Remote Sensing

20 pages, 6375 KB

Open AccessArticle

Multi-Source Satellite Altimetry for Monitoring Storm Wave Footprints in the English Channel’s Coastal Areas

by Emma Imen Turki, Edward Salameh, Carlos Lopez Solano, Md Saiful Islam, Mateo Domingues, Lotfi Aouf, David Gutierrez, Aurélien Carbonnière and Fréderic Frappart

Remote Sens. 2025, 17(18), 3262; https://doi.org/10.3390/rs17183262 - 22 Sep 2025

Viewed by 354

Abstract

Climate wave data, derived from significant wave height (SWH) altimetry, provide accurate information towards nearshore and coastal areas. Their use is crucial to enhance our capabilities of observing, understanding, and forecasting storm waves, even in complex coastal basins. In this study, SWOT nadir [...] Read more.

Climate wave data, derived from significant wave height (SWH) altimetry, provide accurate information towards nearshore and coastal areas. Their use is crucial to enhance our capabilities of observing, understanding, and forecasting storm waves, even in complex coastal basins. In this study, SWOT nadir data were combined with nine existing altimeters for assessing waves and monitoring their evolution during storms in the English Channel, near UK–French coasts. Validation against wave buoys and numerical models shows high accuracy, with correlations around 95%, decreasing to 85% when buoy track offsets > 50 km, producing the largest errors. The multi-source approach enables depth-resolved monitoring, with SWH mapping revealing ~20–25% modulation in the Channel and ~36% dissipation near the Seine Bay during storms. Spectral analysis of multi-source altimeter-derived merged observations improve time-sampling, resolving high-frequency variability from monthly to daily scales and capturing ~75% of storms. Most storm wave features along altimetry tracks are resolved, with CFOSAT mapping nearshore areas and SWOT capturing coastal zones, both achieving ~80% variance. This temporal and spatial monitoring would be further enhanced with SWOT’s 2D wide swath. This finding provides a complementary, comprehensive understanding of coastal waves and offers valuable input for data assimilation, to improve storm wave estimates in coastal basins. Full article

(This article belongs to the Special Issue Advanced Geoinformatics and Remote Sensing Technologies for Early Warning and Disaster Management)

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26 pages, 18433 KB

Open AccessArticle

Integrating Elevation Frequency Histogram and Multi-Feature Gaussian Mixture Model for Ground Filtering of UAV LiDAR Point Clouds in Densely Vegetated Areas

by Chuanxin Liu, Hongtao Wang, Baokun Feng, Cheng Wang, Xiangda Lei and Jianyang Chang

Remote Sens. 2025, 17(18), 3261; https://doi.org/10.3390/rs17183261 - 21 Sep 2025

Viewed by 279

Abstract

Unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) technology enables the acquisition of high-precision three-dimensional point clouds of the Earth’s surface. These data serve as a fundamental input for applications such as digital terrain model (DTM) construction and terrain analysis. Nevertheless, accurately [...] Read more.

Unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) technology enables the acquisition of high-precision three-dimensional point clouds of the Earth’s surface. These data serve as a fundamental input for applications such as digital terrain model (DTM) construction and terrain analysis. Nevertheless, accurately extracting ground points in densely vegetated areas remains challenging. This study proposes a point cloud filtering method for the separation of ground points by integrating elevation frequency histograms and a multi-feature Gaussian mixture model (GMM). Firstly, local elevation frequency histograms are employed to estimate the elevation range for the coarse identification of ground points. Then, GMM is applied to refine the ground segmentation by integrating geometric features, intensity, and spectral information represented by the green leaf index (GLI). Finally, Mahalanobis distance is introduced to optimize the segmentation result, thereby improving the overall stability and robustness of the method in complex terrain and vegetated environments. The proposed method was validated on three study areas with different vegetation cover and terrain conditions, achieving an average

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of 94.14%,

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of 88.45%,

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of 88.35%, and F1-score of 93.85%. Compared to existing ground filtering algorithms (e.g., CSF, SBF, and PMF), the proposed method performs well in all study areas, highlighting its robustness and effectiveness in complex environments, especially in areas densely covered by low vegetation. Full article

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19 pages, 14968 KB

Open AccessArticle

Satellite-Ground Data Fusion for Hourly 5-km Gridded Human-Perceived Temperature Estimation in the Yangtze River Basin, China

by Huabing Ke, Zhongyuan Li, Zhaohua Liu and Zhaoliang Zeng

Remote Sens. 2025, 17(18), 3260; https://doi.org/10.3390/rs17183260 - 21 Sep 2025

Viewed by 284

Abstract

Human-perceived temperature (HPT) reflects the synergistic effects of multiple meteorological factors, and its extremes challenge human-managed and natural systems worldwide, especially in densely populated regions such as the Yangtze River Basin of China. However, detailed information on HPT at high temporal (e.g., hourly) [...] Read more.

Human-perceived temperature (HPT) reflects the synergistic effects of multiple meteorological factors, and its extremes challenge human-managed and natural systems worldwide, especially in densely populated regions such as the Yangtze River Basin of China. However, detailed information on HPT at high temporal (e.g., hourly) and spatial resolution is severely lacking. In this study, we conduct a collaborative inversion for 12 HPT indices at a ~5 km spatial resolution and an hourly temporal resolution in the Yangtze River Basin from multi-source data (e.g., Himawari-8 images, meteorological stations, ERA5-Land reanalysis, and DEM data) using the LightGBM model. The model exhibited high predictive accuracy across all indices, achieving an average coefficient of determination (R²) of 0.981, root mean square error (RMSE) of 1.150 °C, and mean absolute error (MAE) of 0.860 °C. These results aligned well with observational data across spatial and temporal scales, effectively capturing the spatial heterogeneity and diurnal evolution of the region’s thermal environment. Our research provides a reliable data foundation for heat-health risk assessment and regional climate adaptation strategies. Full article

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27 pages, 8643 KB

Open AccessArticle

Determining Vertical Displacement of Agricultural Areas Using UAV-Photogrammetry and a Heteroscedastic Deep Learning Model

by Wojciech Gruszczyński, Edyta Puniach, Paweł Ćwiąkała and Wojciech Matwij

Remote Sens. 2025, 17(18), 3259; https://doi.org/10.3390/rs17183259 - 21 Sep 2025

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Abstract

This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV)-photogrammetry point clouds, offering an alternative to traditional ground filtering methods. Unlike conventional ground filters that rely on point cloud classification, the proposed [...] Read more.

This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV)-photogrammetry point clouds, offering an alternative to traditional ground filtering methods. Unlike conventional ground filters that rely on point cloud classification, the proposed approach employs heteroscedastic regression. The U-Net model predicts the conditional expected values of the elevation corrections, aiming to reduce the impact of vegetation on determined ground surface elevations. Concurrently, it estimates the logarithm of the elevation correction variance, allowing for direct quantification of the uncertainty associated with each elevation correction value. The algorithm was evaluated using three metrics: the root mean square error (RMSE) of vertical displacements, the percentage of nodes with determined displacement values, and the percentage of outliers among those values. Performance was assessed using the technique for order of preference by similarity to ideal solution (TOPSIS) method and compared against several ground-filter-based algorithms across four datasets, each including at least two time intervals. In most cases, the U-Net-based approach demonstrated a slight performance advantage over traditional ground filtering techniques. For example, for the U-Net-based algorithm, for one of the test datasets, the RMSE of the determined subsidences was 6.1 cm, the percentage of nodes with determined subsidences was 80.5%, and the percentage of outliers was 0.2%. For the same case, the algorithm based on the next best model (SMRF) allowed an RMSE of 7.7 cm to be obtained; for 77.3% of nodes, the subsidences were determined; and the percentage of outliers was 0.3%. Full article

(This article belongs to the Special Issue Interpretation and Attribution of Land Subsidence: A Remote Sensing and Machine Learning Perspective)

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25 pages, 6525 KB

Open AccessArticle

Regional Characterization of Deep Convective Clouds for Enhanced Imager Stability Monitoring and Methodology Validation

by David Doelling, Prathana Khakurel, Conor Haney, Arun Gopalan and Rajendra Bhatt

Remote Sens. 2025, 17(18), 3258; https://doi.org/10.3390/rs17183258 - 21 Sep 2025

Viewed by 165

Abstract

The NASA CERES project conducts an independent assessment of the calibration stability of MODIS and VIIRS reflective solar bands to ensure consistency in CERES-derived clouds and radiative flux products. The assessment includes the use of tropical deep convective cloud invariant targets (DCC-IT), identified [...] Read more.

The NASA CERES project conducts an independent assessment of the calibration stability of MODIS and VIIRS reflective solar bands to ensure consistency in CERES-derived clouds and radiative flux products. The assessment includes the use of tropical deep convective cloud invariant targets (DCC-IT), identified using a simple brightness temperature threshold. For visible bands, the collective DCC pixel radiance probability density function (PDF) was negatively skewed. By tracking the bright inflection point, rather than the PDF mode, and applying an anisotropic adjustment suited for the brightest DCC radiances, the lowest trend standard errors were obtained within 0.26% for NPP-VIIRS and within 0.36% for NOAA20-VIIRS and Aqua-MODIS. A kernel density estimation function was used to infer the PDF, which avoided discretization noise caused by sparse sampling. The near 10° regional consistency of the anisotropic corrected PDF inflection point radiances validated the DCC-IT approach. For the shortwave infrared (SWIR) bands, the DCC radiance variability is dependent on the ice particle scattering and absorption and is band-specific. The DCC radiance varies regionally, diurnally, and seasonally; however, the inter-annual variability is much smaller. Empirical bidirectional reflectance distribution functions (BRDFs), constructed from multi-year records, were most effective in characterizing the anisotropic behavior. Due to the distinct land and ocean as well as regional radiance differences, land, ocean, and regional BRDFs were evaluated. The regional radiance variability was mitigated by normalizing the individual regional radiances to the tropical mean radiance. Because the DCC pixel radiances have a Gaussian distribution, the mean radiance was used to track the DCC response. The regional BRDF-adjusted DCC-IT mean radiance trend standard errors were within 0.38%, 0.46%, and 1% for NOAA20-VIIRS, NPP-VIIRS, and Aqua-MODIS, respectively. Full article

(This article belongs to the Section Environmental Remote Sensing)

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20 pages, 20607 KB

Open AccessArticle

Multi-Scenario Land Use Simulation and Cost Assessment of Ecological Corridor Construction in Nanchang City

by Manyu Bi, Yexi Zhong, Daohong Gong and Zeping Xiao

Remote Sens. 2025, 17(18), 3257; https://doi.org/10.3390/rs17183257 - 21 Sep 2025

Viewed by 354

Abstract

As critical components of regional ecological networks, the protection and development of ecological corridors (ECs) are essential for enhancing ecosystem stability. To promote the effective protection of ECs, this study develops an integrated framework—comprising ecological corridor identification, land use simulation, and construction cost [...] Read more.

As critical components of regional ecological networks, the protection and development of ecological corridors (ECs) are essential for enhancing ecosystem stability. To promote the effective protection of ECs, this study develops an integrated framework—comprising ecological corridor identification, land use simulation, and construction cost assessment—to evaluate the cost of EC construction in Nanchang under multiple future land-use scenarios. High-resolution, multi-temporal remote sensing data were used to simulate land-use patterns for 2035 under three scenarios—ecological protection (EP), natural development (ND), and urban expansion (UE)—with the PLUS model. ECs were extracted using the Minimum Cumulative Resistance (MCR) model, and construction costs were quantitatively estimated by overlaying simulated land-use maps with corridor networks while incorporating land adjustment and compensation standards. The results show that: (1) 23 ECs (564.01 km in length, 997.93 km² in area) were identified in Nanchang, with higher corridor density in the northern and southeastern regions. (2) By 2035, the overall land-use structure in Nanchang is projected to remain broadly similar across the three scenarios, though differences will exist in the magnitude of change for individual land-use categories. (3) Cropland dominates the EC landscape (>60%) across all scenarios, while construction land accounts for 6.95%, 7.71%, and 8.39% under the EP, ND, and UE scenarios, respectively. (4) Estimated construction costs are 233.707, 262.354, and 288.897 billion RMB yuan under the EP, ND, and UE scenarios, respectively. Significant spatial variation in costs is observed, and the EP scenario does not consistently yield the lowest costs across administrative units. Additionally, this study proposes a refined zoning strategy for corridor management in Nanchang. The findings offer valuable insights for urban ecological planning and provide a scientific basis for mitigating regional ecological risks while promoting sustainable development in urbanized regions. Full article

(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)

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13 pages, 4670 KB

Open AccessTechnical Note

Restoration of Motion-Blurred, High-Resolution Mars Express SRC Images of Phobos

by Ryodo Hemmi and Hiroshi Kikuchi

Remote Sens. 2025, 17(18), 3256; https://doi.org/10.3390/rs17183256 - 21 Sep 2025

Viewed by 219

Abstract

We present an automated and fully reproducible pipeline for restoring motion-smeared Mars Express SRC images of Phobos. A one-dimensional motion point spread function (PSF) is derived directly from SPICE geometry and microsecond-precision exposure timing, and Wiener deconvolution (SNR = 16 dB) is applied [...] Read more.

We present an automated and fully reproducible pipeline for restoring motion-smeared Mars Express SRC images of Phobos. A one-dimensional motion point spread function (PSF) is derived directly from SPICE geometry and microsecond-precision exposure timing, and Wiener deconvolution (SNR = 16 dB) is applied to recover image sharpness. Tested on 14 images from 4 orbits spanning slant distances of 52–292 km, exposures of 14–20 milliseconds, sampling of 0.47–2.7 m/pixel, and PSF lengths of 11–119 pixels, the method achieves up to 31.7 dB PSNR, 0.78 SSIM, and positive sharpness gains across all cases. The restored images reveal sub-meter surface features previously obscured by motion blur, with residual energy reduced relative to the acquisition model. The workflow relies solely on open data and open-source tools (ISIS, ALE/SpiceyPy, OpenCV), requires no star-field calibration, and generalizes to other motion-degraded planetary datasets, providing a fully transparent and reproducible solution for high-resolution planetary imaging. Full article

(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)

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18 pages, 2949 KB

Open AccessArticle

Development of a Quantitative Survey Method for Pelagic Fish Aggregations Around an Offshore Wind Farm Using Multibeam Sonar

by Masahiro Hamana, Sara Gonzalvo, Takayoshi Otaki and Teruhisa Komatsu

Remote Sens. 2025, 17(18), 3255; https://doi.org/10.3390/rs17183255 - 21 Sep 2025

Viewed by 177

Abstract

Offshore wind farms are rapidly expanding worldwide, and the submerged structures supporting wind turbines have the potential to function as artificial reefs for marine organisms. Quantitative visualization of fish aggregations around these foundations can provide valuable information for promoting collaboration between fisheries and [...] Read more.

Offshore wind farms are rapidly expanding worldwide, and the submerged structures supporting wind turbines have the potential to function as artificial reefs for marine organisms. Quantitative visualization of fish aggregations around these foundations can provide valuable information for promoting collaboration between fisheries and offshore wind energy development. This study explored the use of multibeam sonar to detect spatial distributions and estimate the biomass of pelagic fish aggregations around the foundations of offshore wind power facilities. Fish distribution was extracted from multibeam water column image data using an automated sequence of filtering steps, ending with a spatial filter designed to remove common noise artifacts in multibeam sonar data. The resulting fish aggregations were visualized in three dimensions, revealing a tendency to cluster leeward of turbine and observation tower foundations, and fish biomass was successfully estimated from beam backscatter strength. The developed method can be applied to other offshore wind farms to demonstrate the role of turbine foundations as artificial reefs for fish. Full article

(This article belongs to the Special Issue Multi-Source Data Fusion and Feature Extraction for Underwater Target Detection)

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Graphical abstract

24 pages, 11853 KB

Open AccessArticle

CGAQ-DETR: DETR with Corner Guided and Adaptive Query for SAR Object Detection

by Zhen Zuo, Zhangjunjie Cheng, Siyang Huang, Junyu Wei and Zhuoyuan Wu

Remote Sens. 2025, 17(18), 3254; https://doi.org/10.3390/rs17183254 - 21 Sep 2025

Viewed by 386

Abstract

Object detection in Synthetic Aperture Radar (SAR) images remains a challenging task due to factors such as complex backgrounds, frequent fluctuations in object scale and quantity, and the inherent discrete scattering characteristics of SAR imaging. To address these challenges, we propose a DETR [...] Read more.

Object detection in Synthetic Aperture Radar (SAR) images remains a challenging task due to factors such as complex backgrounds, frequent fluctuations in object scale and quantity, and the inherent discrete scattering characteristics of SAR imaging. To address these challenges, we propose a DETR (DEtection TRansformer) with Corner-Guided and Adaptive Query for SAR Object Detection, which integrates a Corner-Guided Multi-Scale Feature Enhancement Module (CMFE) and an Adaptive Query Regression Module (AQR). The CMFE module processes multi-scale features by detecting and clustering corners to assess the scale and quantity of objects, which are used to compute the importance weights of features at different scales. The AQR module regresses the number of object queries by evaluating the rough object count from the low-level features, thereby achieving more precise and adaptive query allocation. Both modules are supervised by real data. Extensive experiments conducted on the SARDet-100K and FAIR-CSAR datasets demonstrate that our method achieves SOTA (state-of-the-art) performance, and achieved mAP@50 scores of 69.8% and 92.9%, validating its effectiveness and practical applicability in SAR object detection. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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25 pages, 11172 KB

Open AccessArticle

Semantic Segmentation Method of Residential Areas in Remote Sensing Images Based on Cross-Attention Mechanism

by Bin Zhao, Yang Mi, Ruohuai Sun and Chengdong Wu

Remote Sens. 2025, 17(18), 3253; https://doi.org/10.3390/rs17183253 - 20 Sep 2025

Viewed by 257

Abstract

Aiming at common problems such as high classification error rate, environmental noise interference, regional discontinuity, and structural absence in the semantic segmentation of residential areas, this paper proposes a CrossAtt-UNet architecture based on the Cross Attention mechanism. This network is based on the [...] Read more.

Aiming at common problems such as high classification error rate, environmental noise interference, regional discontinuity, and structural absence in the semantic segmentation of residential areas, this paper proposes a CrossAtt-UNet architecture based on the Cross Attention mechanism. This network is based on the Att-UNet framework and innovatively proposes a Cross Attention module. Cross-level information features are extracted by establishing cross-associations on the feature map’s horizontal and vertical coordinate axes. It ensures the efficient utilization of computing resources and significantly improves the accuracy of semantic segmentation and the adjacency relationship of the target region. After many experimental verifications, this network architecture performs outstandingly on the semantic segmentation dataset of living areas, with an accuracy of 95.47%, an mAP (mean average precision) of 94.57%, an mIoU (mean intersection over union) of 89.80%, an F1-score of 94.63%, a train_loss (training loss) of 0.0878, and a val_loss (validation loss) of 0.1459. Its segmentation performance, area integrity, and edge recognition accuracy are higher than those of mainstream networks. The concrete damage detection experiment further indicates that this network has good generalization ability, demonstrating stable performance and robustness. Full article

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36 pages, 9532 KB

Open AccessArticle

Use of SWOT Data for Hydrodynamic Modelling in a Tropical Microtidal Estuarine System: The Case of Casamance (Senegal)

by Amadou Diouf, Edward Salameh, Issa Sakho, Bamol Ali Sow, Julien Deloffre, Carlos López Solano, Emma Imen Turki and Robert Lafite

Remote Sens. 2025, 17(18), 3252; https://doi.org/10.3390/rs17183252 - 20 Sep 2025

Viewed by 301

Abstract

Since the early 1990s, satellite altimetry has significantly improved our understanding of coastal and estuarine dynamics. The Casamance estuary in Senegal exemplifies a tropical microtidal system with limited instrumentation despite pressing environmental, social, and navigational concerns. This study explores the potential of SWOT [...] Read more.

Since the early 1990s, satellite altimetry has significantly improved our understanding of coastal and estuarine dynamics. The Casamance estuary in Senegal exemplifies a tropical microtidal system with limited instrumentation despite pressing environmental, social, and navigational concerns. This study explores the potential of SWOT satellite data to support the calibration and validation of high-resolution hydrodynamic models. Multi-source dataset of in situ measurements and altimetry observations has been combined with numerical modelling to investigate the hydrodynamics in response to physical drivers. Statistical metrics were used to quantify model performance. Results show that SWOT accurately captures water level variations in the main channel (width 800 m to 5 km), including both tidal and non-tidal contributions, with high correlation (R = 0.90) and low error (RMSE < 0.25 m). Performance decreases in tributaries (R = 0.42, RMSE up to 0.34 m), due to interpolated bathymetry and complex local dynamics. Notably, Delft3D achieves R = 0.877 at Diogué (RMSE = 0.204 m) and R = 0.843 at Carabane (RMSE = 0.225 m). These findings highlight the strategic value of SWOT for improving hydrodynamic modelling in data-scarce estuarine environments. Full article

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23 pages, 4493 KB

Open AccessArticle

Future Scenarios of Global Urban Expansion and Carbon Emissions with National Heterogeneity: A Mixed-Effects Model Based on Urban Nighttime Lights

by Jiaoyi Xu, Masanobu Kii, Yoshinori Okano and Chun-Chen Chou

Remote Sens. 2025, 17(18), 3251; https://doi.org/10.3390/rs17183251 - 20 Sep 2025

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Abstract

Cities play a pivotal role in environmental transformation and climate change mitigation. Urban expansion has substantial impacts on socioeconomic development and carbon emissions. This study develops a predictive model for future urban expansion and CO₂ emissions based on nighttime light (NTL) data, [...] Read more.

Cities play a pivotal role in environmental transformation and climate change mitigation. Urban expansion has substantial impacts on socioeconomic development and carbon emissions. This study develops a predictive model for future urban expansion and CO₂ emissions based on nighttime light (NTL) data, under five SSP-RCP scenarios (SSP1–2.6, SSP2–4.5, SSP3–6.0, SSP4–6.0, and SSP5–8.5) projected to 2053. This study introduces three key improvements from previous literature: (1) a mixed-effects model to capture cross- national and regional differences in urban expansion patterns; (2) incorporation of grid-level random effects to reflect inter-city growth heterogeneity; and (3) integration of SSP-RCP scenarios to incorporate the influence of emission efficiency and socioeconomic policies. Using this improved framework, we estimate future urban expansion and carbon emissions for 555 global cities. The results show that the sensitivity of urban expansion to GDP and population growth varies across countries, leading to diverse urban expansion trajectories. Nonetheless, urban areas are projected to increase under all scenarios. Meanwhile, improvements in emission efficiency under the SSP-RCP scenarios are expected to curb future emission trajectories. This study enhances urban scenario modeling and contributes to a better understanding of regional differences in global urban growth and CO₂ emissions. Full article

(This article belongs to the Special Issue The Application of Nighttime Remote Sensing in Achieving the Sustainable Development Goals (Second Edition))

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23 pages, 20427 KB

Open AccessArticle

Analysis of Geometric Distortion in Sentinel-1 Images and Multi-Dimensional Spatiotemporal Evolution Characteristics of Surface Deformation Along the Central Yunnan Water Diversion Project

by Xiaona Gu, Yongfa Li, Xiaoqing Zuo, Cheng Huang, Mingzei Xing, Zhuopei Ruan, Yeyang Yu, Chao Shi, Jingsong Xiao and Qinheng Zou

Remote Sens. 2025, 17(18), 3250; https://doi.org/10.3390/rs17183250 - 20 Sep 2025

Viewed by 276

Abstract

The Central Yunnan Water Diversion Project (CYWDP) is currently under construction and represents China’s most extensive and geologically challenging water transfer infrastructure, facing significant geohazard risks induced by intensive engineering activities, posing severe threats to its entire lifecycle safety. Therefore, monitoring and spatiotemporal [...] Read more.

The Central Yunnan Water Diversion Project (CYWDP) is currently under construction and represents China’s most extensive and geologically challenging water transfer infrastructure, facing significant geohazard risks induced by intensive engineering activities, posing severe threats to its entire lifecycle safety. Therefore, monitoring and spatiotemporal evolution analysis of surface deformation along the CYWDP is critically important. This study presents the first integrated analysis of geometric distortions and multi-dimensional spatiotemporal deformation characteristics along the CYWDP, utilizing both ascending and descending orbit data from Sentinel-1. First, by integrating the Layover-Shadow Mask (LSM) model and R-Index method, we identified geometric distortion types in SAR imagery and evaluated their suitability for deformation monitoring. Subsequently, SBAS-InSAR technology was employed to derive line-of-sight (LOS) deformation information from 124 images (ascending) and 90 images (descending) acquisitions (2022–2024), enabling the identification of significant deformation zones and analyzing their spatial distribution characteristics. Finally, two-dimensional (2D) deformation fields were obtained through the joint inversion of ascending and descending orbit data in typical deformation zones. The results reveal that geometric distortions in Sentinel-1 imagery along the CYWDP are dominated by foreshortening effects, accounting for 35.3% of the study area in the ascending-orbit data and 37.9% in the descending-orbit data. A total of 10 significant deformation-prone areas were detected, and the most pronounced subsidence, amounting to −164 mm/y, was observed in the northern Jinning District (Luoci-Qujiang section), showing expansion trends toward water conveyance infrastructure. This study reveals surface deformation’s multi-dimensional spatiotemporal evolution patterns along the CYWDP. The findings support geohazard mitigation and provide a methodological reference for safety monitoring of major water conservancy projects in complex geological environments. Full article

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16 pages, 3477 KB

Open AccessArticle

Cross-Validation of GEMS Total Ozone from Ozone Profile and Total Column Products Using Pandora and Satellite Observations

by Sungjae Hong, Juseon Bak, Arno Keppens, Kai Yang, Kanghyun Baek, Xiong Liu, Mijeong Kim, Jhoon Kim, Lim-Seok Chang, Hyunjin Lee and Jae-Hwan Kim

Remote Sens. 2025, 17(18), 3249; https://doi.org/10.3390/rs17183249 - 20 Sep 2025

Viewed by 259

Abstract

This study presents a comprehensive validation of total ozone columns from ozone profile (O₃P) and total ozone column (O₃T) products measured by the Geostationary Environment Monitoring Spectrometer (GEMS), through comparisons with Pandora, Ozone Mapping and Profiler Suite (OMPS) and [...] Read more.

This study presents a comprehensive validation of total ozone columns from ozone profile (O₃P) and total ozone column (O₃T) products measured by the Geostationary Environment Monitoring Spectrometer (GEMS), through comparisons with Pandora, Ozone Mapping and Profiler Suite (OMPS) and TROPOspheric Monitoring Instrument (TROPOMI). O₃P version 3.0 demonstrates reduced dependence on viewing geometry compared to version 2.0, whereas the O₃T product shows a consistent offset between versions (v2.0 vs. v2.1). In comparison with Pandora, O₃P exhibits seasonal bias patterns similar to those seen in TROPOMI and OMPS, ranging from −2% in summer to +5% in winter. However, O₃T maintains abnormally persistent negative biases across seasons and times of day, along with a long-term degradation of 2–3% from 2021 to 2024. These findings suggest that O₃T biases likely result from uncorrected radiometric biases rather than algorithmic limitations. Validation metrics further highlight inconsistencies in O₃T, including a lower regression slope (~0.95) in the mid-latitude and higher root mean square errors in the low-latitude (~5%), compared to the other products (near 1.0 and 1–3%, respectively). Overall, O₃P outperforms TROPOMI and OMPS across most validation metrics in mid-latitudes and performs similarly at low latitudes. Full article

(This article belongs to the Special Issue Advances in Retrieval and Validation of Atmospheric Components by Remote Sensing)

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26 pages, 7240 KB

Open AccessArticle

Assessing the Long-Term Changes in the Suspended Particulate Matter in Hangzhou Bay Using MODIS/Aqua Data

by Xinyi Lu, Xianqiang He, Yaqi Zhao, Palanisamy Shanmugam, Fang Gong, Teng Li and Xuchen Jin

Remote Sens. 2025, 17(18), 3248; https://doi.org/10.3390/rs17183248 - 19 Sep 2025

Viewed by 338

Abstract

Hangzhou Bay (HZB) has become a hot spot in hydro-morphodynamic research due to human impacts and natural influences, as well as the substantial quantities of water discharge and sediment load of the Yangtze River and Qiantang River. Although many previous studies have analyzed [...] Read more.

Hangzhou Bay (HZB) has become a hot spot in hydro-morphodynamic research due to human impacts and natural influences, as well as the substantial quantities of water discharge and sediment load of the Yangtze River and Qiantang River. Although many previous studies have analyzed the spatial–temporal variations in suspended particulate matter (TSM) from in situ and satellite observations, the long-term changes in suspended sediment dynamics remain unclear. In this study, we quantified the long-term variation in TSM load using MODIS/Aqua data during 2003–2024. The TSM products in the HZB displayed a decreasing trend from 2003 to 2024 (k = −1.90 mg/L/year, p < 0.05), which may be attributed to decreased sediment discharge from the Yangtze River. The spatial variation in TSM provided quantitative results for HZB, with a substantially increasing trend in the southern shallow areas and a decreasing trend in the northern deep troughs and central bay. The interannual variations in TSM in winter displayed a positive correlation with the sediment load from the Yangtze River (R = 0.640 for the data during 2014–2022) and with wind speed (R = 0.676 for the data during 2009–2021). The TSM of HZB was partly affected by the combined impacts of human activities and climate change. A distinct difference in TSM concentrations on both sides of the Hangzhou Bay Bridge was observed, with higher TSM on the western side than on the eastern side for most of the year during 2003–2024. A decline in TSM was observed near Yushan Island from 2003 to 2024, attributed to large-scale land reclamation and associated alterations in tide-dominated areas. This study provides valuable insights into the long-term changes in suspended sediment and water quality in HZB, which is crucial for managing water resources, creating effective water strategies, predicting future needs, and ensuring sustainable water management. Full article

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19 pages, 12376 KB

Open AccessArticle

Analysis of GNSS Precipitable Water Vapor and Its Gradients During a Rainstorm in North China in July 2023

by Hualin Su, Yizhu Wang, Yunchang Cao, Hong Liang, Linghao Zhou and Zusi Mo

Remote Sens. 2025, 17(18), 3247; https://doi.org/10.3390/rs17183247 - 19 Sep 2025

Viewed by 182

Abstract

This study presents a water vapor gradient (WVG) retrieval method based on Global Navigation Satellite System (GNSS) tropospheric parameter estimation. A case study examined the method’s applicability to the extreme rainstorm event in North China in July 2023. Precipitable water vapor (PWV) and [...] Read more.

This study presents a water vapor gradient (WVG) retrieval method based on Global Navigation Satellite System (GNSS) tropospheric parameter estimation. A case study examined the method’s applicability to the extreme rainstorm event in North China in July 2023. Precipitable water vapor (PWV) and WVG data from 332 GNSS sites in this area were retrieved. Radar and precipitation data were combined to perform a spatiotemporal comparison study. The results show that GNSS PWV and WVG of this weather process were highly consistent with radar reflectivity and precipitation. When a high PWV (>60 mm) was accompanied by WVG convergence, radar reflectivity was significantly strong and precipitation occurred at the leading edge of large gradients and the convergence region. Based on the edge of big WVGs, observed by multiple GNSS stations, the location and movement of rainfall could be identified. In case of large amounts of PWV accompanied by plummeting WVG (down to 0.1–0.4 mm/km), high or persistent precipitation occurs. During the event, compared to the northern plateau, the plain region demonstrated higher PWV, lesser WVG variation, and more intense precipitation, likely caused by the topographic dynamic effect. GNSS PWV and WVG can be key indicators for short-range weather forecasting of extreme rainstorm events. Full article

(This article belongs to the Special Issue Recent Progress in Monitoring the Troposphere with GNSS Techniques)

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26 pages, 9229 KB

Open AccessArticle

Study on Prediction of Potato Above-Ground Biomass and Yield Based on UAV Visible Light Image

by Yiwen Chen, Yaohua Hu, Mengfei Liu, Xiaoyi Shi, Anxiang Huang, Xing Tong, Liangliang Yang and Linrun Cheng

Remote Sens. 2025, 17(18), 3246; https://doi.org/10.3390/rs17183246 - 19 Sep 2025

Viewed by 203

Abstract

Potato above-ground biomass (AGB) and tuber yield estimation remain challenging due to the subjectivity of farmer-based assessments, the high data requirements of spectral analysis methods, and the sensitivity of traditional Structure from Motion (SfM) techniques to soil elevation variability. To address these challenges, [...] Read more.

Potato above-ground biomass (AGB) and tuber yield estimation remain challenging due to the subjectivity of farmer-based assessments, the high data requirements of spectral analysis methods, and the sensitivity of traditional Structure from Motion (SfM) techniques to soil elevation variability. To address these challenges, this study proposes a novel UAV-based visible-light remote sensing framework to estimate the AGB and predict the tuber yield of potato crops. First, a new vegetation index, the Green-Red Combination Vegetation Index (GRCVI), was developed to improve the separability between vegetation and non-vegetation pixels. Second, an improved single-period SfM method was designed to mitigate errors in canopy height estimation caused by terrain variations. Fractional vegetation coverage (FVC) and plant height (PH) derived from UAV imagery were then integrated into a feedforward neural network (FNN) to predict AGB. Finally, potato tuber yield was predicted using polynomial regression based on AGB. Results showed that GRCVI combined with the numerical intersection method and SVM classification achieved FVC extraction accuracy exceeding 95%. The improved SfM method yielded canopy height estimates with R² values ranging from 0.8470 to 0.8554 and RMSE values below 2.3 cm. The AGB estimation model achieved an R² of 0.8341 and an RMSE of 19.9 g, while the yield prediction model obtained an R² of 0.7919 and an RMSE of 47.0 g. This study demonstrates the potential of UAV-based visible-light imagery for cost-effective, non-destructive, and scalable monitoring of potato growth and yield, providing methodological support for precision agriculture and high-throughput phenotyping. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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17 pages, 2527 KB

Open AccessArticle

Monocular Depth Estimation Driven Canopy Segmentation for Enhanced Determination of Vegetation Indices in Olive Grove Monitoring

by Vladan Papić, Nediljko Bugarin, Ivana Marin, Sven Gotovac and Josip Gugić

Remote Sens. 2025, 17(18), 3245; https://doi.org/10.3390/rs17183245 - 19 Sep 2025

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Abstract

This study investigates the application of unmanned aerial vehicles with multispectral cameras for monitoring the condition of olive groves with high accuracy. Multispectral images of olive groves provided detailed insight into the spectral data required for the analysis of vegetation indices. Using deep [...] Read more.

This study investigates the application of unmanned aerial vehicles with multispectral cameras for monitoring the condition of olive groves with high accuracy. Multispectral images of olive groves provided detailed insight into the spectral data required for the analysis of vegetation indices. Using deep learning-based object detection, individual olive trees were identified within the images, which allowed the extraction of parts corresponding to each tree. To separate the background from the canopy, segmentation based on the monocular depth estimation algorithm, Depth Anything, was applied. In this way, elements that are not part of the tree’s crown were removed for more accurate analysis and calculation of the NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge Index) indices. The obtained results were compared with the results obtained for unsegmented patches, threshold-based patches, and manually segmented patches. The comparison and analysis carried out shows that the proposed segmentation approach improved the accuracy of NDVI and NDRE by focusing exclusively on the crowns of the observed trees, excluding the noise of the surrounding vegetation and soil. In addition, measurements were carried out on three observed olive groves at different parts of the vegetation cycle, and the values of the vegetation indices were compared. This integrated method combining drone-based multispectral imaging, deep learning object detection, and advanced segmentation techniques highlights a robust approach to olive tree health monitoring and provides insight into seasonal vegetation dynamics, for winter and spring, to capture differences in vegetative activity. Full article

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18 pages, 10843 KB

Open AccessArticle

Spatiotemporal Dynamics of Bare Sand Patches in the Mu Us Sandy Land, China

by Kang Yang, Yanping Cao and Yingjun Pang

Remote Sens. 2025, 17(18), 3244; https://doi.org/10.3390/rs17183244 - 19 Sep 2025

Viewed by 289

Abstract

Bare sand patches are extensively distributed in dryland ecosystems, and their spatiotemporal evolution provides critical insights into regional eco-environmental changes. The Mu Us Sandy Land, a typical dryland region, exemplifies a distinctive mosaic distribution of bare sand and vegetation patches. Based on the [...] Read more.

Bare sand patches are extensively distributed in dryland ecosystems, and their spatiotemporal evolution provides critical insights into regional eco-environmental changes. The Mu Us Sandy Land, a typical dryland region, exemplifies a distinctive mosaic distribution of bare sand and vegetation patches. Based on the Google Earth Engine (GEE) platform and Landsat time-series imagery (1986–2023), this study extracted multi-temporal bare sand patches using the random forest algorithm. We quantified their spatiotemporal dynamics and identified driving mechanisms through integration with natural/socioeconomic datasets. Key findings include the following: (1) The total area of bare sand patches decreased significantly after 2000, with an average annual reduction of 530.08 km² (p < 0.01), a rate markedly exceeding pre-2000 rates. (2) Before 2000, bare sand patches were widespread across the entire region; however, by 2023, only residual patches persisted in the northwestern regions. (3) The most significant reduction in bare sand patch area is attributable to the shrinkage of giant patches (>10 km²). (4) The spatial distribution of bare sand patches is primarily controlled by a combination of natural factors, including stream, precipitation, topography, and wind regime. (5) The principal drivers of the reduction in bare sand patch area are anthropogenic activities, such as the implementation of ecological restoration projects, advancements in agricultural technology, and transformations in breeding patterns. These findings provide a scientific foundation for desertification control and ecosystem management strategies in drylands. Full article

(This article belongs to the Section Ecological Remote Sensing)

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21 pages, 18206 KB

Open AccessArticle

An Automatic Detection Method of Slow-Moving Landslides Using an Improved Faster R-CNN Model Based on InSAR Deformation Rates

by Chenglong Zhang, Jingxiang Luo and Zhenhong Li

Remote Sens. 2025, 17(18), 3243; https://doi.org/10.3390/rs17183243 - 19 Sep 2025

Viewed by 222

Abstract

Landslides constitute major geohazards that threaten human life, property, and ecological environments; it is imperative to acquire their location information accurately and in a timely manner. Interferometric Synthetic Aperture Radar (InSAR) has been demonstrated to be capable of acquiring subtle surface deformation with [...] Read more.

Landslides constitute major geohazards that threaten human life, property, and ecological environments; it is imperative to acquire their location information accurately and in a timely manner. Interferometric Synthetic Aperture Radar (InSAR) has been demonstrated to be capable of acquiring subtle surface deformation with high precision and is widely applied to wide-area landslide detection. However, after obtaining InSAR deformation rates, visual interpretation is conventionally employed in landslide detection, which is characterized by significant temporal consumption and labor-intensive demands. Despite advancements that have been made through cluster analysis, hotspot analysis, and deep learning, persistent challenges such as low intelligence levels and weak generalization capabilities remain unresolved. In this study, we propose an improved Faster R-CNN model to achieve automatic detection of slow-moving landslides based on InSAR Line of Sight (LOS) annual rates in the upper and middle reaches of the Jinsha River Basin. The model incorporates a ResNet-34 backbone network, Feature Pyramid Network (FPN), and Convolutional Block Attention Module (CBAM) to effectively extract multi-scale features and enhance focus on subtle surface deformation regions. This model achieved test set performance metrics of 93.56% precision, 97.15% recall, and 93.6% F1-score. The proposed model demonstrates robust detection performance for slow-moving landslides, and through comparative analysis with the detection results of hotspot analysis and K-means clustering, it is verified that this method has strong generalization ability in the representative landslide-prone areas of the Qinghai–Tibet Plateau. This approach can support dynamic updates of regional slow-moving landslide inventories, providing crucial technical support for the detection of landslides. Full article

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18 pages, 4761 KB

Open AccessArticle

Submesoscale Eddies Identified by SWOT and Their Comparison with Mesoscale Eddies in the Tropical Western Pacific

by Lunyi Cao, Yongchui Zhang, Yang Wang, Mei Hong, Yongliang Wei, Chunhua Qiu and Xingyue Xia

Remote Sens. 2025, 17(18), 3242; https://doi.org/10.3390/rs17183242 - 19 Sep 2025

Viewed by 179

Abstract

Conventional altimeter satellites, such as TOPEX/Poseidon and Jason series, can identify ocean mesoscale eddies (MEs) but cannot effectively distinguish submesoscale eddies (SMEs) due to horizontal resolution limitations. The emergence of the Surface Water and Ocean Topography (SWOT) satellite has enabled the resolution (or [...] Read more.

Conventional altimeter satellites, such as TOPEX/Poseidon and Jason series, can identify ocean mesoscale eddies (MEs) but cannot effectively distinguish submesoscale eddies (SMEs) due to horizontal resolution limitations. The emergence of the Surface Water and Ocean Topography (SWOT) satellite has enabled the resolution (or detection) of SMEs. At present, Data Unification and Altimeter Combination System (DUACS) (MEs-resolving) and SWOT (SMEs-resolving) satellites operate concurrently in orbit, however a systematic comparison and analysis of their observational outputs has yet to be conducted. Using a closed-contour scalar analysis method, this study identifies SMEs in the tropical western Pacific Ocean and compares the results with those from the dataset. The latitude-dependent Rossby deformation radius is employed to differentiate MEs from SMEs. For MEs, SWOT detects 176 per 10.5-day sub-cycle, while DUACS detects 162, which are roughly equivalent. For SMEs, SWOT identifies 273 per sub-cycle, far exceeding the 13 detected by DUACS. For amplitudes, DUACS measures 5.22 cm and 3.67 cm for MEs and SMEs, respectively, while the values reported by the SWOT satellite are 6.13 cm and 4.49 cm. In both datasets, cyclonic eddies are more prevalent in all cases except for the SMEs detected by SWOT, where anticyclonic eddies slightly outnumber cyclonic eddies. Additionally, during the trial operation and scientific orbit phases, SWOT is able to resolve 29 SMEs per orbit. The results indicate that high-resolution data can distinguish phenomena that conventional satellite altimeters cannot capture, providing valuable references for the analysis and application of SME characteristics. Full article

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17 pages, 9616 KB

Open AccessArticle

Reflections: Spectral Investigation of Black Band Disease in Hawaiian Corals

by Mia B. Melamed, Roberta E. Martin, McKenna Allen and Gregory P. Asner

Remote Sens. 2025, 17(18), 3241; https://doi.org/10.3390/rs17183241 - 19 Sep 2025

Viewed by 293

Abstract

Coral reefs are essential to the cultural, ecological, and economic well-being of Hawai‘i’s communities, yet they face increasing threats from environmental changes and localized stressors, including coral disease. Detecting coral disease often relies on the visible appearance of lesions; however, in the case [...] Read more.

Coral reefs are essential to the cultural, ecological, and economic well-being of Hawai‘i’s communities, yet they face increasing threats from environmental changes and localized stressors, including coral disease. Detecting coral disease often relies on the visible appearance of lesions; however, in the case of black-band disease (BBD), this visual cue appears too late, as disease progression can cause an average rate of tissue loss of up to 5.7 cm² per day over two months, followed by partial or full colony mortality. Reflectance spectroscopy offers a promising tool for detecting subtle spectral changes associated with coral health before visible symptoms emerge, yet few studies have applied this method to coral disease. In situ spectroscopy was used to measure the spectral reflectance of health conditions in Montiporid corals at ‘Anini Reef, Kaua‘i, USA. Discriminant analysis revealed that visually identical tissue types—live tissue on colonies with BBD (live_D) and live tissue on colonies without BBD (live_L)—were spectrally distinct. In contrast, BBD lesions (disease) and adjacent tissue that appeared healthy (transition) exhibited similar spectral signatures. Analyses identified three spectrally distinct tissue health conditions with a misclassification rate of 12.8%. These findings highlight the potential of reflectance spectroscopy for early coral disease detection, which could improve response times and support more effective coral reef conservation efforts. Full article

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25 pages, 11727 KB

Open AccessArticle

An Interpretable Ensemble Learning Framework Based on Remote Sensing for Ecological–Geological Environment Evaluation: The Case of Laos

by Zhengyao Wang, Yunhui Kong, Keyan Xiao, Changjie Cao, Yunhe Li, Yixiao Wu, Miao Xie, Rui Tang, Cheng Li and Chengjie Gong

Remote Sens. 2025, 17(18), 3240; https://doi.org/10.3390/rs17183240 - 19 Sep 2025

Viewed by 321

Abstract

As a critical ecological security barrier in the Indo-China Peninsula, the Lao People’s Democratic Republic (Lao PDR) is increasingly threatened by forest degradation, frequent geological hazards, and intensified anthropogenic disturbances. To address the urgent need for a scientific evaluation of eco-geological environmental quality, [...] Read more.

As a critical ecological security barrier in the Indo-China Peninsula, the Lao People’s Democratic Republic (Lao PDR) is increasingly threatened by forest degradation, frequent geological hazards, and intensified anthropogenic disturbances. To address the urgent need for a scientific evaluation of eco-geological environmental quality, this study develops a comprehensive assessment framework integrating multi-source remote sensing imagery, geological maps, and socio-economic datasets. A total of ten indicators were selected across four dimensions—geology, topography, ecology, and human activity. A stacking ensemble learning model was constructed by combining seven heterogeneous base classifiers—AdaBoost, KNN, Gradient Boosting, Random Forest, SVC, MLP, and XGBoost—with a logistic regression meta-learner. Model interpretability was enhanced using SHAP values to quantify the contribution of each input variable. The stacking model outperformed all individual models, achieving an accuracy of 91.14%, an F1 score of 93.62%, and an AUC of 95.05%. NDVI, GDP, and slope were identified as the most influential factors: vegetation coverage showed a strong positive relationship with environmental quality, while economic development intensity and steep terrain were associated with degradation. Spatial zoning results indicate that high-quality eco-geological zones are concentrated in the low-disturbance plains of the northeast and southeast, whereas vulnerable areas are primarily distributed around the Vientiane metropolitan region and tectonically active mountainous zones. This study offers a robust and interpretable methodological approach to support ecological diagnosis, zonal management, and sustainable development in tropical mountainous regions. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Condition Assessment and Degradation Drivers Monitoring)

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23 pages, 5234 KB

Open AccessArticle

Instance Segmentation of LiDAR Point Clouds with Local Perception and Channel Similarity

by Xinmiao Du and Xihong Wu

Remote Sens. 2025, 17(18), 3239; https://doi.org/10.3390/rs17183239 - 19 Sep 2025

Viewed by 309

Abstract

Lidar point clouds are crucial for autonomous driving, but their sparsity and scale variations pose challenges for instance segmentation. In this paper, we propose LCPSNet, a Light Detection and Ranging (LiDAR) channel-aware point segmentation network designed to handle distance-dependent sparsity and scale variation [...] Read more.

Lidar point clouds are crucial for autonomous driving, but their sparsity and scale variations pose challenges for instance segmentation. In this paper, we propose LCPSNet, a Light Detection and Ranging (LiDAR) channel-aware point segmentation network designed to handle distance-dependent sparsity and scale variation in point clouds. A top-down FPN is adopted, where high-level features are progressively upsampled and fused with shallow layers. The fused features at 1/16, 1/8, and 1/4 are further aligned to a common BEV/polar grid and processed by the Local Perception Module (LPM), which applies cross-scale, position-dependent weighting to enhance intra-object coherence and suppress interference. The Inter-Channel Correlation Module (ICCM) employs ball queries to model spatial and channel correlations, computing an inter-channel similarity matrix to reduce redundancy and highlight valid features. Experiments on SemanticKITTI and Waymo show that LPM and ICCM effectively improve local feature refinement and global semantic consistency. LCPSNet achieves 70.9 PQ and 77.1 mIoU on SemanticKITTI, surpassing mainstream methods and reaching state-of-the-art performance. Full article

(This article belongs to the Section AI Remote Sensing)

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22 pages, 7050 KB

Open AccessArticle

Emission Control and Sensitivity Regime Shifts Drive the Decline in Extreme Ozone Concentration in the Sichuan Basin During 2015–2024

by Hanqing Kang, Bojun Liu, Lei Hong, Jingchuan Shi, Hua Lu, Ying Zhang and Zhaobing Guo

Remote Sens. 2025, 17(18), 3238; https://doi.org/10.3390/rs17183238 - 19 Sep 2025

Viewed by 324

Abstract

In recent years, ozone (O₃) pollution has become a prominent air quality concern in the Sichuan Basin (SCB). Based on surface O₃ measurements from 22 cities between 2015 and 2024, this study investigates the evolution of extreme O₃ pollution [...] Read more.

In recent years, ozone (O₃) pollution has become a prominent air quality concern in the Sichuan Basin (SCB). Based on surface O₃ measurements from 22 cities between 2015 and 2024, this study investigates the evolution of extreme O₃ pollution events and their underlying causes. While the average O₃ concentration, the number of affected cities, and the total O₃ pollution hours have all increased during the past decade, extreme O₃ concentrations have shown a significant decline since 2020. These trends suggest that O₃ pollution in the SCB has become more spatially extensive and less intense. Decomposition analysis attributed ~75% of the post-2020 decline in extreme O₃ concentrations to precursor emission reductions, with meteorological variability explaining the remaining ~25%. Satellite observations of formaldehyde (HCHO) and nitrogen dioxide (NO₂) column densities indicate a regional shift in O₃ formation regimes across the SCB, with many areas transitioning from VOC (volatile organic compound)-limited to transitional or NO_x (nitrogen oxide)-limited conditions. This shift likely contributed to the broader spatial extent and longer duration of O₃ pollution in recent years. Model sensitivity simulations and Integrated Reaction Rate (IRR) analysis demonstrate that reductions in precursor emissions, particularly NO_x, directly weakened daytime photochemical O₃ production and disrupted NO_x-driven radical propagation under transition and NO_x-limited conditions, collectively driving the observed decline in extreme O₃ concentrations. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Observation of Aerosol Properties and Assessment of Their Effects)

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18 pages, 4722 KB

Open AccessArticle

Improving Finite Element Optimization of InSAR-Derived Deformation Source Using Integrated Multiscale Approach

by Andrea Barone, Pietro Tizzani, Antonio Pepe, Maurizio Fedi and Raffaele Castaldo

Remote Sens. 2025, 17(18), 3237; https://doi.org/10.3390/rs17183237 - 19 Sep 2025

Viewed by 276

Abstract

Parametric optimization/inversion of Interferometric Synthetic Aperture Radar (InSAR) measurements enables the modeling of the volcanic deformation source by considering the approximation of the analytic formulations or by defining refined scenarios within a Finite Element (FE) framework. However, the geodetic data modeling can lead [...] Read more.

Parametric optimization/inversion of Interferometric Synthetic Aperture Radar (InSAR) measurements enables the modeling of the volcanic deformation source by considering the approximation of the analytic formulations or by defining refined scenarios within a Finite Element (FE) framework. However, the geodetic data modeling can lead to ambiguous solutions when constraints are unavailable, turning out to be time-consuming. In this work, we use an integrated multiscale approach for retrieving the geometric parameters of volcanic deformation sources and then constraining a Monte Carlo optimization of FE parametric modeling. This approach allows for contemplating more physically complex scenarios and more robust statistical solutions, and significantly decreasing computing time. We propose the Campi Flegrei caldera (CFc) case study, considering the 2019–2022 uplift phenomenon observed using Sentinel-1 satellite images. The workflow firstly consists of applying the Multiridge and ScalFun methods, and Total Horizontal Derivative (THD) technique to determine the position and horizontal sizes of the deformation source. We then perform two independent cycles of parametric FE optimization by keeping (I) all the parameters unconstrained and (II) constraining the source geometric parameters. The results show that the innovative application of the integrated multiscale approach improves the performance of the FE parametric optimization in proposing a reliable interpretation of volcanic deformations, revealing that (II) yields statistically more reliable solutions than (I) in an extraordinary tenfold reduction in computing time. Finally, the retrieved solution at CFc is an oblate-like source at approximately 3 km b.s.l. embedded in a heterogeneous crust. Full article

(This article belongs to the Section Engineering Remote Sensing)

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21 pages, 3753 KB

Open AccessArticle

Lidar-Based Detection and Analysis of Serendipitous Collisions in Shared Indoor Spaces

by Addison H. Flack, Thomas J. Pingel, Timothy D. Baird, Shashank Karki and Nicole Abaid

Remote Sens. 2025, 17(18), 3236; https://doi.org/10.3390/rs17183236 - 18 Sep 2025

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Abstract

Indoor environments significantly influence human interaction, collaboration, and well-being, yet evaluating how architectural designs actually perform in fostering social connections remains challenging. This study demonstrates the use of 11 static-mounted lidar sensors to detect serendipitous encounters—collisions—between people in a shared common space of [...] Read more.

Indoor environments significantly influence human interaction, collaboration, and well-being, yet evaluating how architectural designs actually perform in fostering social connections remains challenging. This study demonstrates the use of 11 static-mounted lidar sensors to detect serendipitous encounters—collisions—between people in a shared common space of a mixed academic–residential university building. A novel collision detection algorithm achieved 86.1% precision and detected 14,022 interactions over 115 days (67 million person-seconds) of an academic semester. While occupancy strongly predicted collision frequency overall (R² ≥ 0.74), significant spatiotemporal variations revealed the complex relationship between co-presence and social interaction. Key findings include the following: (1) collision frequency peaked early in the semester then declined by ~25% by mid-semester; (2) temporal lags between occupancy and collision peaks of 2–3 h in the afternoon indicate that social interaction differs from physical presence; (3) collisions per occupancy peaked on the weekend, with Saturday showing 52% higher rates than the weekly average; and (4) collisions clustered at key transition zones (elevator areas, stair bases), with an additional “friction effect”, where proximity to seating increased interaction rates (>30%) compared to open corridors. This methodology establishes a scalable framework for post-occupancy evaluation, enabling evidence-based assessment of design effectiveness in fostering the spontaneous interactions essential for creativity, innovation, and place-making in built environments. Full article

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27 pages, 23612 KB

Open AccessArticle

Assessment of Long-Term Photovoltaic (PV) Power Potential in China Based on High-Quality Solar Radiation and Optimal Tilt Angles of PV Panels

by Wenbo Zhao, Xiaotong Zhang, Shuyue Yang, Yanjun Duan, Lingfeng Lu, Xinpei Han, Lingchen Bu, Run Jia and Yunjun Yao

Remote Sens. 2025, 17(18), 3235; https://doi.org/10.3390/rs17183235 - 18 Sep 2025

Viewed by 274

Abstract

Solar photovoltaic (PV) plays a crucial role in China’s pursuit of carbon neutrality. Assessing the PV power potential over China is essential for future energy planning and policy making. Surface solar radiation and panel tilt angle are critical factors influencing PV power generation. [...] Read more.

Solar photovoltaic (PV) plays a crucial role in China’s pursuit of carbon neutrality. Assessing the PV power potential over China is essential for future energy planning and policy making. Surface solar radiation and panel tilt angle are critical factors influencing PV power generation. However, existing solar radiation datasets cannot fully meet assessment needs due to insufficient temporal coverage and limited accuracy, and the impact of panel tilt angles on PV potential is largely overlooked. This study developed a PV power estimation framework to assess the long-term (1980–2019) PV power potential at 609 stations across China, based on reconstructed high-quality solar radiation and optimized tilt angles. The validation of PV power estimates using ground measured outputs from four operational PV power stations indicated a correlation coefficient of 0.67 and a root mean square error of 0.07 for estimated daily capacity factor (CF). The assessment results revealed that the multi-year mean CF of China is 0.149 ± 0.031, with higher potentials in northern provinces and lower in southern provinces. The mean annual CF shows a declining trend of −7 × 10⁻⁴ per decade during 1980–2019, with significant decreases primarily in heavily polluted regions. In addition, we propose an optimal tilt angle estimation model based on diffuse fraction, achieving higher accuracy than previously released models. The estimated optimal tilt angle results in an increase in PV energy yield by 14.9 TWh/year for China compared with latitude-based schemes, based on China’s cumulative PV capacity by 2023 (609 GW). Our findings provide valuable insights for the effective implementation of solar PV projects in China. Full article

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38 pages, 10032 KB

Open AccessArticle

Closed and Structural Optimization for 3D Line Segment Extraction in Building Point Clouds

by Ruoming Zhai, Xianquan Han, Peng Wan, Jianzhou Li, Yifeng He and Bangning Ding

Remote Sens. 2025, 17(18), 3234; https://doi.org/10.3390/rs17183234 - 18 Sep 2025

Viewed by 235

Abstract

The extraction of architectural structural line features can simplify the 3D spatial representation of built environments, reduce the storage and processing burden of large-scale point clouds, and provide essential geometric primitives for downstream modeling tasks. However, existing 3D line extraction methods suffer from [...] Read more.

The extraction of architectural structural line features can simplify the 3D spatial representation of built environments, reduce the storage and processing burden of large-scale point clouds, and provide essential geometric primitives for downstream modeling tasks. However, existing 3D line extraction methods suffer from incomplete and fragmented contours, with missing or misaligned intersections. To overcome these limitations, this study proposes a patch-level framework for 3D line extraction and structural optimization from building point clouds. The proposed method first partitions point clouds into planar patches and establishes local image planes for each patch, enabling a structured 2D representation of unstructured 3D data. Then, graph-cut segmentation is proposed to extract compact boundary contours, which are vectorized into closed lines and back-projected into 3D space to form the initial line segments. To improve geometric consistency, regularized geometric constraints, including adjacency, collinearity, and orthogonality constraints, are further designed to merge homogeneous segments, refine topology, and strengthen structural outlines. Finally, we evaluated the approach on three indoor building environments and four outdoor scenes, and experimental results show that it reduces noise and redundancy while significantly improving the completeness, closure, and alignment of 3D line features in various complex architectural structures. Full article

(This article belongs to the Special Issue Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud)

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28 pages, 9748 KB

Open AccessArticle

ER-PASS: Experience Replay with Performance-Aware Submodular Sampling for Domain-Incremental Learning in Remote Sensing

by Yeseok Lee, Donghyeon Lee, Taehong Kwak and Yongil Kim

Remote Sens. 2025, 17(18), 3233; https://doi.org/10.3390/rs17183233 - 18 Sep 2025

Viewed by 211

Abstract

In recent years, deep learning has become a dominant research trend in the field of remote sensing. However, due to significant domain discrepancies among datasets collected from various platforms, models trained on a single domain often struggle to generalize to other domains. In [...] Read more.

In recent years, deep learning has become a dominant research trend in the field of remote sensing. However, due to significant domain discrepancies among datasets collected from various platforms, models trained on a single domain often struggle to generalize to other domains. In domain-incremental learning scenarios, such discrepancies often lead to catastrophic forgetting, hindering the practical deployment of deep learning models. To address this, we propose ER-PASS, an experience replay-based continual learning algorithm that incorporates a performance-aware submodular sampling strategy. ER-PASS balances adaptability across domains and retention of knowledge by combining the strengths of joint learning and experience replay, while maintaining practical efficiency in terms of training time and memory usage. We validated our method on two remote sensing applications—building segmentation and land use/land cover (LULC) classification—using UNet and DeepLabV3+. Experimental results show that ER-PASS consistently outperforms existing continual learning methods in average incremental accuracy (AIA) and backward transfer (BWT), ensuring generalization across domains and mitigating catastrophic forgetting. While these results were obtained under restricted conditions, limited to a sequence of domains from high to low resolution and two applications, they underscore the potential of ER-PASS as a practical and general-purpose solution for continual learning in remote sensing. Full article

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