Remote Sensing

21 pages, 4544 KB

Open AccessArticle

Small Ship Detection Based on a Learning Model That Incorporates Spatial Attention Mechanism as a Loss Function in SU-ESRGAN

by Kohei Arai, Yu Morita and Hiroshi Okumura

Remote Sens. 2026, 18(3), 417; https://doi.org/10.3390/rs18030417 - 27 Jan 2026

Abstract

Ship monitoring using Synthetic Aperture Radar (SAR) data faces significant challenges in detecting small vessels due to low spatial resolution and speckle noise. While ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) has shown promise for image super-resolution, it struggles with SAR imagery characteristics. This [...] Read more.

Ship monitoring using Synthetic Aperture Radar (SAR) data faces significant challenges in detecting small vessels due to low spatial resolution and speckle noise. While ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) has shown promise for image super-resolution, it struggles with SAR imagery characteristics. This study proposes SA/SU-ESRGAN, which extends the SU-ESRGAN framework by incorporating a spatial attention mechanism loss function. SU-ESRGAN introduced semantic structural loss to accurately preserve ship shapes and contours; our enhancement adds spatial attention to focus reconstruction efforts on ship regions while suppressing background noise. Experimental results demonstrate that SA/SU-ESRGAN successfully detects small vessels that remain undetectable by SU-ESRGAN, achieving improved detection capabilities with a PSNR of approximately 26 dB (SSIM is around 0.5) and enhanced visual clarity in ship boundaries. The spatial attention mechanism effectively reduces noise influence, producing clearer super-resolution results suitable for maritime surveillance applications. Based on the HRSID dataset, a representative dataset for evaluating ship detection performance using SAR data, we evaluated ship detection performance using images in which the spatial resolution of the SAR data was artificially degraded using a smoothing filter. We found that with a 4 × 4 filter, all eight ships were detected without any problems, but with an 8 × 8 filter, only three of the eight ships were detected. When super-resolution was applied to this, six ships were detected. Full article

(This article belongs to the Special Issue Applications of SAR for Environment Observation Analysis)

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26 pages, 8779 KB

Open AccessArticle

TAUT: A Remote Sensing-Based Terrain-Adaptive U-Net Transformer for High-Resolution Spatiotemporal Downscaling of Temperature over Southwest China

by Zezhi Cheng, Jiping Guan, Li Xiang, Jingnan Wang and Jie Xiang

Remote Sens. 2026, 18(3), 416; https://doi.org/10.3390/rs18030416 - 27 Jan 2026

Abstract

High-precision temperature prediction is crucial for dealing with extreme weather events under the background of global warming. However, due to the limitations of computing resources, numerical weather prediction models are difficult to directly provide high spatio-temporal resolution data that meets the specific application [...] Read more.

High-precision temperature prediction is crucial for dealing with extreme weather events under the background of global warming. However, due to the limitations of computing resources, numerical weather prediction models are difficult to directly provide high spatio-temporal resolution data that meets the specific application requirements of a certain region. This problem is particularly prominent in areas with complex terrain. The use of remote sensing data, especially high-resolution terrain data, provides key information for understanding and simulating the interaction between land and atmosphere in complex terrain, making the integration of remote sensing and NWP outputs to achieve high-precision meteorological element downscaling a core challenge. Aiming at the challenge of temperature scaling in complex terrain areas of Southwest China, this paper proposes a novel deep learning model—Terrain Adaptive U-Net Transformer (TAUT). This model takes the encoder–decoder structure of U-Net as the skeleton, deeply integrates the global attention mechanism of Swin Transformer and the local spatiotemporal feature extraction ability of three-dimensional convolution, and innovatively introduces the multi-branch terrain adaptive module (MBTA). The adaptive integration of terrain remote sensing data with various meteorological data, such as temperature fields and wind fields, has been achieved. Eventually, in the complex terrain area of Southwest China, a spatio-temporal high-resolution downscaling of 2 m temperature was realized (from 0.1° in space to 0.01°, and from 3 h intervals to 1 h intervals in time). The experimental results show that within the 48 h downscaling window period, the TAUT model outperforms the comparison models such as bilinear interpolation, SRCNN, U-Net, and EDVR in all evaluation metrics (MAE, RMSE, COR, ACC, PSNR, SSIM). The systematic ablation experiment verified the independent contributions and synergistic effects of the Swin Transformer module, the 3D convolution module, and the MBTA module in improving the performance of each model. In addition, the regional terrain verification shows that this model demonstrates good adaptability and stability under different terrain types (mountains, plateaus, basins). Especially in cases of high-temperature extreme weather, it can more precisely restore the temperature distribution details and spatial textures affected by the terrain, verifying the significant impact of terrain remote sensing data on the accuracy of temperature downscaling. The core contribution of this study lies in the successful construction of a hybrid architecture that can jointly leverage the local feature extraction advantages of CNN and the global context modeling capabilities of Transformer, and effectively integrate key terrain remote sensing data through dedicated modules. The TAUT model offers an effective deep learning solution for precise temperature prediction in complex terrain areas and also provides a referential framework for the integration of remote sensing data and numerical model data in deep learning models. Full article

(This article belongs to the Special Issue Application of Remote Sensing Data in Data Assimilation, Reanalysis and Artificial Intelligence for Mesoscale Numerical Weather Models (Second Edition))

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21 pages, 9165 KB

Open AccessArticle

MSMC: Multi-Scale Embedding and Meta-Contrastive Learning for Few-Shot Fine-Grained SAR Target Classification

by Bowen Chen, Minjia Yang, Yue Wang and Xueru Bai

Remote Sens. 2026, 18(3), 415; https://doi.org/10.3390/rs18030415 - 26 Jan 2026

Abstract

Constrained by observation conditions and high inter-class similarity, effective feature extraction and classification of synthetic aperture radar (SAR) targets in few-shot scenarios remains a persistent challenge. To address this issue, this article proposes a few-shot fine-grained SAR target classification method based on multi-scale [...] Read more.

Constrained by observation conditions and high inter-class similarity, effective feature extraction and classification of synthetic aperture radar (SAR) targets in few-shot scenarios remains a persistent challenge. To address this issue, this article proposes a few-shot fine-grained SAR target classification method based on multi-scale embedding network and meta-contrastive learning (MSMC). Specifically, the MSMC integrates two complementary training pipelines; the first employs metric-based meta-learning to facilitate few-shot classification, while the second adopts an auxiliary training strategy to enhance feature diversity through contrastive learning. Furthermore, a shared multi-scale embedding network (MSEN) is designed to extract discriminative multi-scale features via adaptive candidate region generation and joint multi-scale embedding. The experimental results on the MSTAR dataset demonstrate that the proposed method achieves superior few-shot fine-grained classification performance compared to existing methods. Full article

(This article belongs to the Section AI Remote Sensing)

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23 pages, 1460 KB

Open AccessArticle

Integrating Strong Ground Motion Simulation with Nighttime Light Remote Sensing for Seismic Damage Assessment in the 2025 Dingri Mw7.1 Earthquake

by Wenyue Wang, Ke Sun and Fang Ouyang

Remote Sens. 2026, 18(3), 414; https://doi.org/10.3390/rs18030414 - 26 Jan 2026

Abstract

On 7 January 2025, an Mw7.1 earthquake struck Dingri County, Tibet, causing severe damage in a high-altitude, sparsely instrumented region where traditional damage assessment methods are limited. To address this, we developed an integrated "source simulation–nighttime light validation" framework. First, a kinematic source [...] Read more.

On 7 January 2025, an Mw7.1 earthquake struck Dingri County, Tibet, causing severe damage in a high-altitude, sparsely instrumented region where traditional damage assessment methods are limited. To address this, we developed an integrated "source simulation–nighttime light validation" framework. First, a kinematic source model (constrained by InSAR and teleseismic data) and the Unified Seismic Tomography models for continental China lithosphere 2.0 (USTClitho2.0) velocity model were used with the curved-grid finite difference method to simulate high-resolution ground motion and intensity fields. Second, NASA Black Marble (VNP46A2) nighttime light data, processed with the Block-Matching and 3D filtering (BM3D) algorithm, were analyzed to compute pixel-level radiance changes and township-level total nighttime light loss rates (TNLR). The results reveal a high spatial consistency between simulated high-intensity zones and areas of significant light loss. For instance, Mangpu Township, within a simulated high-intensity zone, exhibited a TNLR of 44.7%. This demonstrates that nighttime light remote sensing can effectively validate physical simulations in areas lacking dense seismic networks. Our framework provides a novel, complementary methodology for rapid and reliable post-earthquake damage assessment in high-mountain, data-sparse regions. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Natural Hazards and Sustainable Development)

27 pages, 3922 KB

Open AccessArticle

Hierarchical Multiscale Fusion with Coordinate Attention for Lithologic Mapping from Remote Sensing

by Fuyuan Xie and Yongguo Yang

Remote Sens. 2026, 18(3), 413; https://doi.org/10.3390/rs18030413 - 26 Jan 2026

Abstract

Accurate lithologic maps derived from satellite imagery underpin structural interpretation, mineral exploration, and geohazard assessment. However, automated mapping in complex terranes remains challenging because spectrally similar units, narrow anisotropic bodies, and ambiguous contacts can degrade boundary fidelity. In this study, we propose SegNeXt-HFCA, [...] Read more.

Accurate lithologic maps derived from satellite imagery underpin structural interpretation, mineral exploration, and geohazard assessment. However, automated mapping in complex terranes remains challenging because spectrally similar units, narrow anisotropic bodies, and ambiguous contacts can degrade boundary fidelity. In this study, we propose SegNeXt-HFCA, a hierarchical multiscale fusion network with coordinate attention for lithologic segmentation from a Sentinel-2/DEM feature stack. The model builds on SegNeXt and introduces a hierarchical multiscale encoder with coordinate attention to jointly capture fine textures and scene-level structure. It further adopts a class-frequency-aware hybrid loss that combines boundary-weighted online hard-example mining cross-entropy with Lovász-Softmax to better handle long-tailed classes and ambiguous contacts. In addition, we employ a robust training and inference scheme, including entropy-guided patch sampling, exponential moving average of parameters, test-time augmentation, and a DenseCRF-based post-refinement. Two study areas in the Beishan orogen, northwestern China (Huitongshan and Xingxingxia), are used to evaluate the method with a unified 10-channel Sentinel-2/DEM feature stack. Compared with U-NetFormer, PSPNet, DeepLabV3+, DANet, LGMSFNet, SegFormer, BiSeNetV2, and the SegNeXt backbone, SegNeXt-HFCA improves mean intersection-over-union (mIoU) by about 3.8% in Huitongshan and 2.6% in Xingxingxia, respectively, and increases mean pixel accuracy by approximately 3–4%. Qualitative analyses show that the proposed framework better preserves thin-unit continuity, clarifies lithologic contacts, and reduces salt-and-pepper noise, yielding geologically more plausible maps. These results demonstrate that hierarchical multiscale fusion with coordinate attention, together with class- and boundary-aware optimization, provides a practical route to robust lithologic mapping in structurally complex regions. Full article

(This article belongs to the Section Remote Sensing for Geospatial Science)

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24 pages, 7306 KB

Open AccessArticle

Drone-Based Maritime Anomaly Detection with YOLO and Motion/Appearance Fusion

by Nutchanon Suvittawat, De Wen Soh and Sutthiphong Srigrarom

Remote Sens. 2026, 18(3), 412; https://doi.org/10.3390/rs18030412 - 26 Jan 2026

Abstract

Maritime surveillance is critical for ensuring the safety and continuity of sea logistics, port operations, and coastal activities in the presence of anomalies such as unlawful maritime activities, security-related incidents, and anomalous events (e.g., tsunamis or aggressive marine wildlife). Recent advances in unmanned [...] Read more.

Maritime surveillance is critical for ensuring the safety and continuity of sea logistics, port operations, and coastal activities in the presence of anomalies such as unlawful maritime activities, security-related incidents, and anomalous events (e.g., tsunamis or aggressive marine wildlife). Recent advances in unmanned aerial vehicles (UAVs)/drones and computer vision enable automated, wide-area monitoring that can reduce dependence on continuous human observation and mitigate the limitations of traditional methods in complex maritime environments (e.g., waves, ship clutter, and marine animal movement). This study proposes a hybrid anomaly detection and tracking pipeline that integrates YOLOv12, as the primary object detector, with two auxiliary modules: (i) motion assistance for tracking moving anomalies and (ii) stillness (appearance) assistance for tracking slow-moving or stationary anomalies. The system is trained and evaluated on a custom maritime dataset captured using a DJI Mini 2 drone operating around a port area near Bayshore MRT Station (TE29), Singapore. Windsurfers are used as proxy (dummy) anomalies because real anomaly footage is restricted for security reasons. On the held-out test set, the trained model achieves over 90% on Precision, Recall, and mAP50 across all classes. When deployed on real maritime video sequences, the pipeline attains a mean Precision of 92.89% (SD 13.31), a mean Recall of 90.44% (SD 15.24), and a mean Accuracy of 98.50% (SD 2.00%), indicating strong potential for real-world maritime anomaly detection. This proof of concept provides a basis for future deployment and retraining on genuine anomaly footage obtained from relevant authorities to further enhance operational readiness for maritime and coastal security. Full article

(This article belongs to the Special Issue Multi-Modal and Multi-Task Learning in Photogrammetry and Remote Sensing)

24 pages, 9506 KB

Open AccessArticle

An SBAS-InSAR Analysis and Assessment of Landslide Deformation in the Loess Plateau, China

by Yan Yang, Rongmei Liu, Liang Wu, Tao Wang and Shoutao Jiao

Remote Sens. 2026, 18(3), 411; https://doi.org/10.3390/rs18030411 - 26 Jan 2026

Abstract

This study conducts a landslide deformation assessment in Tianshui, Gansu Province, on the Chinese Loess Plateau, utilizing the Small Baseline Subset InSAR (SBAS-InSAR) method integrated with velocity direction conversion and Z-score clustering. The Chinese Loess Plateau is one of the most landslide-prone regions [...] Read more.

This study conducts a landslide deformation assessment in Tianshui, Gansu Province, on the Chinese Loess Plateau, utilizing the Small Baseline Subset InSAR (SBAS-InSAR) method integrated with velocity direction conversion and Z-score clustering. The Chinese Loess Plateau is one of the most landslide-prone regions in China due to frequent rains, strong topographical gradients and severe soil erosion. By constructing subsets of interferograms, SBAS-InSAR can mitigate the influence of decorrelation to a certain extent, making it a highly effective technique for monitoring regional surface deformation and identifying landslides. To overcome the limitations of the satellite’s one-dimensional Line-of-Sight (LOS) measurements and the challenge of distinguishing true landslide signals from noise, two optimization strategies were implemented. First, LOS velocities were projected onto the local steepest slope direction, assuming translational movement parallel to the slope. Second, a Z-score clustering algorithm was employed to aggregate measurement points with consistent kinematic signatures, enhancing identification robustness, with a slight trade-off in spatial completeness. Based on 205 Sentinel-1 Single-Look Complex (SLC) images acquired from 2014 to 2024, the integrated workflow identified 69 “active, very slow” and 63 “active, extremely slow” landslides. These results were validated through high-resolution historical optical imagery. Time series analysis reveals that creep deformation in this region is highly sensitive to seasonal rainfall patterns. This study demonstrates that the SBAS-InSAR post-processing framework provides a cost-effective, millimeter-scale solution for updating landslide inventories and supporting regional risk management and early warning systems in loess-covered terrains, with the exception of densely forested areas. Full article

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24 pages, 7626 KB

Open AccessArticle

Detection of Pine Wilt Disease Using an Explainable Recognition Model Based on Fusion of Vegetation Indices and Texture Features from UAV Multispectral Imagery

by Hao Shi, Ruirui Zhang, Meixiang Chen, Huixiang Liu and Liping Chen

Remote Sens. 2026, 18(3), 410; https://doi.org/10.3390/rs18030410 - 26 Jan 2026

Abstract

Pine Wilt Disease (PWD) is a global destructive forest disease. It poses a serious threat to ecological security and forestry economy, and early detection of PWD is crucial for its prevention and control. Most current studies on identifying infected pine trees based on [...] Read more.

Pine Wilt Disease (PWD) is a global destructive forest disease. It poses a serious threat to ecological security and forestry economy, and early detection of PWD is crucial for its prevention and control. Most current studies on identifying infected pine trees based on multispectral data only rely on Vegetation Indices (VIs). They fail to fully explore the role of Texture Features (TFs) in disease identification. Furthermore, existing models generally lack interpretability. To address these issues, this study proposes a machine learning classification framework integrating VIs and TFs. It also introduces the SHAP algorithm to clarify the contribution of key features to classification decisions. The results show that the method using fused VIs and TFs as input features performs significantly better than using single features. Among the four models evaluated, LGBM achieved the best performance (OA: 0.897, Macro-F1: 0.895), followed by LR (OA: 0.818, Macro-F1: 0.830), RF (OA: 0.790, Macro-F1: 0.786), and SVM (OA: 0.770, Macro-F1: 0.787) when using fused VIs-TFs. SHAP analysis further reveals that VIs such as Vegetation Atmospherically Resistant Index (VARI), Plant Senescence Reflectance Index (PSRI), Difference Vegetation Index (DVI), Anthocyanin Reflectance Index (ARI), and Normalized Difference Red Edge Index (NDRE), as well as TFs like NIR-Mean (NIR-M), play a dominant role in identifying disease stages. Among the VIs, VARI demonstrated the highest contribution, while NIR-M showed the most significant contribution among TFs. Specifically, VIs are more advantageous in distinguishing the pre-visual, early, middle, and late stages. In contrast, TFs contributed more to identifying healthy and dead trees. This study confirms that fusing VIs and TFs can effectively complement the physiological and structural information of pine canopies. Combined with the interpretable LGBM model, it provides a new technical path for the accurate monitoring of PWD. Full article

(This article belongs to the Section Forest Remote Sensing)

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23 pages, 14742 KB

Open AccessArticle

Grapevine Canopy Volume Estimation from UAV Photogrammetric Point Clouds at Different Flight Heights

by Leilson Ferreira, Pedro Marques, Emanuel Peres, Raul Morais, Joaquim J. Sousa and Luís Pádua

Remote Sens. 2026, 18(3), 409; https://doi.org/10.3390/rs18030409 - 26 Jan 2026

Abstract

Vegetation volume is a useful indicator for assessing canopy structure and supporting vineyard management tasks such as foliar applications and canopy management. The photogrammetric processing of imagery acquired using unmanned aerial vehicles (UAVs) enables the generation of dense point clouds suitable for estimating [...] Read more.

Vegetation volume is a useful indicator for assessing canopy structure and supporting vineyard management tasks such as foliar applications and canopy management. The photogrammetric processing of imagery acquired using unmanned aerial vehicles (UAVs) enables the generation of dense point clouds suitable for estimating canopy volume, although point cloud quality depends on spatial resolution, which is influenced by flight height. This study evaluates the effect of three flight heights (30 m, 60 m, and 100 m) on grapevine canopy volume estimation using convex hull, alpha shape, and voxel-based models. UAV-based RGB imagery and field measurements were collected during three periods at different phenological stages in an experimental vineyard. The strongest agreement with field-measured volume occurred at 30 m, where point density was highest. Envelope-based methods showed reduced performance at higher flight heights, while voxel-based grids remained more stable when voxel size was adapted to point density. Estimator behavior also varied with canopy architecture and development. The results indicate appropriate parameter choices for different flight heights and confirm that UAV-based RGB imagery can provide reliable grapevine canopy volume estimates. Full article

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

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24 pages, 34167 KB

Open AccessArticle

A Hybrid Physics–Machine Learning Framework for Landslide Susceptibility Assessment with an Improved Non–Landslide Sampling Strategy

by Dalei Peng, Maoyuan Chen, Yeping Zhou, Pinliang Li, Shihao Xiao, Yuyang Shen, Boren Tan, Linghao Kong and Qiang Xu

Remote Sens. 2026, 18(3), 408; https://doi.org/10.3390/rs18030408 - 26 Jan 2026

Abstract

Rainfall–triggered clustered landslides pose severe risks to communities and infrastructure in mountainous regions. High–precision susceptibility assessment is essential for early warning and hazard mitigation. The traditional buffering method neglects physical slope stability mechanisms, leading to the misclassification of potentially unstable areas. To improve [...] Read more.

Rainfall–triggered clustered landslides pose severe risks to communities and infrastructure in mountainous regions. High–precision susceptibility assessment is essential for early warning and hazard mitigation. The traditional buffering method neglects physical slope stability mechanisms, leading to the misclassification of potentially unstable areas. To improve susceptibility model accuracy, we propose an improved non–landslide sampling strategy that integrates the physical–model TRIGRS (Transient Rainfall Infiltration and Grid–based Regional Slope–Stability Model) with 50 m buffering constraints. A hybrid physics–machine learning framework is used to evaluate the performance of landslide susceptibility assessment across four machine learning models, such as Multi–Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). Among the four models, the TRIGRS model integrated with MLP achieves the highest accuracy in susceptibility mapping. The improved non–landslide sampling strategy increased average Area Under the Curve (AUC) by 16.46% in random cross–validation and improved spatial generalization capability by 29% in spatial cross–validation, demonstrating its robustness in unseen areas. SHAP factor analysis further confirms rainfall, groundwater table, and human activity as the primary influencing factors, which aligns with physical mechanisms and improves model interpretability. Therefore, the proposed non–landslide sampling strategy coupled with the TRIGRS and MLP models outperforms traditional buffering method in evaluating regional landslide susceptibility, providing a more physically basis for geohazard risk assessment. Full article

(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)

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24 pages, 5159 KB

Open AccessArticle

Forest Age Estimation by Integrating Tree Species Identity and Multi-Source Remote Sensing: Validating Heterogeneous Growth Patterns Through the Plant Economic Spectrum Theory

by Xiyu Zhang, Chao Zhang, Li Zhou, Huan Liu, Lianjin Fu and Wenlong Yang

Remote Sens. 2026, 18(3), 407; https://doi.org/10.3390/rs18030407 - 26 Jan 2026

Abstract

Current mainstream remote sensing approaches to forest age estimation frequently neglect interspecific differences in functional traits, which may limit the accurate representation of species-specific tree growth strategies. This study develops and validates a technical framework that incorporates multi-source remote sensing and tree species [...] Read more.

Current mainstream remote sensing approaches to forest age estimation frequently neglect interspecific differences in functional traits, which may limit the accurate representation of species-specific tree growth strategies. This study develops and validates a technical framework that incorporates multi-source remote sensing and tree species functional trait heterogeneity to systematically improve the accuracy of plantation age mapping. We constructed a processing chain—“multi-source feature fusion–species identification–heterogeneity modeling”—for a typical karst plantation landscape in southeastern Yunnan. Using the Google Earth Engine (GEE) platform, we integrated Sentinel-1/2 and Landsat time-series data, implemented a Gradient Boosting Decision Tree (GBDT) algorithm for species classification, and built age estimation models that incorporate species identity as a proxy for the growth strategy heterogeneity delineated by the Plant Economic Spectrum (PES) theory. Key results indicate: (1) Species classification reached an overall accuracy of 89.34% under spatial block cross-validation, establishing a reliable basis for subsequent modeling. (2) The operational model incorporating species information achieved an R² (coefficient of determination) of 0.84 (RMSE (Root Mean Square Error) = 6.52 years) on the test set, demonstrating a substantial improvement over the baseline model that ignored species heterogeneity (R² = 0.62). This demonstrates that species identity serves as an effective proxy for capturing the growth strategy heterogeneity described by the Plant Economic Spectrum (PES) theory, which is both distinguishable and valuable for modeling within the remote sensing feature space. (3) Error propagation analysis demonstrated strong robustness to classification uncertainties (γ = 0.23). (4) Plantation structure in the region was predominantly young-aged, with forests aged 0–20 years covering over 70% of the area. Despite inherent uncertainties in ground-reference age data, the integrated framework exhibited clear relative superiority, improving R² from 0.62 to 0.84. Both error propagation analysis (γ = 0.23) and Monte Carlo simulations affirmed the robustness of the tandem workflow and the stability of the findings, providing a reliable methodology for improved-accuracy plantation carbon sink quantification. Full article

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32 pages, 29618 KB

Open AccessArticle

Combining ALS and Satellite Data to Develop High-Resolution Forest Growth Potential Maps for Plantation Stands in Western Canada

by Faezeh Khalifeh Soltanian, Luiz Henrique Terezan, Colin E. Chisholm, Pamela Dykstra, William H. MacKenzie and Che Elkin

Remote Sens. 2026, 18(3), 406; https://doi.org/10.3390/rs18030406 (registering DOI) - 26 Jan 2026

Abstract

Mapping forest growth potential across varying environments is challenging, especially when field measurements are limited. In this study, we integrated Airborne Laser Scanning (ALS) terrain derivatives and Sentinel-2 spectral indices to model Site Index (SI), using forest plantations, at 10-m spatial resolution across [...] Read more.

Mapping forest growth potential across varying environments is challenging, especially when field measurements are limited. In this study, we integrated Airborne Laser Scanning (ALS) terrain derivatives and Sentinel-2 spectral indices to model Site Index (SI), using forest plantations, at 10-m spatial resolution across three ecologically distinct regions in British Columbia (Aleza Lake, Deception, and Eagle Hills). Random Forest regression models were calibrated using field-measured SI and a multistep variable-selection procedure that included Variance Inflation Factor (VIF) screening followed by model-based variable importance assessment. Model performance was evaluated using repeated 10-fold cross-validation. The combined ALS–Sentinel-2 models substantially outperformed single-source models, yielding cross-validated R² values of 0.63, 0.44, and 0.56 for Aleza Lake, Deception, and Eagle Hills, respectively, compared with R² values of 0.40, 0.40, and 0.46 for ALS-only models. Key predictors consistently included terrain metrics, such as the Topographic Position Index (TPI) and the Topographic Wetness Index (TWI), along with satellite-derived chlorophyll-sensitive indices including S2REP (Sentinel-2 red-edge position), MTCI (MERIS terrestrial chlorophyll), and GNDVI (Greenness Normalized Difference Vegetation Index). A general model using predictors common to all regions performed comparably (R² = 0.63, 0.41, 0.52), demonstrating the transferability and operational potential of the approach. These findings demonstrate that integrating ALS-derived terrain metrics with Sentinel-2 spectral indices provides a robust, age-independent framework for capturing spatial variability in forest productivity across landscapes. This multi-sensor fusion approach enhances traditional SI methods and single-sensor models, providing a scalable and operational tool for forest management and long-term planning in changing environmental conditions. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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26 pages, 12755 KB

Open AccessArticle

Coupling Time-Series Sentinel-2 Imagery with Multi-Scale Landscape Metrics to Decipher Seasonal Waterbird Diversity Patterns

by Jiaxu Fan, Lei Cui, Yi Lian, Peng Du, Yangqianqian Ren, Xunqiang Mo and Zhengwang Zhang

Remote Sens. 2026, 18(3), 405; https://doi.org/10.3390/rs18030405 - 25 Jan 2026

Abstract

Seasonal dynamics in wetland landscapes are closely associated with habitat availability and are likely to influence the spatial organization and diversity of waterbird communities. However, most existing studies rely on static land-cover representations or single spatial scales, limiting our ability to characterize how [...] Read more.

Seasonal dynamics in wetland landscapes are closely associated with habitat availability and are likely to influence the spatial organization and diversity of waterbird communities. However, most existing studies rely on static land-cover representations or single spatial scales, limiting our ability to characterize how waterbirds respond to seasonally shifting habitats across scales. Focusing on the Qilihai Wetland in Tianjin, China, we combined high-frequency waterbird surveys from 2019–2021 with multi-temporal, season-matched Sentinel-2 imagery and the Dynamic World dataset. Partial least squares regression (PLSR) was applied across a continuous spatial gradient (100–3000 m) to quantify scale-dependent statistical associations between landscape composition and configuration derived from satellite-mapped habitat mosaics on different functional groups. Waterbird diversity exhibited pronounced seasonal contrasts. During the breeding and post-fledging period, high-diversity assemblages were stably concentrated within core wetland areas, showing limited spatial variability. In contrast, during the wintering and stopover period, community distributions became increasingly dispersed, with elevated spatial heterogeneity and interannual variability associated with habitat reorganization. The scale of effect shifted systematically between seasons. In the breeding and post-fledging period, both waterfowl and waders responded predominantly to local-scale landscape factors (<800 m), consistent with nesting requirements and microhabitat conditions. During the wintering and stopover period, however, the characteristic response scale of waterfowl expanded to 1500–2000 m, suggesting stronger associations with broader landscape context, whereas waders remained closely linked to local-scale shallow-water and mudflat connectivity (~200 m). Functional traits played a key role in structuring these scale-dependent responses, with diving behavior and tarsus length being associated with strong constraints on habitat use. Overall, our results suggest that waterbird diversity patterns emerge from the interaction between seasonal habitat dynamics, landscape structure, and functional trait filtering, underscoring the need for phenology-informed, multi-scale conservation strategies that move beyond static spatial boundaries. Full article

(This article belongs to the Section Ecological Remote Sensing)

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18 pages, 7389 KB

Open AccessArticle

Enhanced Deep Convolutional Neural Network-Based Multiscale Object Detection Framework for Efficient Water Resource Monitoring Using Remote Sensing Imagery

by Sultan Almutairi, Mashael Maashi, Hadeel Alsolai, Mohammed Burhanur Rehman, Hanadi Alkhudhayr and Asma A. Alhashmi

Remote Sens. 2026, 18(3), 404; https://doi.org/10.3390/rs18030404 - 25 Jan 2026

Abstract

Water resource monitoring can provide beneficial information supporting water management; however, present operational systems are small and provide only a subset of the information needed. Primary advancements consist of the clear explanation of water redistribution and water use from groundwater and river schemes, [...] Read more.

Water resource monitoring can provide beneficial information supporting water management; however, present operational systems are small and provide only a subset of the information needed. Primary advancements consist of the clear explanation of water redistribution and water use from groundwater and river schemes, achieving better spatial detail and increased precision as evaluated against hydrometric observation. In such cases, Earth Observation (EO) satellite systems are persistently creating extensive data, which is now essential for applications in different fields. With readily available open-source satellite imagery, aerial remote sensing is progressively becoming a quick and efficient tool for monitoring land and water resource development actions, demonstrating time and cost savings. At present, the deep learning (DL) model will be beneficial for monitoring water resources and EO utilizing remote sensing. In this paper, a Deep Neural Network-Based Object Detection for Water Resource Monitoring and Earth Observation (DNNOD-WRMEO) model is introduced. The main intention is to develop an effective monitoring and analysis framework for water resources and Earth surface observations using aerial remote sensing images. Initially, the Wiener filter (WF) model was used for image pre-processing. For object detection, the Yolov12 method was used for identifying, locating, and classifying objects within an image, followed by the DNNOD-WRMEO methodology, which implements the ResNet-CapsNet model for the backbone feature extraction method. Finally, the temporal convolutional network (TCN) model was implemented for the classification of water resources. The comparison analysis of the DNNOD-WRMEO methodology exhibited a superior accuracy value of 98.61% compared with existing models under the AIWR dataset. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)

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22 pages, 25659 KB

Open AccessArticle

High-Resolution Imaging of Multi-Beam Uniform Linear Array Sonar Based on Two-Stage Sparse Deconvolution Method

by Jian Wang, Junhong Cui, Ruo Li, Haisen Li and Jing Wang

Remote Sens. 2026, 18(3), 403; https://doi.org/10.3390/rs18030403 - 25 Jan 2026

Abstract

Classical beamforming (CBF) beamforming constrains the accuracy and quality of underwater acoustic imaging by producing wide main-lobes that reduce resolution, high sidelobes that cause leakage, and point-spread functions that blur targets. Existing approaches typically address only one of these issues at a time, [...] Read more.

Classical beamforming (CBF) beamforming constrains the accuracy and quality of underwater acoustic imaging by producing wide main-lobes that reduce resolution, high sidelobes that cause leakage, and point-spread functions that blur targets. Existing approaches typically address only one of these issues at a time, limiting their ability to resolve multiple, interrelated problems simultaneously. In this study, we introduce a double-compression deconvolution high-resolution beamforming method designed to enhance multi-beam sonar imaging using an underwater uniform linear array. The proposed approach formulates imaging as a sparse deconvolution problem and suppresses off-target interference through two sparse constraints, thereby improving the sonar’s resolving capability. During sparse reconstruction, an auxiliary-parameter iterative shrinkage-threshold algorithm is employed to recover azimuthal sparse signals with higher accuracy. Simulations and controlled pool experiments demonstrate that, relative to classical beamforming, the proposed method significantly improves resolution, suppresses off-target interference, expands the imaging intensity dynamic range, and yields clearer target representations. This study provides an effective strategy to mitigate intrinsic limitations in high-resolution underwater sonar imaging. Full article

(This article belongs to the Special Issue Underwater Remote Sensing: Status, New Challenges and Opportunities)

30 pages, 12207 KB

Open AccessArticle

Automatic Identification and Segmentation of Diffuse Aurora from Untrimmed All-Sky Auroral Videos

by Qian Wang, Peiqi Hao and Han Pan

Remote Sens. 2026, 18(3), 402; https://doi.org/10.3390/rs18030402 - 25 Jan 2026

Abstract

Diffuse aurora is a widespread and long-lasting auroral emission that plays an important role in diagnosing magnetosphere-ionosphere coupling and magnetospheric plasma transport. Despite its scientific significance, diffuse aurora remains challenging to identify automatically in all-sky imager (ASI) observations due to its weak optical [...] Read more.

Diffuse aurora is a widespread and long-lasting auroral emission that plays an important role in diagnosing magnetosphere-ionosphere coupling and magnetospheric plasma transport. Despite its scientific significance, diffuse aurora remains challenging to identify automatically in all-sky imager (ASI) observations due to its weak optical intensity, indistinct boundaries, and gradual temporal evolution. These characteristics, together with frequent cloud contamination, limit the effectiveness of conventional keogram-based or morphology-driven detection approaches and hinder large-scale statistical analyses based on long-term optical datasets. In this study, we propose an automated framework for the identification and temporal segmentation of diffuse aurora from untrimmed all-sky auroral videos. The framework consists of a frame-level coarse identification module that combines weak morphological information with inter-frame temporal dynamics to detect candidate diffuse-auroral intervals, and a snippet-level segmentation module that dynamically aggregates temporal information to capture the characteristic gradual onset-plateau-decay evolution of diffuse aurora. Bidirectional temporal modeling is employed to improve boundary localization, while an adaptive mixture-of-experts mechanism reduces redundant temporal variations and enhances discriminative features relevant to diffuse emission. The proposed method is evaluated using multi-year 557.7 nm ASI observations acquired at the Arctic Yellow River Station. Quantitative experiments demonstrate state-of-the-art performance, achieving 96.3% frame-wise accuracy and an Edit score of 87.7%. Case studies show that the method effectively distinguishes diffuse aurora from cloud-induced pseudo-diffuse structures and accurately resolves gradual transition boundaries that are ambiguous in keograms. Based on the automated identification results, statistical distributions of diffuse aurora occurrence, duration, and diurnal variation are derived from continuous observations spanning 2003–2009. The proposed framework enables robust and fully automated processing of large-scale all-sky auroral images, providing a practical tool for remote sensing-based auroral monitoring and supporting objective statistical studies of diffuse aurora and related magnetospheric processes. Full article

(This article belongs to the Special Issue Advances in Near-Earth Space and Atmospheric Physics from Ground-Based and Satellite Observations)

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23 pages, 10123 KB

Open AccessArticle

High-Resolution Satellite-Driven Estimation of Photosynthetic Carbon Sequestration in the Sundarbans Mangrove Forest, Bangladesh

by Nur Hussain, Md Adnan Rahman, Md Rezaul Karim, Parvez Rana, Md Nazrul Islam and Anselme Muzirafuti

Remote Sens. 2026, 18(3), 401; https://doi.org/10.3390/rs18030401 - 25 Jan 2026

Abstract

Mangrove forests provide essential climate regulation and coastal protection, yet fine-scale quantification of carbon dynamics remains limited in the Sundarbans due to spatial heterogeneity and tidal influences. This study estimated canopy structural and photosynthetic dynamics from 2019 to 2023 by integrating 10 m [...] Read more.

Mangrove forests provide essential climate regulation and coastal protection, yet fine-scale quantification of carbon dynamics remains limited in the Sundarbans due to spatial heterogeneity and tidal influences. This study estimated canopy structural and photosynthetic dynamics from 2019 to 2023 by integrating 10 m spatial high-resolution remote sensing with a light use efficiency (LUE) modeling framework. Leaf Area Index (LAI) was retrieved at 10 m resolution using the PROSAIL radiative transfer model applied to Sentinel-2 data to characterize the canopy structure of the mangrove forest. LUE-based Gross Primary Productivity (GPP) was estimated using Sentinel-2 vegetation and water indices and MODIS fPAR with station observatory temperature data. Annual carbon uptake showed clear interannual variation, ranging from 1881 to 2862 g C m⁻² yr⁻¹ between 2019 and 2023. GPP estimates were strongly correlated with MODIS-GPP (R² = 0.86, p < 0.001), demonstrating the method’s reliability for monitoring mangrove carbon sequestration. LUE-based Solar-induced Chlorophyll Fluorescence (SIF) was derived at 10 m resolution and compared with TROPOMI-SIF observations to assess correspondence (R² = 0.88, p < 0.001) with photosynthetic activity. LAI, GPP and SIF exhibited pronounced seasonal and interannual variability on photosynthetic activity, with higher values during the monsoon growing season and lower values during dry periods. Mean NDVI declined from 2019 to 2023 and modeled annual carbon uptake ranged from approximately 43 to 65 Mt CO₂ eq, with lower sequestration in 2022–2023 associated with climatic stress. Strong correlations among LAI, NDVI, GPP, and SIF indicated consistent coupling between photosynthetic activity and carbon uptake in the mangrove ecosystem. These results provide a fine-scale assessment of mangrove carbon dynamics relevant to conservation and climate-mitigation planning in tropical regions. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Technologies in Coastal Observation)

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28 pages, 5622 KB

Open AccessArticle

A Multi-Class Bahadur–Lazarsfeld Expansion Framework for Pixel-Level Fusion in Multi-Sensor Land Cover Classification

by Spiros Papadopoulos, Georgia Koukiou and Vassilis Anastassopoulos

Remote Sens. 2026, 18(3), 399; https://doi.org/10.3390/rs18030399 - 25 Jan 2026

Abstract

In many land cover classification tasks, the limited precision of individual sensors hinders the accurate separation of certain classes, largely due to the complexity of the Earth’s surface morphology. To mitigate these issues, decision fusion methodologies are employed, allowing data from multiple sensors [...] Read more.

In many land cover classification tasks, the limited precision of individual sensors hinders the accurate separation of certain classes, largely due to the complexity of the Earth’s surface morphology. To mitigate these issues, decision fusion methodologies are employed, allowing data from multiple sensors to be synthesized into robust and more conclusive classification outcomes. This study employs fully polarimetric Synthetic Aperture Radar (PolSAR) imagery and leverages the strengths of three decomposition methods, namely Pauli’s, Krogager’s, and Cloude’s, by extracting their respective components for improved detection. From each decomposition method, three scattering components are derived, enabling the extraction of informative features that describe the scattering behavior associated with various land cover types. The extracted scattering features, treated as independent sensors, were used to train three neural network classifiers. The resulting outputs were then considered as local decisions for each land cover type and subsequently fused through a decision fusion rule to generate more complete and accurate classification results. Experimental results demonstrate that the proposed Multi-Class Bahadur–Lazarsfeld Expansion (MC-BLE) fusion significantly enhances classification performance, achieving an overall accuracy (OA) of 95.78% and a Kappa coefficient of 0.94. Compared to individual classification methods, the fusion notably improved per-class accuracy, particularly for complex land cover boundaries. The core innovation of this work is the transformation of the Bahadur–Lazarsfeld Expansion (BLE), originally designed for binary decision fusion into a multi-class framework capable of addressing multiple land cover types, resulting in a more effective and reliable decision fusion strategy. Full article

(This article belongs to the Special Issue Advances in Multispectral Image Processing for Land Use and Land Cover Mapping)

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19 pages, 10092 KB

Open AccessArticle

Short-Term Degradation of Aquatic Vegetation Induced by Demolition of Enclosure Aquaculture Revealed by Remote Sensing

by Sheng Xu, Ying Xu, Guanxi Chen and Juhua Luo

Remote Sens. 2026, 18(3), 400; https://doi.org/10.3390/rs18030400 - 24 Jan 2026

Abstract

Aquatic vegetation (AV) forms the structural and functional basis of lake ecosystems, providing irreplaceable ecological functions such as water self-purification and the sustenance of biodiversity. Under the “Yangtze River’s Great Protection Strategy”, the action of returning nets to the lake has significantly improved [...] Read more.

Aquatic vegetation (AV) forms the structural and functional basis of lake ecosystems, providing irreplaceable ecological functions such as water self-purification and the sustenance of biodiversity. Under the “Yangtze River’s Great Protection Strategy”, the action of returning nets to the lake has significantly improved water-quality in the middle and lower reaches of the Yangtze River (MLRYR) basin. However, its ecological benefits for key biotic components, particularly AV communities, remain unclear. To address this knowledge gap, this study utilized Landsat and Sentinel-1 satellite imagery to analyze the dynamic evolution of enclosure aquaculture (EA) and AV in 25 lakes (>10 km²) within the MLRYR basin from 1989 to 2023. A U-Net deep learning model was employed to extract EA data (2016–2023), and a vegetation and bloom extraction algorithm was applied to map different AV groups (1989–2023). Results indicate that by 2023, 88% (22/25) of the lakes had completed EA removal. Over the 34-year period, floating/emergent aquatic vegetation (FEAV) exhibited fluctuating trends, while submerged aquatic vegetation (SAV) demonstrated a significant decline, particularly during the EA demolition phase (2016–2023), when its area sharply decreased from 804.8 km² to 247.3 km²—a reduction of 69.3%. Spatial comparative analysis further confirmed that SAV degradation was substantially more severe in EA removal areas than in EA retention areas. This study demonstrates that EA demolition, while beneficial for improving water quality, exerts significant short-term negative impacts on AV. These findings highlight the urgent need for lake governance policies to shift from single-objective management toward integrated strategies that equally prioritize water-quality improvement and ecological restoration. Future efforts should enhance targeted restoration in EA removal areas through active vegetation recovery and habitat reconstruction, thereby preventing catastrophic regime shifts to phytoplankton-dominated turbid-water states in lake ecosystems. Full article

(This article belongs to the Special Issue Water Quality Monitoring and Quantitative Analysis in Marine and Inland Areas Utilizing Remote Sensing Techniques)

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