegetated intertidal ecosystems, such as seagrass meadows, salt marshes, and macroalgal beds, are vital for biodiversity, coastal protection, and climate regulation; however, they remain highly vulnerable to anthropogenic and climate-induced stressors. This study aims to assess interannual changes in intertidal vegetation cover along the Portuguese mainland coast from 2015 to 2024 using Sentinel-2 satellite imagery calibrated with high-resolution multispectral unoccupied aerial vehicle (UAV) data, to determine the most accurate index for mapping intertidal vegetation. Among the 16 indices tested, the Atmospherically Resilient Vegetation Index (ARVI) showed the highest predictive performance. Based on a model relating intertidal vegetation cover to this index, an ARVI value greater than or equal to 0.214 was established to estimate the area covered with intertidal vegetation. Applying this threshold to time-series data revealed considerable spatial and temporal variability in vegetation cover, with estuarine systems such as the Ria de Aveiro and the Ria Formosa showing the greatest extents and marked fluctuations. At the national level, no consistent overall trend was identified for the study period. Despite limitations related to satellite image resolution and single-site validation, the results demonstrate the feasibility and utility of combining UAV data and satellite indices for long-term, large-scale monitoring of intertidal vegetation. Full article
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36 pages, 27661 KB  
Article
Analysis of Land Subsidence During Rapid Urbanization in Chongqing, China: Impacts of Metro Construction, Groundwater Dynamics, and Natural–Anthropogenic Environment Interactions
by Yuanfeng Li, Yuan Yao, Yice Deng, Jiazheng Ren and Keren Dai
Remote Sens. 2025, 17(21), 3539; https://doi.org/10.3390/rs17213539 (registering DOI) - 26 Oct 2025
Abstract
Urban land subsidence, a globally prevalent environmental problem and geohazard triggered by rapid urbanization, threatens ecological security and socioeconomic stability. Chongqing City in southwestern China, recognized as the world’s largest mountainous city, has encountered land subsidence challenges exacerbated by accelerated urban construction. This [...] Read more.
Urban land subsidence, a globally prevalent environmental problem and geohazard triggered by rapid urbanization, threatens ecological security and socioeconomic stability. Chongqing City in southwestern China, recognized as the world’s largest mountainous city, has encountered land subsidence challenges exacerbated by accelerated urban construction. This study proposes an effective method for extracting urbanization intensity by integrating Sentinel-1, Sentinel-2, and its derived synthetic aperture radar and spectral indices features, combined with texture features. The small baseline subset interferometric synthetic aperture radar technique was employed to monitor land subsidence in Chongqing between 2018 and 2024. Furthermore, the relationships among urbanization intensity, metro construction, groundwater dynamics, and land subsidence were systematically analyzed. Finally, geographical detector and multiscale geographically weighted regression models were employed to explore the interactive effects of anthropogenic, topographic, geological-tectonic, climatic, and land surface characteristic factors contributing to land subsidence. The findings reveal that (1) the method proposed in this paper can effectively extract urbanization intensity and provide an important approach to analyze the influence of urbanization on land subsidence. (2) Land subsidence along newly opened metro lines was more pronounced than along existing lines. The shorter the interval between metro construction completion and the start of operation, the greater the subsidence observed within the first 3 months of operation, which indicates that this interval influences land subsidence. (3) Overall, groundwater dynamics and land subsidence showed a clear correlation from June 2022 to June 2023, a phenomenon largely caused by the extreme summer high temperatures of 2022, triggering reduced precipitation and a notable groundwater decline. Beyond this period, however, only a weak correlation was observed between groundwater fluctuations and land subsidence trends, indicating that other factors likely dominated subsidence dynamics. (4) The anthropogenic factors have a higher relative influence on land subsidence than other drivers. In terms of q-value, the top six factors are road network density > precipitation > elevation > enhanced normalized difference impervious surface index > population density > nighttime light, while distance to fault exhibits the least explanatory power. Given Chongqing’s exemplary status as a mountainous city, this study offers a foundational reference for subsequent quantitative analyses of land subsidence and its drivers in other mountainous cities worldwide. Full article
(This article belongs to the Special Issue Advances in SAR Remote Sensing for Landslide Monitoring and Hazard Assessment)
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26 pages, 7456 KB  
Article
More Accurate and Reliable Phenology Retrieval in Southwest China: Multi-Method Comparison and Uncertainty Analysis
by Feng Tang, Zhongxi Ge and Xufeng Wang
Remote Sens. 2025, 17(21), 3538; https://doi.org/10.3390/rs17213538 (registering DOI) - 26 Oct 2025
Abstract
Accurate phenological information is crucial for evaluating ecosystem dynamics and the carbon budget. As one of China’s largest terrestrial ecosystem carbon pools, Southwest China plays a significant role in achieving the “dual carbon” goals of carbon peaking and carbon neutrality. However, evergreen forests [...] Read more.
Accurate phenological information is crucial for evaluating ecosystem dynamics and the carbon budget. As one of China’s largest terrestrial ecosystem carbon pools, Southwest China plays a significant role in achieving the “dual carbon” goals of carbon peaking and carbon neutrality. However, evergreen forests are widely distributed in this region, and phenology extraction based on vegetation indices has certain limitations, while SIF-based phenology extraction offers a viable alternative. This study first evaluated phenological results derived from three solar-induced chlorophyll fluorescence (SIF) datasets, six curve-fitting methods, and five phenological extraction thresholds at flux sites to determine the optimal threshold and SIF data for phenological indicator extraction. Secondly, uncertainties in phenological indicators obtained from the six fitting methods were quantified at the regional scale. Finally, based on the optimal phenological results, the spatiotemporal variations in phenology in Southwest China were systematically analyzed. Results show: (1) Optimal thresholds are 20% for the start of growing season (SOS) and 30% for the end of growing season (EOS), with GOSIF best for SOS and EOS, and CSIF for the peak of growing season (POS). (2) Cubic Smoothing Spline (CS) has the lowest uncertainty for SOS, while Savitzky–Golay Filter (SG) has the lowest for EOS and POS. (3) Phenology exhibits significant spatial heterogeneity, with SOS and POS generally showing an advancing trend, and EOS and length of growing season (LOS) showing a delaying (extending) trend. This study provides a reference for phenology extraction in regions with frequent cloud cover and widespread evergreen vegetation, supporting effective assessment of regional ecosystem dynamics and carbon balance. Full article
(This article belongs to the Section Ecological Remote Sensing)
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19 pages, 5900 KB  
Article
Land-Cover Controls on the Accuracy of PS-InSAR-Derived Concrete Track Settlement Measurements
by Byung-kyu Kim, Joonyoung Kim, Jeongjun Park, Ilwha Lee and Mintaek Yoo
Remote Sens. 2025, 17(21), 3537; https://doi.org/10.3390/rs17213537 (registering DOI) - 25 Oct 2025
Abstract
Accurate monitoring of settlement in high-speed railway embankments is critical for operational safety and long-term serviceability. This study investigates the applicability of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) for quantifying millimeter-scale deformations and emphasizes how surrounding environmental factors influence measurement accuracy. Using [...] Read more.
Accurate monitoring of settlement in high-speed railway embankments is critical for operational safety and long-term serviceability. This study investigates the applicability of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) for quantifying millimeter-scale deformations and emphasizes how surrounding environmental factors influence measurement accuracy. Using 29 TerraSAR-X images acquired between 2016 and 2018, PS-InSAR-derived settlements were compared with precise leveling survey data across twelve representative embankment sections of the Honam High-Speed Railway in South Korea. Temporal and spatial discrepancies between the two datasets were harmonized through preprocessing, allowing robust accuracy assessment using mean absolute error (MAE) and standard deviation (SD). Results demonstrate that PS-InSAR reliably captures settlement trends, with MAE ranging from 1.7 to 4.2 mm across different scenes. However, significant variability in accuracy was observed depending on local land-cover composition. Correlation analysis revealed that vegetation-dominated areas, such as agricultural and forest land, reduce persistent scatterer density and increase measurement variability, whereas high-reflectivity surfaces, including transportation facilities and buildings, enhance measurement stability and precision. These findings confirm that environmental conditions are decisive factors in determining the performance of PS-InSAR. The study highlights the necessity of integrating site-specific land-cover information when designing and interpreting satellite-based monitoring strategies for railway infrastructure management. Full article
(This article belongs to the Special Issue Remote Sensing for Sustainability and Durability of Transportation Infrastructures (Second Edition))
23 pages, 25388 KB  
Article
High-Resolution Monitoring and Driving Factor Analysis of Long-Term Surface Deformation in the Linfen-Yuncheng Basin
by Yuting Wu, Longyong Chen, Tao Jiang, Yihao Xu, Yan Li and Zhe Jiang
Remote Sens. 2025, 17(21), 3536; https://doi.org/10.3390/rs17213536 (registering DOI) - 25 Oct 2025
Abstract
The comprehensive, accurate, and rapid acquisition of large-scale surface deformation using Interferometric Synthetic Aperture Radar (InSAR) technology provides crucial information support for regional eco-geological safety assessments and the rational development and utilization of groundwater resources. The Linfen-Yuncheng Basin in Shanxi Province is one [...] Read more.
The comprehensive, accurate, and rapid acquisition of large-scale surface deformation using Interferometric Synthetic Aperture Radar (InSAR) technology provides crucial information support for regional eco-geological safety assessments and the rational development and utilization of groundwater resources. The Linfen-Yuncheng Basin in Shanxi Province is one of China’s historically most frequented regions for geological hazards in plain areas, such as land subsidence and ground fissures. This study employed the coherent point targets based Small Baseline Subset (SBAS) time-series InSAR technique to interpret a dataset of 224 scenes of 5 m resolution RADARSAT-2 satellite SAR images acquired from January 2017 to May 2024. This enabled the acquisition of high-resolution spatiotemporal characteristics of surface deformation in the Linfen-Yuncheng Basin during the monitoring period. The results show that the area with a deformation rate exceeding 5 mm/a in the study area accounts for 12.3% of the total area, among which the subsidence area accounts for 11.1% and the uplift area accounts for 1.2%, indicating that the overall surface is relatively stable. There are four relatively significant local subsidence areas in the study area. The total area with a rate exceeding 30 mm/a is 41.12 km2, and the maximum cumulative subsidence is close to 810 mm. By combining high-resolution satellite images and field survey data, it is found that the causes of the four subsidence areas are all the extraction of groundwater for production, living, and agricultural irrigation. This conclusion is further confirmed by comparing the InSAR monitoring results with the groundwater level data of monitoring wells. In addition, on-site investigations reveal that there is a mutually promoting and spatially symbiotic relationship between land subsidence and ground fissures in the study area. The non-uniform subsidence areas monitored by InSAR show significant ground fissure activity characteristics. The InSAR monitoring results can be used to guide the identification and analysis of ground fissure disasters. This study also finds that due to the implementation of surface water supply projects, the demand for groundwater in the study area has been continuously decreasing. The problem of ground water over-extraction has been gradually alleviated, which in turn promotes the continuous recovery of the groundwater level and reduces the development intensity of land subsidence and ground fissures. Full article
(This article belongs to the Special Issue Applications of Radar Remote Sensing in Earth Observation)
21 pages, 3844 KB  
Article
Impacts of Aerosol Optical Depth on Different Types of Cloud Macrophysical and Microphysical Properties over East Asia
by Xinlei Han, Qixiang Chen, Zijue Song, Disong Fu and Hongrong Shi
Remote Sens. 2025, 17(21), 3535; https://doi.org/10.3390/rs17213535 (registering DOI) - 25 Oct 2025
Abstract
Aerosol–cloud interaction remains one of the largest sources of uncertainty in weather and climate modeling. This study investigates the impacts of aerosols on the macro- and microphysical properties of different cloud types over East Asia, based on nine years of joint satellite observations [...] Read more.
Aerosol–cloud interaction remains one of the largest sources of uncertainty in weather and climate modeling. This study investigates the impacts of aerosols on the macro- and microphysical properties of different cloud types over East Asia, based on nine years of joint satellite observations from CloudSat, CALIPSO, and MODIS, combined with ERA5 reanalysis data. Results reveal pronounced cloud-type dependence in aerosol effects on cloud fraction, cloud top height, and cloud thickness. Aerosols enhance the development of convective clouds while suppressing the vertical extent of stable stratiform clouds. For ice-phase structures, ice cloud fraction and ice water path significantly increase with aerosol optical depth (AOD) in deep convective and high-level clouds, whereas mid- to low-level clouds exhibit reduced ice crystal effective radius and ice water content, indicating an “ice crystal suppression effect.” Even after controlling for 14 meteorological variables, partial correlations between AOD and cloud properties remain significant, suggesting a degree of aerosol influence independent of meteorological conditions. Humidity and wind speed at different altitudes are identified as key modulating factors. These findings highlight the importance of accounting for cloud-type differences, moisture conditions, and dynamic processes when assessing aerosol–cloud–climate interactions and provide observational insights to improve the parameterization of aerosol indirect effects in climate models. Full article
(This article belongs to the Special Issue Observation of Optical and Radiative Properties of Atmospheric Aerosols by Ground-Based Remote Sensing Measurements)
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17 pages, 10370 KB  
Article
Spatiotemporal Distribution and Applicability Evaluation of Remote Sensing Precipitation in River Basins Across Mainland China
by Chenxi Zhao, Mingyi Xu, Zhiming Wang, Ji Li, Jingyu Zheng, Mei Yuan, Yuyu Tao and Lijuan Shi
Remote Sens. 2025, 17(21), 3534; https://doi.org/10.3390/rs17213534 (registering DOI) - 25 Oct 2025
Abstract
This research evaluates the performance of the Final Run remote sensing precipitation products from the Integrated Multi-satellite Retrievals for GPM (IMERG-F) in complex terrain river basins (2014–2023). Utilizing decade-long daily precipitation data from 2415 manned national-level ground stations, the evaluation employs eight statistical [...] Read more.
This research evaluates the performance of the Final Run remote sensing precipitation products from the Integrated Multi-satellite Retrievals for GPM (IMERG-F) in complex terrain river basins (2014–2023). Utilizing decade-long daily precipitation data from 2415 manned national-level ground stations, the evaluation employs eight statistical metrics—probability of detection, false alarm ratio, accuracy, critical success index, Pearson correlation coefficient (PCC), root mean square difference, mean difference, and relative difference—to analyze detection accuracy, correlation, and bias on daily, monthly, and annual scales. The main findings include the following: (1) IMERG-F’s daily precipitation detection capability follows a three-tier spatial pattern (northwest to southeast), aligning with the stepped terrain of China. (2) Stronger correlations (PCC = 0.7–0.9) with gauge data emerge in southeastern regions despite higher biases, while northwestern areas show weaker correlations but fewer deviations. (3) IMERG-F overestimates annual rainy days, but slightly underestimates precipitation intensity compared with ground observations. (4) Annual precipitation estimates exceed gauge measurements, particularly in the Songhua and Liao River Basins (18–20% overestimation). Monthly analysis shows fewer errors during rainy seasons versus winter dry periods, with pronounced seasonal variations in northwestern basins. These findings emphasize the need for terrain-aware calibration to improve satellite precipitation monitoring in hydrologically diverse basins, particularly addressing seasonal and spatial error patterns in water resource management applications in northern China. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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20 pages, 7699 KB  
Article
Large-Gradient Displacement Monitoring and Parameter Inversion of Mining Collapse with the Optical Flow Method of Synthetic Aperture Radar Images
by Chuanjiu Zhang and Jie Chen
Remote Sens. 2025, 17(21), 3533; https://doi.org/10.3390/rs17213533 (registering DOI) - 25 Oct 2025
Abstract
Monitoring large-gradient surface displacement caused by underground mining remains a significant challenge for conventional Synthetic Aperture Radar (SAR)-based techniques. This study introduces optical flow methods to monitor large-gradient displacement in mining areas and conducts a comprehensive comparison with Small Baseline Subset Interferometric SAR [...] Read more.
Monitoring large-gradient surface displacement caused by underground mining remains a significant challenge for conventional Synthetic Aperture Radar (SAR)-based techniques. This study introduces optical flow methods to monitor large-gradient displacement in mining areas and conducts a comprehensive comparison with Small Baseline Subset Interferometric SAR (SBAS-InSAR) and Pixel Offset Tracking (POT) methods. Using 12 high-resolution TerraSAR-X (TSX) SAR images over the Daliuta mining area in Yulin, China, we evaluate the performance of each method in terms of sensitivity to displacement gradients, computational efficiency, and monitoring accuracy. Results indicate that SBAS-InSAR is only capable of detecting displacement at the decimeter level in the Dalinta mining area and is unable to monitor rapid, large-gradient displacement exceeding the meter scale. While POT can detect meter-scale displacements, it suffers from low efficiency and low precision. In contrast, the proposed optical flow method (OFM) achieves sub-pixel accuracy with root mean square errors of 0.17 m (compared to 0.26 m for POT) when validated against Global Navigation Satellite System (GNSS) data while improving computational efficiency by nearly 30 times compared to POT. Furthermore, based on the optical flow results, mining parameters and three-dimensional (3D) displacement fields were successfully inverted, revealing maximum vertical subsidence exceeding 4.4 m and horizontal displacement over 1.5 m. These findings demonstrate that the OFM is a reliable and efficient tool for large-gradient displacement monitoring in mining areas, offering valuable support for hazard assessment and mining management. Full article
(This article belongs to the Special Issue Geological Hazard Monitoring, Identify, Predict, and Risk Assessment Using Geographic Information Science and Remote Sensing)
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40 pages, 5708 KB  
Review
Advances on Multimodal Remote Sensing Foundation Models for Earth Observation Downstream Tasks: A Survey
by Guoqing Zhou, Lihuang Qian and Paolo Gamba
Remote Sens. 2025, 17(21), 3532; https://doi.org/10.3390/rs17213532 (registering DOI) - 24 Oct 2025
Abstract
Remote sensing foundation models (RSFMs) have demonstrated excellent feature extraction and reasoning capabilities under the self-supervised learning paradigm of “unlabeled datasets—model pre-training—downstream tasks”. These models achieve superior accuracy and performance compared to existing models across numerous open benchmark datasets. However, when confronted with [...] Read more.
Remote sensing foundation models (RSFMs) have demonstrated excellent feature extraction and reasoning capabilities under the self-supervised learning paradigm of “unlabeled datasets—model pre-training—downstream tasks”. These models achieve superior accuracy and performance compared to existing models across numerous open benchmark datasets. However, when confronted with multimodal data, such as optical, LiDAR, SAR, text, video, and audio, the RSFMs exhibit limitations in cross-modal generalization and multi-task learning. Although several reviews have addressed the RSFMs, there is currently no comprehensive survey dedicated to vision–X (vision, language, audio, position) multimodal RSFMs (MM-RSFMs). To tackle this gap, this article provides a systematic review of MM-RSFMs from a novel perspective. Firstly, the key technologies underlying MM-RSFMs are reviewed and analyzed, and the available multimodal RS pre-training datasets are summarized. Then, recent advances in MM-RSFMs are classified according to the development of backbone networks and cross-modal interaction methods of vision–X, such as vision–vision, vision–language, vision–audio, vision–position, and vision–language–audio. Finally, potential challenges are analyzed, and perspectives for MM-RSFMs are outlined. This survey from this paper reveals that current MM-RSFMs face the following key challenges: (1) a scarcity of high-quality multimodal datasets, (2) limited capability for multimodal feature extraction, (3) weak cross-task generalization, (4) absence of unified evaluation criteria, and (5) insufficient security measures. Full article
(This article belongs to the Section AI Remote Sensing)
23 pages, 10676 KB  
Article
Hourly and 0.5-Meter Green Space Exposure Mapping and Its Impacts on the Urban Built Environment
by Yan Wu, Weizhong Su, Yingbao Yang and Jia Hu
Remote Sens. 2025, 17(21), 3531; https://doi.org/10.3390/rs17213531 (registering DOI) - 24 Oct 2025
Abstract
Accurately mapping urban residents’ exposure to green space at high spatiotemporal resolutions is essential for assessing disparities and equality across blocks and enhancing urban environment planning. In this study, we developed a framework to generate hourly green space exposure maps at 0.5 m [...] Read more.
Accurately mapping urban residents’ exposure to green space at high spatiotemporal resolutions is essential for assessing disparities and equality across blocks and enhancing urban environment planning. In this study, we developed a framework to generate hourly green space exposure maps at 0.5 m resolution using multiple sources of remote sensing data and an Object-Based Image Classification with Graph Convolutional Network (OBIC-GCN) model. Taking the main urban area in Nanjing city of China as the study area, we proposed a Dynamic Residential Green Space Exposure (DRGE) metric to reveal disparities in green space access across four housing price blocks. The Palma ratio was employed to explain the inequity characteristics of DRGE, while XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive explanation) methods were utilized to explore the impacts of built environment factors on DRGE. We found that the difference in daytime and nighttime DRGE values was significant, with the DRGE value being higher after 6:00 compared to the night. Mean DRGE on weekends was about 1.5 times higher than on workdays, and the DRGE in high-priced blocks was about twice that in low-priced blocks. More than 68% of residents in high-priced blocks experienced over 8 h of green space exposure during weekend nighttime (especially around 19:00), which was much higher than low-price blocks. Moreover, spatial inequality in residents’ green space exposure was more pronounced on weekends than on workdays, with lower-priced blocks exhibiting greater inequality (Palma ratio: 0.445 vs. 0.385). Furthermore, green space morphology, quantity, and population density were identified as the critical factors affecting DRGE. The optimal threshold for Percent of Landscape (PLAND) was 25–70%, while building density, height, and Sky View Factor (SVF) were negatively correlated with DRGE. These findings address current research gaps by considering population mobility, capturing green space supply and demand inequities, and providing scientific decision-making support for future urban green space equality and planning. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Urban Environment and Climate)
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23 pages, 11034 KB  
Article
UEBNet: A Novel and Compact Instance Segmentation Network for Post-Earthquake Building Assessment Using UAV Imagery
by Ziying Gu, Shumin Wang, Kangsan Yu, Yuanhao Wang and Xuehua Zhang
Remote Sens. 2025, 17(21), 3530; https://doi.org/10.3390/rs17213530 (registering DOI) - 24 Oct 2025
Abstract
Unmanned aerial vehicle (UAV) remote sensing is critical in assessing post-earthquake building damage. However, intelligent disaster assessment via remote sensing faces formidable challenges from complex backgrounds, substantial scale variations in targets, and diverse spatial disaster dynamics. To address these issues, we propose UEBNet, [...] Read more.
Unmanned aerial vehicle (UAV) remote sensing is critical in assessing post-earthquake building damage. However, intelligent disaster assessment via remote sensing faces formidable challenges from complex backgrounds, substantial scale variations in targets, and diverse spatial disaster dynamics. To address these issues, we propose UEBNet, a high-precision post-earthquake building instance segmentation model that systematically enhances damage recognition by integrating three key modules. Firstly, the Depthwise Separable Convolutional Block Attention Module suppresses background noise that visually resembles damaged structures. This is achieved by expanding the receptive field using multi-scale pooling and dilated convolutions. Secondly, the Multi-feature Fusion Module generates scale-robust feature representations for damaged buildings with significant size differences by processing feature streams from different receptive fields in parallel. Finally, the Adaptive Multi-Scale Interaction Module accurately reconstructs the irregular contours of damaged buildings through an advanced feature alignment mechanism. Extensive experiments were conducted using UAV imagery collected after the Ms 6.8 earthquake in Tingri County, Tibet Autonomous Region, China, on 7 January 2025, and the Ms 6.2 earthquake in Jishishan County, Gansu Province, China, on 18 December 2023. Results indicate that UEBNet enhances segmentation mean Average Precision (mAPseg) and bounding box mean Average Precision (mAPbox) by 3.09% and 2.20%, respectively, with equivalent improvements of 2.65% in F1-score and 1.54% in overall accuracy, outperforming state-of-the-art instance segmentation models. These results demonstrate the effectiveness and reliability of UEBNet in accurately segmenting earthquake-damaged buildings in complex post-disaster scenarios, offering valuable support for emergency response and disaster relief. Full article
(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)
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20 pages, 7276 KB  
Article
Semantic Segmentation of Coral Reefs Using Convolutional Neural Networks: A Case Study in Kiritimati, Kiribati
by Dominica E. Harrison, Gregory P. Asner, Nicholas R. Vaughn, Calder E. Guimond and Julia K. Baum
Remote Sens. 2025, 17(21), 3529; https://doi.org/10.3390/rs17213529 (registering DOI) - 24 Oct 2025
Abstract
Habitat complexity plays a critical role in coral reef ecosystems by enhancing habitat availability, increasing ecological resilience, and offering coastal protection. Structure-from-motion (SfM) photogrammetry has become a standard approach for quantifying habitat complexity in reef monitoring programs. However, a major bottleneck remains in [...] Read more.
Habitat complexity plays a critical role in coral reef ecosystems by enhancing habitat availability, increasing ecological resilience, and offering coastal protection. Structure-from-motion (SfM) photogrammetry has become a standard approach for quantifying habitat complexity in reef monitoring programs. However, a major bottleneck remains in the two-dimensional (2D) classification of benthic cover in three-dimensional (3D) models, where experts are required to manually annotate individual colonies and identify coral species or taxonomic groups. With recent advances in deep learning and computer vision, automated classification of benthic habitats is possible. While some semi-automated tools exist, they are often limited in scope or do not provide semantic segmentation. In this investigation, we trained a convolutional neural network with the ResNet101 architecture on three years (2015, 2017, and 2019) of human-annotated 2D orthomosaics from Kiritimati, Kiribati. Our model accuracy ranged from 71% to 95%, with an overall accuracy of 84% and a mean intersection of union of 0.82, despite highly imbalanced training data, and it demonstrated successful generalizability when applied to new, untrained 2023 plots. Successful automation depends on training data that captures local ecological variation. As coral monitoring efforts move toward standardized workflows, locally developed models will be key to achieving fully automated, high-resolution classification of benthic communities across diverse reef environments. Full article
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23 pages, 4617 KB  
Article
IAASNet: Ill-Posed-Aware Aggregated Stereo Matching Network for Cross-Orbit Optical Satellite Images
by Jiaxuan Huang, Haoxuan Sun and Taoyang Wang
Remote Sens. 2025, 17(21), 3528; https://doi.org/10.3390/rs17213528 (registering DOI) - 24 Oct 2025
Abstract
Stereo matching estimates disparity by finding correspondences between stereo image pairs. Under ill-posed conditions such as geometric differences, radiometric differences, and temporal changes, accurate estimation becomes difficult due to insufficient matching information. In remote sensing imagery, such ill-posed regions are more common because [...] Read more.
Stereo matching estimates disparity by finding correspondences between stereo image pairs. Under ill-posed conditions such as geometric differences, radiometric differences, and temporal changes, accurate estimation becomes difficult due to insufficient matching information. In remote sensing imagery, such ill-posed regions are more common because of complex imaging conditions. This problem is particularly pronounced in cross-track satellite stereo images, where existing methods often fail to effectively handle noise due to insufficient features or excessive reliance on prior assumptions. In this work, we propose an ill-posed-aware aggregated satellite stereo matching network, which integrates monocular depth estimation with an ill-posed-guided adaptive aware geometry fusion module to balance local and global features while reducing noise interference. In addition, we design an enhanced mask augmentation strategy during training to simulate occlusions and texture loss in complex scenarios, thereby improving robustness. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches on the US3D dataset, achieving a 5.38% D1-error and 0.958 pixels endpoint error (EPE). In particular, our method shows significant advantages in ill-posed regions. Overall, the proposed network not only exhibits strong feature learning ability but also demonstrates robust generalization in real-world remote sensing applications. Full article
(This article belongs to the Special Issue Signal Processing, Image Processing and Fusion Techniques in Remote Sensing)
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21 pages, 49278 KB  
Article
Lightweight Attention Refined and Complex-Valued BiSeNetV2 for Semantic Segmentation of Polarimetric SAR Image
by Ruiqi Xu, Shuangxi Zhang, Chenchu Dong, Shaohui Mei, Jinyi Zhang and Qiang Zhao
Remote Sens. 2025, 17(21), 3527; https://doi.org/10.3390/rs17213527 (registering DOI) - 24 Oct 2025
Abstract
In the semantic segmentation tasks of polarimetric SAR images, deep learning has become an important end-to-end method that uses convolutional neural networks (CNNs) and other advanced network architectures to extract features and classify the target region pixel by pixel. However, applying original networks [...] Read more.
In the semantic segmentation tasks of polarimetric SAR images, deep learning has become an important end-to-end method that uses convolutional neural networks (CNNs) and other advanced network architectures to extract features and classify the target region pixel by pixel. However, applying original networks used to optical images for PolSAR image segmentation directly will result in the loss of rich phase information in PolSAR data, which leads to unsatisfactory classification results. In order to make full use of polarization information, the complex-valued BiSeNetV2 with a bilateral-segmentation structure is studied and expanded in this work. Then, considering further improving the ability to extract semantic features in the complex domain and alleviating the imbalance of polarization channel response, the complex-valued BiSeNetV2 with a lightweight attention module (LAM-CV-BiSeNetV2) is proposed for the semantic segmentation of PolSAR images. LAM-CV-BiSeNetV2 supports complex-valued operations, and a lightweight attention module (LAM) is designed and introduced at the end of the Semantic Branch to enhance the extraction of detailed features. Compared with the original BiSeNetV2, the LAM-CV-BiSeNetV2 can not only more fully extract the phase information from polarimetric SAR data, but also has stronger semantic feature extraction capabilities. The experimental results on the Flevoland and San Francisco datasets demonstrate that the proposed LAM has better and more stable performance than other commonly used attention modules, and the proposed network can always obtain better classification results than BiSeNetV2 and other known real-valued networks. Full article
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24 pages, 6893 KB  
Article
Biases of Sentinel-5P and Suomi-NPP Cloud Top Height Retrievals: A Global Comparison
by Zhuowen Zheng, Lechao Dong, Jie Yang, Qingxin Wang, Hao Lin and Siwei Li
Remote Sens. 2025, 17(21), 3526; https://doi.org/10.3390/rs17213526 (registering DOI) - 24 Oct 2025
Viewed by 90
Abstract
Cloud Top Height (CTH) is a fundamental parameter in atmospheric science, critically influencing Earth’s radiation budget and hydrological cycle. Satellite-based passive remote sensing provides the primary means of monitoring CTH on a global scale due to its extensive spatial coverage. However, these passive [...] Read more.
Cloud Top Height (CTH) is a fundamental parameter in atmospheric science, critically influencing Earth’s radiation budget and hydrological cycle. Satellite-based passive remote sensing provides the primary means of monitoring CTH on a global scale due to its extensive spatial coverage. However, these passive retrieval techniques often rely on idealized physical assumptions, leading to significant systematic biases. To quantify these biases, this study provides an evaluation of two prominent passive CTH products, i.e., Sentinel-5P (S5P, O2 A-band) and Suomi-NPP (NPP, thermal infrared), by comparing their global data from July 2018 to June 2019 against the active CloudSat/CALIPSO (CC) reference. The results reveal stark and complementary error patterns. For single-layer liquid clouds over land, the products exhibit opposing biases, with S5P underestimating CTH while NPP overestimates it. For ice clouds, both products show a general underestimation, but NPP is more accurate. In challenging two-layer scenes, both retrieval methods show large systematic biases, with S5P often erroneously detecting the lower cloud layer. These distinct error characteristics highlight the fundamental limitations of single-sensor retrievals and reveal the potential to organically combine the advantages of different products to improve CTH accuracy. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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21 pages, 2767 KB  
Article
Semi-Automated Extraction of Active Fire Edges from Tactical Infrared Observations of Wildfires
by Christopher C. Giesige, Eric Goldbeck-Dimon, Andrew Klofas and Mario Miguel Valero
Remote Sens. 2025, 17(21), 3525; https://doi.org/10.3390/rs17213525 (registering DOI) - 24 Oct 2025
Abstract
Remote sensing of wildland fires has become an integral part of fire science. Airborne sensors provide high spatial resolution and can provide high temporal resolution, enabling fire behavior monitoring at fine scales. Fire agencies frequently use airborne long-wave infrared (LWIR) imagery for fire [...] Read more.
Remote sensing of wildland fires has become an integral part of fire science. Airborne sensors provide high spatial resolution and can provide high temporal resolution, enabling fire behavior monitoring at fine scales. Fire agencies frequently use airborne long-wave infrared (LWIR) imagery for fire monitoring and to aid in operational decision-making. While tactical remote sensing systems may differ from scientific instruments, our objective is to illustrate that operational support data has the capacity to aid scientific fire behavior studies and to facilitate the data analysis. We present an image processing algorithm that automatically delineates active fire edges in tactical LWIR orthomosaics. Several thresholding and edge detection methodologies were investigated and combined into a new algorithm. Our proposed method was tested on tactical LWIR imagery acquired during several fires in California in 2020 and compared to manually annotated mosaics. Jaccard index values ranged from 0.725 to 0.928. The semi-automated algorithm successfully extracted active fire edges over a wide range of image complexity. These results contribute to the integration of infrared fire observations captured during firefighting operations into scientific studies of fire spread and support landscape-scale fire behavior modeling efforts. Full article
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18 pages, 1644 KB  
Technical Note
Cross-Validation of Surface Reflectance Between GF5-02 AHSI and EnMAP Across Diverse Land Cover Types
by Shuhan Liu, Yujie Zhao, Xia Wang, Li Guo, Kun Shang, Ping Zhou, Bangyu Ge, Bai Xue and Jiaxing Liu
Remote Sens. 2025, 17(21), 3524; https://doi.org/10.3390/rs17213524 - 24 Oct 2025
Viewed by 104
Abstract
Multi-source hyperspectral data are increasingly applied in environmental monitoring, precision agriculture, and geological exploration, yet differences in sensor characteristics hinder interoperability. This study presents a systematic cross-validation of surface reflectance between the German EnMAP mission and the Chinese GF5-02 Advanced Hyperspectral Imager (AHSI) [...] Read more.
Multi-source hyperspectral data are increasingly applied in environmental monitoring, precision agriculture, and geological exploration, yet differences in sensor characteristics hinder interoperability. This study presents a systematic cross-validation of surface reflectance between the German EnMAP mission and the Chinese GF5-02 Advanced Hyperspectral Imager (AHSI) across four representative land cover types: minerals in the East Tianshan Mountains, tropical grasslands in Hainan Danzhou, desert in Dunhuang, and inland salt lakes in Qinghai. Using EnMAP Level-2A products as reference, we evaluated GF5-02 reflectance with spectral angle (SA), root mean squared error (RMSE), relative RMSE (RRMSE), and correlation coefficient (R). Results show strong consistency for high- and medium-reflectance surfaces (R > 0.96, SA < 0.08 rad), while water bodies exhibit larger discrepancies (R = 0.82, SA = 0.34 rad), likely due to atmospheric correction and sensor response differences. Additional ground validation in the East Tianshan region confirmed the reliability and stability of GF5-02 data. Overall, GF5-02 demonstrates high consistency with EnMAP across most land cover types, supporting quantitative applications, though further improvements are needed for low-reflectance environments. Full article
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25 pages, 79534 KB  
Article
Late Quaternary Segment Faulting Behavior of Yilan-Yitong Fault and Its Potential Seismic Hazards, NE China, by Using Multisource Remote Sensing Data
by Qinghai Wei, Shuang Liu, Panxin Yang, Chaozhong Hu, Wenqiao Li, Peng Du, Jian Kang, Yanbo Zhang, Zhe Zhang, Qinjian Tian and Yueren Xu
Remote Sens. 2025, 17(21), 3523; https://doi.org/10.3390/rs17213523 - 23 Oct 2025
Viewed by 206
Abstract
Quantitative investigation of major fault zones with low slip rates and long recurrence intervals in densely populated regions is essential for understanding earthquake recurrence and assessing seismic hazard. The Tanlu Fault Zone, a major lithospheric boundary extending from eastern China into Russia, provides [...] Read more.
Quantitative investigation of major fault zones with low slip rates and long recurrence intervals in densely populated regions is essential for understanding earthquake recurrence and assessing seismic hazard. The Tanlu Fault Zone, a major lithospheric boundary extending from eastern China into Russia, provides a key case study. Through remote sensing interpretation integrated with seismic-geological evidence, we identified a ~150 km-long fresh surface rupture zone along the Yilan–Yitong Fault in the Fangzheng–Tangyuan region of Heilongjiang Province, NE China. Chronological constraints from previous and recent trenching indicate that the most recent event occurred in the late Holocene, with an estimated magnitude of Mw ≈ 7.6, comparable to the scale of AD 1668 Tancheng earthquake in North China. The northeastern section of the Tanlu Fault Zone is also subject to long-term far-field Coulomb stress loading from subduction of the Pacific Plate beneath the Eurasian Plate. Although the fault exhibits long recurrence intervals, the urgency of future strong earthquakes cannot be overlooked. Furthermore, our results suggest that the northeastern Tanlu Fault Zone is characterized by segmentation, underscoring the need for refined paleoseismic investigations to constrain recurrence behavior and seismic hazard in Northeast China. Full article
(This article belongs to the Topic Disaster and Environment Monitoring Based on Multisource Remote Sensing Images)
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21 pages, 2555 KB  
Article
Enhancing PPP-B2b Performance with Regional Atmospheric Augmentation
by Qing Zhao, Shuguo Pan, Wang Gao, Xianlu Tao, Hao Liu, Zeyu Zhang and Qiang Wang
Remote Sens. 2025, 17(21), 3522; https://doi.org/10.3390/rs17213522 - 23 Oct 2025
Viewed by 150
Abstract
Currently, the PPP-B2b service faces challenges such as long convergence times and re-convergence issues after signal interruptions due to the lack of high-precision atmospheric enhancement. To address this, this study develops a multi-frequency uncombined Precise Point Positioning (PPP) model that accounts for Clock [...] Read more.
Currently, the PPP-B2b service faces challenges such as long convergence times and re-convergence issues after signal interruptions due to the lack of high-precision atmospheric enhancement. To address this, this study develops a multi-frequency uncombined Precise Point Positioning (PPP) model that accounts for Clock Constant Bias (CCB) based on PPP-B2b products, extracting atmospheric delays from reference stations and performing regional modeling. Considering the spatiotemporal characteristics of the ionosphere, a stochastic model for enhancement information that varies with time and satellite elevation is established. The performance of atmospheric-enhanced PPP-B2b is validated on the user end. Results demonstrate that zenith wet delay (ZWD) and ionospheric modeling generally achieve centimeter-level accuracy. However, during certain periods, ionospheric modeling errors are significant. By adjusting the stochastic model, approximately 98% of modeling errors can be enveloped. With atmospheric constraints, both convergence speed and positioning accuracy of PPP-B2b are significantly improved. Using thresholds of 30 cm horizontally and 40 cm vertically, the convergence times for horizontal and vertical components are approximately (16.7, 21.3) min for single BDS-3 and (3.8, 5.0) min for the dual-system combination, respectively. In contrast, with atmospheric constraints applied, convergence thresholds are met almost at the first epoch. Within one minute, single BDS-3 and the dual-system combination achieve accuracies better than (0.15, 0.3) m and (0.1, 0.2) m horizontally and vertically, respectively. Furthermore, even under high-elevation cutoff conditions, stable and rapid high-precision positioning remains achievable through atmospheric enhancement. Full article
(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)
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25 pages, 7582 KB  
Article
A Novel Framework for Long-Term Forest Disturbance Monitoring: Synergizing the LandTrendr Algorithm with CNN in Northeast China
by Zhaoyi Zheng, Ying Yu, Xiguang Yang, Xinyi Yuan and Zhuohan Hou
Remote Sens. 2025, 17(21), 3521; https://doi.org/10.3390/rs17213521 - 23 Oct 2025
Viewed by 244
Abstract
As carbon cycling and global environmental protection gain increasing attention, forest disturbance research has intensified worldwide. Constrained by limited data availability, existing frameworks often rely on extracting individual spectral bands for simple binary disturbance detection, lacking systematic approaches to visualize and classify causes [...] Read more.
As carbon cycling and global environmental protection gain increasing attention, forest disturbance research has intensified worldwide. Constrained by limited data availability, existing frameworks often rely on extracting individual spectral bands for simple binary disturbance detection, lacking systematic approaches to visualize and classify causes of disturbance over large areas. Accurately identifying disturbance types is critical because different disturbances (e.g., fires, logging, pests) exhibit vastly different impacts on forest structure, successional pathways and, consequently, forest carbon sequestration and storage capacities. This study proposes an integrated remote sensing and deep learning (DL) method for forest disturbance type identification, enabling high-precision monitoring in Northeast China from 1992 to 2023. Leveraging the Google Earth Engine platform, we integrated Landsat time-series data (30 m resolution), Global Forest Change data, and other multi-source datasets. We extracted four key vegetation indices (NDVI, EVI, NBR, NDMI) to construct long-term forest disturbance feature series. A comparative analysis showed that the proposed convolutional neural network (CNN) model with six feature bands achieved 5.16% higher overall accuracy and a 6.92% higher Kappa coefficient than a random forest (RF) algorithm. Remarkably, even with only six features, the CNN model outperformed the RF model trained on fifteen features, achieving a 0.4% higher overall accuracy and a 0.58% higher Kappa coefficient, while utilizing 60% fewer parameters. The CNN model accurately classified forest disturbances—including fires, pests, logging, and geological disasters—achieving a 92.26% overall accuracy and an 89.04% Kappa coefficient. This surpasses the 81.4% accuracy of the Global Forest Change product. The method significantly improves the spatiotemporal accuracy of regional-scale forest monitoring, offering a robust framework for tracking ecosystem dynamics. Full article
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28 pages, 78040 KB  
Article
Predicting Air Temperature Patterns in Milan Using Crowdsourced Measurements and Earth Observation Data
by Matej Žgela, Alberto Vavassori and Maria Antonia Brovelli
Remote Sens. 2025, 17(21), 3520; https://doi.org/10.3390/rs17213520 - 23 Oct 2025
Viewed by 203
Abstract
High-resolution air temperature (AT) data is essential for understanding urban heat dynamics, particularly in urban areas characterised by complex microclimates. However, AT is rarely available in such detail, emphasising the need for its modelling. This study employs a Random Forest regression framework to [...] Read more.
High-resolution air temperature (AT) data is essential for understanding urban heat dynamics, particularly in urban areas characterised by complex microclimates. However, AT is rarely available in such detail, emphasising the need for its modelling. This study employs a Random Forest regression framework to predict 20 m resolution AT maps across Milan, Italy, for 2022. We focus on seasonal heatwave periods, identified from a long-term climate reanalysis dataset, and multiple diurnal and nocturnal phases that reflect the daily evolution of AT. We predict AT from a high-quality dataset of 97 authoritative and crowdsourced stations, incorporating predictors derived from geospatial and Earth Observation data, including Sentinel-2 indices and urban morphology metrics. Model performance is highest during the late afternoon and nighttime, with an average R2 between 0.33 and 0.37, and an RMSE between 0.7 and 1.4 °C. This indicates modest, yet reasonable agreement with observations, given the challenges of high-resolution AT mapping. Daytime predictions prove more challenging, as noted in previous studies using similar methods. Furthermore, we explore the potential of hyperspectral (HS) data to estimate surface material abundances through spectral unmixing and assess their influence on AT. Results highlight the added value of HS-derived material abundance maps for insights into urban thermal properties and their relationship with AT patterns. The produced maps are useful for identifying intra-urban AT variability during extreme heat conditions and can support numerical model validation and city-scale heat mitigation planning. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surfaces: Observation, Modeling, and Data Assimilation)
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19 pages, 7070 KB  
Article
Research on the Application of Atmospheric Motion Vector from MetOp Satellite Series in CMA-GFS
by Jiali Ma, Yan Liu and Xiaomin Wan
Remote Sens. 2025, 17(21), 3519; https://doi.org/10.3390/rs17213519 - 23 Oct 2025
Viewed by 157
Abstract
Atmospheric motion vector (AMV) products from EUMETSAT’s MetOp satellite series, including MetOp-B, MetOp-C, and the MetOp-B/C tandem (MetOp-Dual), have been assimilated at many numerical weather prediction centers worldwide. However, they have not yet been applied in the China Meteorological Administration’s Global Forecast System [...] Read more.
Atmospheric motion vector (AMV) products from EUMETSAT’s MetOp satellite series, including MetOp-B, MetOp-C, and the MetOp-B/C tandem (MetOp-Dual), have been assimilated at many numerical weather prediction centers worldwide. However, they have not yet been applied in the China Meteorological Administration’s Global Forecast System (CMA-GFS). This study addresses this gap by developing assimilation techniques, including quality control and thinning methods for MetOp AMVs. Based on these techniques, one-month assimilation and forecasting experiments reveal that MetOp AMVs increased the AMV volume in CMA-GFS by 25%, filling certain gaps over polar and oceanic areas. Notable and steady improvements in the background of CMA-GFS have been found, particularly in polar and high-latitude regions. The usable forecast lead time for the global 500 hPa geopotential height is extended by 0.22 days, enhancing the reliability of medium-range forecasts. Furthermore, the more substantial improvements in short-range (0–3 days) forecasting, potentially benefit severe weather alerting. This study marks the first to successfully apply MetOp-B, MetOp-C and MetOp-Dual products in CMA-GFS, confirming their value for improving the performance of the system. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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23 pages, 16607 KB  
Article
Few-Shot Class-Incremental SAR Target Recognition with a Forward-Compatible Prototype Classifier
by Dongdong Guan, Rui Feng, Yuzhen Xie, Xiaolong Zheng, Bangjie Li and Deliang Xiang
Remote Sens. 2025, 17(21), 3518; https://doi.org/10.3390/rs17213518 - 23 Oct 2025
Viewed by 185
Abstract
In practical Synthetic Aperture Radar (SAR) applications, new-class objects can appear at any time as the rapid accumulation of large-scale and high-quantity SAR imagery and are usually supported by limited instances in most cooperative scenarios. Hence, powering advanced deep-learning (DL)-based SAR Automatic Target [...] Read more.
In practical Synthetic Aperture Radar (SAR) applications, new-class objects can appear at any time as the rapid accumulation of large-scale and high-quantity SAR imagery and are usually supported by limited instances in most cooperative scenarios. Hence, powering advanced deep-learning (DL)-based SAR Automatic Target Recognition (SAR ATR) systems with the ability to continuously learn new concepts from few-shot samples without forgetting the old ones is important. In this paper, we tackle the Few-Shot Class-Incremental Learning (FSCIL) problem in the SAR ATR field and propose a Forward-Compatible Prototype Classifier (FCPC) by emphasizing the model’s forward compatibility to incoming targets before and after deployment. Specifically, the classifier’s sensitivity to diversified cues of emerging targets is improved in advance by a Virtual-class Semantic Synthesizer (VSS), considering the class-agnostic scattering parts of targets in SAR imagery and semantic patterns of the DL paradigm. After deploying the classifier in dynamic worlds, since novel target patterns from few-shot samples are highly biased and unstable, the model’s representability to general patterns and its adaptability to class-discriminative ones are balanced by a Decoupled Margin Adaptation (DMA) strategy, in which only the model’s high-level semantic parameters are timely tuned by improving the similarity of few-shot boundary samples to class prototypes and the dissimilarity to interclass ones. For inference, a Nearest-Class-Mean (NCM) classifier is adopted for prediction by comparing the semantics of unknown targets with prototypes of all classes based on the cosine criterion. In experiments, contributions of the proposed modules are verified by ablation studies, and our method achieves considerable performance on three FSCIL of SAR ATR datasets, i.e., SAR-AIRcraft-FSCIL, MSTAR-FSCIL, and FUSAR-FSCIL, compared with numerous benchmarks, demonstrating its superiority and effectiveness in dealing with the FSCIL of SAR ATR. Full article
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23 pages, 97064 KB  
Article
A Study on the Identification of Geohazards in Henan Province Based on the Basic Deformation Products of LuTan-1
by Jing Lu, Xinming Tang, Tao Li, Lei Wei, Lingfei Guo, Xiang Zhang and Xuefei Zhang
Remote Sens. 2025, 17(21), 3517; https://doi.org/10.3390/rs17213517 - 23 Oct 2025
Viewed by 687
Abstract
Henan Province, characterized by hills and mountains in its western, northern, and southern regions, is a high-risk area for geohazards in China. In this paper, we are the first to investigate the geohazards over Henan using the basic deformation products of LuTan-1, and [...] Read more.
Henan Province, characterized by hills and mountains in its western, northern, and southern regions, is a high-risk area for geohazards in China. In this paper, we are the first to investigate the geohazards over Henan using the basic deformation products of LuTan-1, and we provide the minimum detectable deformation gradients of the products. The basic products consist of deformation field products generated by differential interferometric synthetic aperture radar (InSAR, DInSAR) and time-series deformation products derived from multi-temporal InSAR (MT-InSAR). They were produced using the acquisitions from June 2023 to February 2025. We identified 1620 potential geohazards, including 1340 landslides located in western and southern Henan, 139 ground collapses due to underground mining concentrated in the coal-rich central and eastern regions, and 141 cases of ground deformation located mainly in the agricultural areas of central and northern Henan. DInSAR detected 1470 hazards, while MT-InSAR found 150 more. By calculating the deformation between adjacent pixels, we found that the minimum detectable deformation gradients of the 150 geohazards were less than 0.061 mm/m, which is not detectable by DInSAR. The deformation gradients were greater than 0.017 mm/m and were discovered by MT-InSAR. The overall distribution exhibits a certain pattern, offering a basis for geohazard monitoring. Full article
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26 pages, 6792 KB  
Article
Predicting Wildfire Risk in Southwestern Saudi Arabia Using Machine Learning and Geospatial Analysis
by Liangwei Liao and Xuan Zhu
Remote Sens. 2025, 17(21), 3516; https://doi.org/10.3390/rs17213516 - 23 Oct 2025
Viewed by 130
Abstract
In recent years, ecosystems in Saudi Arabia have experienced severe degradation due to factors such as hyperaridity, overgrazing, climate change, urban expansion, and an increase in uncontrolled wildfires. Among these, wildfires have emerged as the second most significant threat to forests after urban [...] Read more.
In recent years, ecosystems in Saudi Arabia have experienced severe degradation due to factors such as hyperaridity, overgrazing, climate change, urban expansion, and an increase in uncontrolled wildfires. Among these, wildfires have emerged as the second most significant threat to forests after urban expansion. This study aims to map wildfire susceptibility in southwestern Saudi Arabia by identifying key driving factors and evaluating the performance of several machine learning models under conditions of limited and imbalanced data. The models tested include Maxent, logistic regression, random forest, XGBoost, and support vector machine. In addition, an NDVI-based phenological approach was applied to assess seasonal vegetation dynamics and to compare its effectiveness with conventional machine learning-based susceptibility mapping. All methods generated effective wildfire risk maps, with Maxent achieving the highest predictive accuracy (AUC = 0.974). The results indicate that human activities and dense vegetation cover are the primary contributors to wildfire occurrence. This research provides valuable insights for wildfire risk assessment in data-scarce regions and supports proactive fire management strategies in non-traditional fire-prone environments. Full article
(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)
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