Remote Sensing

35 pages, 18375 KB

Open AccessArticle

Spatiotemporal Dynamics and Nonlinear Associations of Eco-Environmental Quality in Mining Areas Using MRSEI and an Object-Based XGBoost-SHAP Framework

by Ting Li, Chaokui Li, Ping Zhang, Yan Mao, Qin Tian and Ling Jiang

Remote Sens. 2026, 18(18), 3099; https://doi.org/10.3390/rs18183099 - 9 Sep 2026

Abstract

Long-term mining activities can cause complex ecological and environmental problems, including mineral surface exposure, dust-related disturbance, and vegetation degradation, while conventional ecological indices may not fully characterize the distinctive ecological conditions of mining areas. This study developed a mining-area Modified Remote Sensing Ecological [...] Read more.

Long-term mining activities can cause complex ecological and environmental problems, including mineral surface exposure, dust-related disturbance, and vegetation degradation, while conventional ecological indices may not fully characterize the distinctive ecological conditions of mining areas. This study developed a mining-area Modified Remote Sensing Ecological Index (MRSEI) by incorporating the Lithological Mineral Index (LMI), Normalized Dust Difference Index (NDDI), and Vegetation Health Index (VHI) into the Remote Sensing Ecological Index (RSEI) framework. Object-based analytical units were constructed using Landsat time-series data from 2000 to 2024 and G-means clustering, followed by the development of an Object-Based XGBoost-SHAP (OB-XGBoost-SHAP) framework to quantify the relative model contributions and nonlinear associations of explanatory factors. The results showed that, in the Daye mining area (DMA), MRSEI provided clearer differentiation of ecological quality among land-use types than RSEI and Mining-Specific Eco-Environment Index (MSEEI), with an overall gradient of forest land > cultivated land > construction land > mining land. Annual Principal Component Analysis (Annual-PCA) and Global Principal Component Analysis (Global-PCA) results showed high consistency (Pearson r = 0.985). Across the seven evaluation years, the mean MRSEI of the DMA increased from 0.5493 in 2000 to 0.6035 in 2024, representing an overall change of 9.87%. The object-based model consistently outperformed the regular-grid model under random splitting, while model performance decreased under spatially separated validation but retained moderate predictive capability. Particulate Matter (PM10), Digital Elevation Model (DEM), Temperature (TEM), and Land Use Intensity (LU) showed relatively high model contributions, with their relative importance varying across different periods. This study integrates mining-specific ecological characterization, object-based spatial analysis, and explanatory-factor contribution analysis, providing a methodological reference for ecological quality assessment and long-term change monitoring in mining areas. Full article

(This article belongs to the Special Issue Innovative Applications of Multi-Source Remote Sensing Technology in Ecological Environment Quality Assessment and Governance)

23 pages, 2123 KB

Open AccessArticle

Winter Wheat Yield Estimations Based on Multisource Remote Sensing Parameters and the BiLSTM–CNN Model

by Yi Xie, Sicheng Ma, Lan Xun, Shujing Shi and Pengxin Wang

Remote Sens. 2026, 18(18), 3098; https://doi.org/10.3390/rs18183098 - 9 Sep 2026

Abstract

Winter wheat is a cornerstone of China’s grain production, contributing substantially to national food security and overall cereal output. This study modeled the nonlinear associations between multitemporal remote sensing variables and winter wheat yield. To produce high-spatiotemporal-resolution inputs, we used the Enhanced Spatial [...] Read more.

Winter wheat is a cornerstone of China’s grain production, contributing substantially to national food security and overall cereal output. This study modeled the nonlinear associations between multitemporal remote sensing variables and winter wheat yield. To produce high-spatiotemporal-resolution inputs, we used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to integrate Sentinel-2 normalized difference vegetation index (NDVI) data with MODIS NDVI data, generating NDVI composites at 8-day intervals with a 10-m spatial resolution. The NDVI, actual evapotranspiration (ET), land surface temperature (LST), precipitation (PRE), and soil moisture (SM) were selected as predictors for yield estimation because they are closely associated with winter wheat growth and yield formation during primary growth stages. By integrating the local temporal feature-learning capacity of a one-dimensional convolutional neural network (1-D CNN) with the strength of a bidirectional long short-term memory (BiLSTM) model in capturing temporal dependencies within time series, a BiLSTM–CNN model was constructed for wheat yield estimation and prediction. The BiLSTM–CNN model showed higher estimation accuracy than individual BiLSTM and 1-D CNN models, with an R² of 0.69 and root mean square error (RMSE) of 478.68 kg/hm². The use of all the parameters produced the best estimation performance among all the parameter combinations. Approximately two months before harvest, the model still provided satisfactory yield prediction accuracy. This study provides an important theoretical basis for high-accuracy regional winter wheat yield estimation and pre-harvest forecasting. Full article

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

27 pages, 4212 KB

Open AccessArticle

Inshore Analysis of the Morphostructural Evolution of the Coastal Cliffs of Bessin, Basse Normandie, France

by Alizée Vioget, Antonin Chalé, Clément Michoud, Stéphane Costa, Olivier Maquaire, Michel Jaboyedoff, Marc-Henri Derron and Robert Davidson

Remote Sens. 2026, 18(18), 3097; https://doi.org/10.3390/rs18183097 - 9 Sep 2026

Abstract

The limestone–marl cliffs along France’s northern coast, particularly from Arromanches-les-Bains to Longues-sur-Mer, are experiencing significant erosion. This study investigates cliff retreat mechanisms and classifies instability types through image analysis, LiDAR measurements, and field observations. Key instabilities comprise various types of rock instabilities within [...] Read more.

The limestone–marl cliffs along France’s northern coast, particularly from Arromanches-les-Bains to Longues-sur-Mer, are experiencing significant erosion. This study investigates cliff retreat mechanisms and classifies instability types through image analysis, LiDAR measurements, and field observations. Key instabilities comprise various types of rock instabilities within limestone formations, principally promoted by erosion and creeping of the underlying marls. Direct limestone panel sinkings, wave-cut notches and rockslides are also observed. All of these are controlled by environmental exposure, weathering, and pebble beaches. A review of images from 1947 to 2012 showed that cliff recession occurs in discrete events, averaging 0.06–0.30 m per year. Notably, a May 2013 rockslide near Cape Manvieux displaced around 30,000 cubic m of material, triggered by interactions between creeping marls and destabilized limestone. This research emphasises the significance of comprehending these processes. Full article

(This article belongs to the Section Environmental Remote Sensing)

39 pages, 1472 KB

Open AccessArticle

Frequency-Guided Cross-Scale Refinement Network for UAV Detection

by Xingwei Yan, Haitao Zhao, Kunlin Zou, Wei Wang, Yaxiu Zhang and Yan Zhang

Remote Sens. 2026, 18(18), 3096; https://doi.org/10.3390/rs18183096 - 9 Sep 2026

Abstract

In recent years, the use of UAVs has become increasingly widespread, and the public safety risks posed by unauthorized UAV flights have become increasingly prominent, creating an urgent need for effective detection and identification of UAV targets. However, such targets are small in [...] Read more.

In recent years, the use of UAVs has become increasingly widespread, and the public safety risks posed by unauthorized UAV flights have become increasingly prominent, creating an urgent need for effective detection and identification of UAV targets. However, such targets are small in size, have low contrast, and exhibit an extremely low signal-to-noise ratio; conventional detection methods generally suffer from insufficient feature discrimination, missed detections, and false alarms in complex backgrounds. To address these challenges, this paper proposes a Frequency-Guided Cross-scale Refinement Network (FGCR-Net). Based on an encoder-decoder architecture, this network achieves end-to-end collaborative optimization through cross-layer feature fusion, side-channel prediction refinement, and frequency-domain background suppression. First, a multi-path selective cross-layer fusion module (SCFM) is designed. This module employs coordinated modeling via both channel and spatial paths, supplemented by adaptive weighting with learnable coefficients, to perform differentiated selective fusion of the encoder’s fine-grained features and the decoder’s semantic features, thereby bridging the semantic gap at jump connections; Second, we designed a Cross-Scale Adaptive Fusion Enhancement Attention Module (CAFEM), which cascades multi-receptive-field hollow convolutions, strip pooling, and a bidirectional semantic guidance mechanism to perform cross-scale refinement on the side outputs of each decoder layer, thereby alleviating the issues of blurred boundaries and false alarms caused by inconsistent quality of multi-scale prediction maps and insufficient cross-layer consistency; finally, we design a Frequency-Guided Semantic Enhancement Module (FGSEM), which uses the Fast Fourier Transform (FFT) to decouple encoder features into the frequency domain. By leveraging low-frequency energy to predict the background confidence map and applying spatially selective suppression to high-frequency components, this module distinguishes, from a frequency-domain perspective, the high-frequency responses of complex backgrounds and targets that are highly similar in the spatial domain. Experiments on MSDS-UAV, a self-built multi-scenario UAV dataset for small targets, demonstrate that our method consistently outperforms existing state-of-the-art methods across multiple performance metrics, with Pixel Accuracy, Mean Intersection over Union, and Probability of Detection reaching 92.76%, 70.91%, and 92.69%, respectively; Compared to the baseline model, these three metrics improved by 1.90, 3.20, and 3.76 percentage points, respectively, fully validating the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Deep Learning-Based Small-Target Detection in Remote Sensing—2nd Edition)

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36 pages, 29582 KB

Open AccessArticle

Ground-Based GNSS Atmospheric Remote Sensing for Ultra-Short-Term Wind-Power Forecasting: A Direction-Proxy-Guided Graph-Residual Approach

by Peiyan Gong and Chaoxia Yuan

Remote Sens. 2026, 18(18), 3095; https://doi.org/10.3390/rs18183095 - 9 Sep 2026

Abstract

Ground-based Global Navigation Satellite System (GNSS) stations provide continuous atmospheric remote sensing through electromagnetic propagation delays. Precise point positioning (PPP) yields zenith tropospheric delay (ZTD), and ZTD gradients provide a proxy for off-farm tropospheric structure that is unavailable to supervisory control and data [...] Read more.

Ground-based Global Navigation Satellite System (GNSS) stations provide continuous atmospheric remote sensing through electromagnetic propagation delays. Precise point positioning (PPP) yields zenith tropospheric delay (ZTD), and ZTD gradients provide a proxy for off-farm tropospheric structure that is unavailable to supervisory control and data acquisition (SCADA)-only forecasts. We propose a model combining a long short-term memory (LSTM) backbone, GNSS conditioning, and a graph neural network (GNN), denoted LSTM+GNN+GNSS, for 4 h wind-power forecasting. Historical PPP-derived ZTD and quality indicators condition a shared temporal representation; a ZTD-gradient direction proxy, turbine geometry, and observation confidence guide a gated graph-residual correction at 15–90 min. On 666 common Yandun test origins, we compare LSTM, LSTM+GNN, and LSTM+GNN+GNSS. The complete system achieves a normalized mean absolute error (nMAE) of 4.53% (4.527 ± 0.132% across three power-model seeds), reducing nMAE by 7.57% relative to LSTM+GNN and 8.23% relative to LSTM. Paired moving-block 95% confidence intervals support both comparisons, while Bonferroni-adjusted lead-wise tests agree from 30 to 225 min. These results demonstrate the incremental predictive value of the complete GNSS-conditioning pathway under the chronological holdout protocol. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

18 pages, 3923 KB

Open AccessData Descriptor

OPMS-Seg: A UAV-Based High-Resolution Image Dataset for Semantic Segmentation of Open-Pit Coal Mine Slopes

by Lingkai Shi, Yide Geng, Haoran Wang, Hongwei Wang, Zhixin Jin, Zhiyong Yang, Guilin Hu and Guangxu Luo

Remote Sens. 2026, 18(18), 3094; https://doi.org/10.3390/rs18183094 - 9 Sep 2026

Abstract

Landslides induced by slope deformation in open-pit coal mines pose significant risks to personnel safety and production continuity. Intelligent slope recognition is a prerequisite for early deformation warning, yet no publicly available segmentation dataset specifically targeting open-pit mine slopes currently exists, hindering progress [...] Read more.

Landslides induced by slope deformation in open-pit coal mines pose significant risks to personnel safety and production continuity. Intelligent slope recognition is a prerequisite for early deformation warning, yet no publicly available segmentation dataset specifically targeting open-pit mine slopes currently exists, hindering progress in vision-based monitoring. To address this gap, we present OPMS-Seg, a benchmark dataset for semantic segmentation of open-pit mine slopes. The dataset contains 2814 UAV-captured RGB images and 15,334 polygon instances, covering four geometric slope types (Backlight, Stepped, Rubble, and Bottom types). Annotation files are provided in COCO (JSON), YOLO (TXT), and binary mask (PNG) formats to support diverse segmentation models. All annotations were validated by mining engineering experts. The dataset supports two segmentation tasks: (1) binary segmentation (slope vs. background) and (2) four-class fine-grained segmentation (distinguishing the four slope geometry types). In this paper, we present baseline results for the binary segmentation task, while the four-class task is provided as a benchmark for future research. We evaluated four classic models—U-Net, DeepLabV3+, YOLOv5-Seg, and YOLOv11-Seg—on OPMS-Seg, achieving strong performance and confirming annotation accuracy and scene representativeness. This open-access dataset is intended to accelerate intelligent monitoring, algorithm benchmarking, and low-altitude remote sensing tasks in open-pit environments. Full article

(This article belongs to the Section Earth Observation Data)

27 pages, 8393 KB

Open AccessReview

Retrieval of Vegetation Nitrogen from Hyperspectral Remote Sensing: A Critical Review of Recent Methodological Advances

by Jochem Verrelst, Anirudh Belwalkar, Kang Yu, Miguel Morata and Manish Kumar Patel

Remote Sens. 2026, 18(18), 3093; https://doi.org/10.3390/rs18183093 - 9 Sep 2026

Abstract

Hyperspectral retrieval of nitrogen-related vegetation variables has undergone rapid methodological advances driven by the emergence of protein-sensitive radiative transfer models (RTMs), modern machine learning (ML), and operational imaging spectroscopy. This review synthesizes recent developments in hyperspectral retrieval of nitrogen-related vegetation variables across leaf [...] Read more.

Hyperspectral retrieval of nitrogen-related vegetation variables has undergone rapid methodological advances driven by the emergence of protein-sensitive radiative transfer models (RTMs), modern machine learning (ML), and operational imaging spectroscopy. This review synthesizes recent developments in hyperspectral retrieval of nitrogen-related vegetation variables across leaf and canopy scales, with particular emphasis on advances reported between 2020 and 2026. We examine the evolution from classical parametric regression and nonlinear ML approaches towards physically based RTM inversion and hybrid RTM–ML frameworks that integrate the complementary strengths of physical modeling and statistical learning. Particular attention is given to protein-sensitive RTMs, advanced ML approaches, and uncertainty-aware retrieval. Recent developments highlight the potential of hybrid RTM–ML frameworks to combine physical consistency with computationally efficient statistical learning, while probabilistic methods such as Gaussian Process Regression provide additional capabilities for uncertainty characterization. The review further discusses the transition from experimental studies to operational applications enabled by airborne and satellite imaging spectroscopy, including PRISMA, EnMAP, and forthcoming missions such as CHIME. Remaining challenges include the inherently ill-posed nature of nitrogen retrieval, limited and insufficiently representative calibration data, uncertainty characterization, and generalization across sensors, species, and ecosystems. Overall, the reviewed evidence points towards increasingly integrated retrieval frameworks, while emphasizing that robust transferability and operational implementation remain dependent on representative data, physical realism, and rigorous uncertainty assessment. Full article

(This article belongs to the Special Issue Hyperspectral Data Analysis of Vegetation and Soil Monitoring)

33 pages, 9596 KB

Open AccessArticle

A Hybrid Super-Resolution and Object Detection Framework for Small Ship Recognition in Optical Remote Sensing Imagery

by Muhammad Abubakar Saleem, Waseemullah Nazir, Muhammad Umar Farooq, Muhammad Qasim Memon, Sami Dhahbi, Afef Dhahbi and Anas Bilal

Remote Sens. 2026, 18(18), 3092; https://doi.org/10.3390/rs18183092 - 9 Sep 2026

Abstract

Continuous maritime surveillance increasingly relies on optical remote sensing, yet small ships are difficult to detect in low-resolution imagery because limited spatial resolution, complex sea backgrounds, and sensor noise suppress the fine-grained cues on which detectors depend, while routine acquisition of high-resolution imagery [...] Read more.

Continuous maritime surveillance increasingly relies on optical remote sensing, yet small ships are difficult to detect in low-resolution imagery because limited spatial resolution, complex sea backgrounds, and sensor noise suppress the fine-grained cues on which detectors depend, while routine acquisition of high-resolution imagery is prohibitively costly. To address this problem, a hybrid framework (RGT-YOLOv5Det) is proposed that couples transformer-based super-resolution (SR) with a lightweight object detector so that fine spatial detail is restored before detection. Methodologically, a paired benchmark (Ship-HRRSI/Ship-LRRSI) was constructed from the public TGRS-HRRSD dataset by standardising images to 800 × 800 pixels and generating 200 × 200 pixel counterparts via 4× bicubic down-sampling; three SR models (RGT, HAT, and Real-ESRGAN) and four detectors (YOLOv5s, YOLOv8s, YOLOv10s, and Faster R-CNN-MobileNetV3-Large-FPN) were fine-tuned and compared under identical training settings, and the best-performing components were integrated into the proposed two-stage pipeline. In the results, RGT delivered the best reconstruction quality (PSNR 22.038 dB, SSIM 0.3502) with the fewest parameters (13.37 M), YOLOv5s proved the most resolution-robust detector, and the integrated RGT-YOLOv5Det achieved mAP@0.5 of 0.947 and mAP@0.5:0.95 of 0.768 on low-resolution imagery, exceeding the best standalone detector score on each metric by 0.033 and 0.089, respectively. It is concluded that restoring structural detail prior to detection offers an accurate and acquisition cost-efficient alternative to high-resolution imaging, providing a practical route to reliable small ship detection in degraded optical remote sensing imagery. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection (Third Edition))

22 pages, 52356 KB

Open AccessArticle

Enhancing Our View from Above: A Downscaling Technique to Reveal Finer-Scale Urban Heat Patterns

by Evan Shea, Aubrey Benson, Tijmen Witvliet, Mark Fillo and Melissa R. McHale

Remote Sens. 2026, 18(18), 3091; https://doi.org/10.3390/rs18183091 - 9 Sep 2026

Abstract

Remotely sensed surface temperature data are widely used by researchers and municipal practitioners to characterize urban thermal patterns and assess the impacts of increasing urban heat. While satellite-derived land surface temperature (LST) data effectively identify broad-scale patterns, their 30 m spatial resolution limits [...] Read more.

Remotely sensed surface temperature data are widely used by researchers and municipal practitioners to characterize urban thermal patterns and assess the impacts of increasing urban heat. While satellite-derived land surface temperature (LST) data effectively identify broad-scale patterns, their 30 m spatial resolution limits their ability to resolve fine-scale thermal variability. Downscaling approaches have been developed to generate higher-resolution LST products; however, in the absence of independent, high-resolution surface temperature observations, it remains difficult to determine whether the additional spatial variation introduced by downscaling reflects meaningful landscape-related thermal structure or model-generated variability. Our objective was to evaluate whether the additional spatial variation introduced by downscaling LST from 30 to 10 m was statistically consistent with independently derived land cover composition and landscape compositional heterogeneity. Using an open-source random forest model, we downscaled Landsat 8/9 LST to 10 m resolution using Sentinel-2-derived predictors over a summer season in Kelowna, BC, Canada. We quantified the additional thermal variation introduced through downscaling and used Generalized Additive Models (GAMs) to relate this variation to high-resolution land cover characteristics summarized within 100 m grid cells. Land cover composition and landscape compositional heterogeneity together explained 65.5% of the additional variation introduced by downscaling. Added thermal variation was most strongly associated with canopy and impervious cover, two landscape characteristics consistently identified as major controls on urban land surface temperature. These findings suggest that the additional spatial variation introduced through downscaling is consistent with independently derived landscape structure known to influence urban thermal patterns. Rather than providing direct validation of downscaled temperatures, this analysis offers an indirect means of evaluating whether downscaled LST produces interpretable, landscape-consistent thermal variability that may be useful for urban analysis and planning. Full article

(This article belongs to the Special Issue Monitoring Urban Environment and Temperature Change Using Remote Sensing)

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33 pages, 20866 KB

Open AccessArticle

An Integrated Approach for Mapping Multi-Category Abandoned Cropland Based on Swin-Unet, Land-Use Trajectories and Phenological Features in a Double-Cropping Rice Region of Southern China

by Yuhao Jin, Falin Zhuang, Hengkang Zhu, Xiaozhen Qiu and Xing Mao

Remote Sens. 2026, 18(18), 3090; https://doi.org/10.3390/rs18183090 - 9 Sep 2026

Abstract

Cropland abandonment directly undermines the regional food supply, requiring accurate monitoring to inform protection and recultivation policies. However, multi-category abandoned-cropland mapping remains challenging in southern China’s double-cropping rice regions because fragmented fields, frequent cloud/rain conditions, and complex phenology can obscure abandonment signals. Focusing [...] Read more.

Cropland abandonment directly undermines the regional food supply, requiring accurate monitoring to inform protection and recultivation policies. However, multi-category abandoned-cropland mapping remains challenging in southern China’s double-cropping rice regions because fragmented fields, frequent cloud/rain conditions, and complex phenology can obscure abandonment signals. Focusing on a typical double-cropping rice region in southern China, this study first used Swin-UNet to generate annual land-use maps from 2018 to 2022 and derive interannual trajectories. It then identified non-cultivated cropland within each year using Normalized Difference Vegetation Index (NDVI) data from key phenological periods and block-adaptive Otsu thresholding. Overlaying cultivation status with land-use trajectories further subdivided non-cultivated cropland into construction-occupied abandoned, naturally abandoned cropland, and fallow cropland. Finally, the spatial differentiation of each type and the potential grain-production gains from recultivating naturally abandoned cropland were analyzed. Results showed that annual classification accuracies from 2018 to 2022 ranged from 94.7 to 95.5%, supporting reliable cropland change trajectory extraction. From 2019 to 2022, total abandoned area decreased from 546.60 km² to 486.73 km². Naturally abandoned cropland remained dominant but declined mainly because 18.5–21.7% of it was converted back to stable cropland annually, while construction-occupied abandonment increased and fallow cropland remained relatively stable. Natural abandonment concentrated on moderate slopes near settlements and roads, with a theoretical grain-production potential equivalent to 12.88–15.14% of Maoming’s annual grain output. This study advances abandoned-cropland monitoring from area estimation to type-specific process interpretation and recultivation-priority assessment in southern double-cropping rice regions. Full article

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27 pages, 42222 KB

Open AccessArticle

Geo-XAI Reveals Wildfire Risk Driver Differences and Spatial Heterogeneity Between Drought and Non-Drought Periods in Southwest China Mountains

by Fuwen Li, Wenlong Yang, Jiangxia Ye, Lei Kong, Xiaojie Yin, Xun Zhao, Weili Kou, Zhichao Huang, Xinkun Zhu, Zhou Mao and Yiping Xu

Remote Sens. 2026, 18(18), 3089; https://doi.org/10.3390/rs18183089 - 9 Sep 2026

Abstract

Global warming has increased the frequency of droughts, further complicating wildfire susceptibility assessment in topographically complex mountainous regions. However, the factors associated with wildfire occurrence and their nonlinear spatial responses under contrasting drought conditions remain poorly understood. To address this knowledge gap, we [...] Read more.

Global warming has increased the frequency of droughts, further complicating wildfire susceptibility assessment in topographically complex mountainous regions. However, the factors associated with wildfire occurrence and their nonlinear spatial responses under contrasting drought conditions remain poorly understood. To address this knowledge gap, we conducted a case study in a fire-prone mountainous region of southwestern China. Drought and non-drought periods were identified using the Standardized Precipitation Evapotranspiration Index (SPEI) and run theory. Using historical wildfire records from 2006 to 2020 and 16 wildfire drivers, we developed three machine-learning models and applied GeoShapley to quantify the contributions of key predictors and characterize their spatial dynamics under different drought conditions. The results showed that the Extreme Gradient Boosting (XGB) model achieved the best predictive performance (AUC = 0.85–0.91) and effectively captured the spatial patterns of wildfire susceptibility. Meteorological variables consistently emerged as the dominant controls on wildfire occurrence, although their relative importance and functional effects differed substantially between drought and non-drought conditions. GeoShapley analysis further revealed pronounced spatial heterogeneity in the effects of the major drivers, with these spatial patterns further modulated by drought conditions. In particular, during drought periods, the local geographical context amplified the spatial interaction effect of precipitation, resulting in a stronger risk-enhancing effect than during non-drought periods. By decomposing variable contributions into non-spatial main effects and spatially explicit interaction effects, this study reveals how the effects of wildfire drivers vary spatially under contrasting drought conditions and provides a robust framework for targeted wildfire mitigation in complex mountainous landscapes. Full article

(This article belongs to the Special Issue Remote Sensing and Earth Intelligence in Wildfire Ecology and Forest Restoration Monitoring)

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26 pages, 40355 KB

Open AccessArticle

High-Spectral-Resolution Retrieval of Ultraviolet and Visible Aerosol Optical Depth Using the Pandora Spectrometer

by Seong-Young Kim, Ukkyo Jeong, Hyunkee Hong, Suna Shin, Serin Kim, Donghee Kim, Si-Chee Tsay, Jhoon Kim, Hyeji Cha, Kangho Bae and Enkhbaatar Davaanyam

Remote Sens. 2026, 18(18), 3088; https://doi.org/10.3390/rs18183088 - 9 Sep 2026

Abstract

Atmospheric aerosols from natural and anthropogenic sources are a major component of air pollution and substantially affect air quality, human health, and atmospheric chemistry. Reliable and continuous aerosol optical depth (τ_aer) measurements from ground-based instruments are therefore essential for characterizing [...] Read more.

Atmospheric aerosols from natural and anthropogenic sources are a major component of air pollution and substantially affect air quality, human health, and atmospheric chemistry. Reliable and continuous aerosol optical depth (τ_aer) measurements from ground-based instruments are therefore essential for characterizing aerosol loading and its spatiotemporal variability. This study presents a framework for retrieving τ_aer in the UV (315–375 nm) and visible (400–530 nm) spectral ranges using the Pandora spectrometer. In the ultraviolet (UV) range, reliable τ_aer retrieval from standard direct-Sun measurements is hindered by out-of-band stray light from visible wavelengths. To address this limitation, this study utilizes measurements acquired with a U340 filter, which suppresses this contamination in UV spectra and enables reliable τ_aer retrievals in the UV region. Through the proposed spectral fitting framework, trace gas absorption and aerosol extinction components are successfully separated from the measured total optical depth. Concurrently, τ_aer values at visible wavelengths are retrieved from standard Diffuser measurements. The retrieved UV and visible τ_aer values show good agreement with collocated AErosol RObotic NETwork (AERONET) data, with R² = 0.88–0.99, RMSE = 0.016–0.039, |MBE| = 0.002–0.023, slopes of 0.93–1.01, and |intercepts| < 0.01. Together with the trace gas products simultaneously retrieved by Pandora, these τ_aer datasets can serve as a valuable resource for air pollution analysis. Full article

(This article belongs to the Special Issue Remote Sensing for Air Quality, Health, and Sustainable Development)

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18 pages, 3947 KB

Open AccessArticle

An Intelligent Method for Ice Thickness Identification Using Drone-Borne Ground-Penetrating Radar

by Ruige Shi, Zhenjun Zhu, Zizhao Lu, Jiangyang Pan, Xu Meng, Hai Liu, Zongming Yang, Di Cui, Weizheng Kong and Yingxin Shang

Remote Sens. 2026, 18(18), 3087; https://doi.org/10.3390/rs18183087 - 9 Sep 2026

Abstract

Unmanned Aerial Vehicle-borne Ground-Penetrating Radar (UAV-GPR) has been used for ice thickness monitoring in lakes and rivers due to its non-contact measurement, high resolution, and operational flexibility. Existing algorithms can extract ice layer boundaries by tracking continuous bottom reflections in GPR images. However, [...] Read more.

Unmanned Aerial Vehicle-borne Ground-Penetrating Radar (UAV-GPR) has been used for ice thickness monitoring in lakes and rivers due to its non-contact measurement, high resolution, and operational flexibility. Existing algorithms can extract ice layer boundaries by tracking continuous bottom reflections in GPR images. However, they fail when the radar signal lacks a clear bottom reflection—a common condition in ice layers containing unfrozen water—and manual interpretation remains time-consuming. To address this limitation, this paper builds a freshwater ice GPR dataset covering both fully frozen and unfrozen water-bearing zones, and proposes a method for ice thickness identification based on the DeepLabv3+ neural network. The model performs pixel-level binary classification, labeling each pixel as ice layer or background, and generates a segmentation mask that constrains the subsequent thickness calculation to valid ice regions only. Field validation against drilling measurements demonstrates that the model achieves Intersection over Union (IoU) of 97.12% and an F1-score of 98.54% for ice layer identification, with a relative error in ice thickness measurement below 3% based on five borehole measurements. Field tests in two reservoirs across Tibet and Jilin, China, demonstrate that the proposed method can accurately characterize the distribution and thickness of the ice layer while effectively eliminating the interference of unfrozen water zones. The results demonstrate that the proposed method can provide automated, accurate ice thickness estimates for UAV-GPR surveys of freshwater ice. Full article

(This article belongs to the Special Issue Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms)

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25 pages, 8273 KB

Open AccessArticle

Multi-Sensor Mapping of Aquatic Vegetation Using Sentinel-2, SAR–Optical Fusion and Derived Spectral and SAR Indices in South Florida

by Bella Harandi, Nathan Gavin, Jing Hu and Weiwei Zhan

Remote Sens. 2026, 18(18), 3086; https://doi.org/10.3390/rs18183086 - 9 Sep 2026

Abstract

Accurate mapping of submerged aquatic vegetation (SAV) and emergent aquatic vegetation (EAV) is essential for hydrologic assessment and wetland management. This study developed and evaluated a multi-sensor aquatic vegetation mapping framework using Sentinel-1 SAR, Sentinel-2 optical imagery, and derived spectral and SAR indices [...] Read more.

Accurate mapping of submerged aquatic vegetation (SAV) and emergent aquatic vegetation (EAV) is essential for hydrologic assessment and wetland management. This study developed and evaluated a multi-sensor aquatic vegetation mapping framework using Sentinel-1 SAR, Sentinel-2 optical imagery, and derived spectral and SAR indices in the Everglades stormwater treatment areas (STAs) of South Florida. Three feature configurations were compared: Sentinel-2 only (S2), Sentinel-2 combined with Sentinel-1 (S2 + S1), and Sentinel-2 and Sentinel-1 augmented with derived spectral and SAR indices (S2 + S1 + Indices). Random forest (RF) and TabNet classifiers were trained to discriminate SAV and EAV. On the internal test set, both classifiers achieved similar overall accuracies (OA = 0.83–0.85), with no clear performance advantage between RF and TabNet. However, differences emerged in spatial generalization. The S2 + S1 + Indices configuration consistently produced the most robust performance in independent validation areas, achieving OA values of 0.86 and 0.90 for TabNet. The results indicate that the combination of Sentinel-1, Sentinel-2, and derived spectral and SAR indices generally provides the most robust and transferable feature representation, with the greatest benefits observed in the unseen spatial validation areas. These findings demonstrate the potential of multi-sensor data fusion for scalable wetland vegetation monitoring in cloud-prone and hydrologically dynamic environments. Full article

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23 pages, 10174 KB

Open AccessArticle

Stratified Spatiotemporal Residual Detection of Weak Active-Fire Anomalies from VIIRS 375 m Time Series

by Huijuan Gao and Yanfang Ming

Remote Sens. 2026, 18(18), 3085; https://doi.org/10.3390/rs18183085 - 9 Sep 2026

Abstract

Satellite active-fire products may miss fires that occupy only a small fraction of a pixel and produce limited absolute thermal responses. In this study, weak thermal anomalies are operationally defined as confirmed fire-affected VIIRS pixels with relatively low absolute

B T_{4}

[...] Read more.

Satellite active-fire products may miss fires that occupy only a small fraction of a pixel and produce limited absolute thermal responses. In this study, weak thermal anomalies are operationally defined as confirmed fire-affected VIIRS pixels with relatively low absolute

B T_{4}

and

Δ B T

responses but positive deviations from their recent temporal and local spatial backgrounds. STAR-FD (Stratified Spatiotemporal Adaptive Residual Fire Detection) was developed to detect such anomalies from VIIRS 375 m observations. The method reconstructs a 14-day recent thermal background and jointly evaluates single-pixel temporal residuals and spatial-contrast temporal residuals. Stable non-fire samples are stratified by day/night condition, season, macroclimate zone, land cover, and view zenith angle, and residual thresholds are estimated within each stratum. Validation was conducted in Heilongjiang, northwestern India, and California using Sentinel-2 MSI, Landsat 8/9 OLI, and available external fire information. STAR-FD detected 927 overpass-level thermal-anomaly events, of which 855 were confirmed as Fire, corresponding to a confirmation rate of 92.23%. VNP14IMG detected 343 events, of which 333 were confirmed as Fire (97.08%). STAR-FD identified 522 more confirmed fire-related thermal-anomaly events than VNP14IMG, corresponding to a 156.76% increase, while retaining 88.05% of VNP14IMG events. In northwestern India and Heilongjiang, STAR-FD-added daytime confirmed fire pixels had median

B T_{4}

values 9.49 K and 11.88 K lower, respectively, and median

Δ B T

values 8.10 K and 10.78 K lower than detections shared by both methods. These results show that STAR-FD provides complementary detection of confirmed fire-related thermal anomalies with weaker absolute thermal signals. Full article

(This article belongs to the Section Environmental Remote Sensing)

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26 pages, 6131 KB

Open AccessArticle

Weak-Observation-Aware Multi-Object Tracking in Satellite Video with Temporal Evidence and Trajectory Reliability

by Fen Hu, Peng Yang, Jie Dou and Lei Dou

Remote Sens. 2026, 18(18), 3084; https://doi.org/10.3390/rs18183084 - 9 Sep 2026

Abstract

Multi-object tracking (MOT) in satellite video is fundamentally limited by low target observability: genuine targets often produce weak and unstable responses, while structured backgrounds can generate persistent target-like interference, leading to missed detections, fragmented trajectories, and identity switches. To address this ambiguity, we [...] Read more.

Multi-object tracking (MOT) in satellite video is fundamentally limited by low target observability: genuine targets often produce weak and unstable responses, while structured backgrounds can generate persistent target-like interference, leading to missed detections, fragmented trajectories, and identity switches. To address this ambiguity, we propose a weak-observation-aware and reliability-guided framework with a layered two-stage design: the front end enhances weak observations over short temporal windows, whereas the back end controls their use for long-term trajectory association and state updating according to their reliability. The front-end Temporal Prior Module (TPM) constructs a structure-aware temporal prior from motion-aligned historical evidence, strengthening weak-target responses while limiting the propagation of structured-background interference. The back-end Satellite Weak-Observation Reliability-Guided Tracker (SWRT) introduces active-track support into short-window reasoning and combines current, short-term, and long-term evidence into a candidate-reliability score that regulates association eligibility and state-update strength. On VISO, the proposed method achieves a multiple object tracking accuracy (MOTA) of 72.5% and an identity F1 score (IDF1) of 81.1%. On SATMTB-MOT, it achieves the best MOTA and IDF1 for airplanes and vehicles and remains competitive for ships among the compared methods. These results demonstrate that this layered design achieves a better balance between target recovery and identity preservation under low-observability conditions. Full article

(This article belongs to the Special Issue Advanced Object Detection, Classification and Recognition in VIR Optical and SAR Remote Sensing Imagery)

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31 pages, 31203 KB

Open AccessArticle

Hyperspectral Mineral Mapping of Drill-Core Samples at Sadiola Hill Gold Deposit, Mali, West Africa: The Paragenesis of the Polyphase Alteration Mineralogy

by Semyon Martynenko, Frank van Ruitenbeek, Kim Hein and Pekka Tuisku

Remote Sens. 2026, 18(18), 3083; https://doi.org/10.3390/rs18183083 - 9 Sep 2026

Abstract

The Sadiola Hill gold deposit is located in Mali in the Kédougou–Kénieba Inlier (KKI) of the West African craton. The gold deposit is hosted in metamorphosed carbonaceous and clastic sedimentary rocks that are intruded by diorite dykes and granitoids. The deposit records a [...] Read more.

The Sadiola Hill gold deposit is located in Mali in the Kédougou–Kénieba Inlier (KKI) of the West African craton. The gold deposit is hosted in metamorphosed carbonaceous and clastic sedimentary rocks that are intruded by diorite dykes and granitoids. The deposit records a multistage history of alteration, including complex compositional variation and speciation of carbonate group minerals and white and dark micas. We present the results of spectral and petrographic studies of 36 drill core samples from six diamond boreholes from the Sadiola Hill gold mine to (1) refine paragenetic studies and (2) define alteration assemblages accompanying the main gold event. The methodologies used included high-resolution short-wave infrared (SWIR) hyperspectral imaging, electron microprobe analysis, and conventional petrography. The alteration assemblages that correspond with the gold mineralizing event include ferroan dolomite, dolomite, ankerite, Al-poor muscovite/illite to muscovite/muscovitic illite, and phlogopite. Pervasive and vein-type carbonate alteration includes ferroan dolomite–ankerite in greywacke and dolomite in the marble (with minor Fe²⁺ substitutions). The texture and spectral character of pervasive hydrothermal dolomite differ from metamorphic dolomite. In the greywacke, it is dominantly expressed as Au-stage Al-poor illite and Al-poor muscovite. In marble, it is expressed as moderately crystalline muscovitic illite and muscovite. In well-mineralized marble samples, white mica is notably more crystalline and aluminous. Pre-gold Mg-rich illite is fully consumed. Phengite, calcite, and chlorite occur late in the paragenetic sequence irrespective of lithology and post-date the main gold event. Results demonstrate that the integrated study of hyperspectral imaging with petrography and microprobe analysis characterizes the alteration mineralogy of the gold-mineralizing event and refines the paragenetic sequence of the gold deposit. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing for Mineral Exploration and Lithological Mapping)

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17 pages, 13887 KB

Open AccessArticle

Station-Based Evaluation of AI Weather Models for Near-Surface Temperature, Pressure, and Wind Forecasts over Eastern Coastal China

by Xiangping Chen, Liangke Huang, Zhouao Zheng, Yuhang Gu, Changzeng Tang, Yifei Yang, Haojun Li, Peng Yuan and Lilong Liu

Remote Sens. 2026, 18(18), 3082; https://doi.org/10.3390/rs18183082 - 9 Sep 2026

Abstract

Accurate prediction of near-surface meteorological variables is important for weather services and coastal risk management. However, the station-level performance of global artificial intelligence (AI) weather models remains insufficiently characterized in complex coastal environments. This study evaluated Pangu-Weather, FengWu, FuXi, and the Global Forecast [...] Read more.

Accurate prediction of near-surface meteorological variables is important for weather services and coastal risk management. However, the station-level performance of global artificial intelligence (AI) weather models remains insufficiently characterized in complex coastal environments. This study evaluated Pangu-Weather, FengWu, FuXi, and the Global Forecast System (GFS) against observations from 210 stations in eastern coastal China from July to December 2022. The assessment focused specifically on 2 m temperature, surface pressure, 10 m wind speed, and wind direction across forecast lead times, stations, and routine and typhoon conditions. FuXi had the lowest temperature RMSE (1.70 °C), whereas FengWu had the lowest pressure and wind-speed RMSE values (0.89 hPa and 1.17 m/s, respectively). The models showed distinct spatial error patterns, and wind-speed errors were concentrated at several northern coastal and transition-zone stations. During Typhoon Muifa, errors increased for all models, with the largest deterioration occurring for wind speed. FengWu retained the lowest typhoon-period wind-speed RMSE (1.87 m/s), whereas GFS had the largest value (3.03 m/s). Wind-direction distributions remained difficult for all models to reproduce. These results support variable-specific model selection, but they should not be interpreted as a general ranking of atmospheric forecast systems because the validation is limited to near-surface station data and a six-month period. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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27 pages, 5233 KB

Open AccessArticle

Azimuth-Extended Spotlight SAR Based on Multifrequency Subpulse Diversity

by Hai Huang, Ximin Li, Shengqi Zhu, Jiawei Qi, Shaohua Li, Guisheng Liao and Lan Lan

Remote Sens. 2026, 18(18), 3081; https://doi.org/10.3390/rs18183081 - 8 Sep 2026

Abstract

An azimuth wide-swath spotlight synthetic aperture radar (SAR) imaging method utilizing multifrequency subpulse (MFSP) diversity is proposed. Initially, the MFSP scheme is formulated by dividing the transmit pulse into multiple subpulses, where each is assigned a distinct carrier frequency offset with bandwidth-level spacing. [...] Read more.

An azimuth wide-swath spotlight synthetic aperture radar (SAR) imaging method utilizing multifrequency subpulse (MFSP) diversity is proposed. Initially, the MFSP scheme is formulated by dividing the transmit pulse into multiple subpulses, where each is assigned a distinct carrier frequency offset with bandwidth-level spacing. Different subpulses are then steered toward multiple desired subscenes to achieve wide azimuth beam coverage. After receiving the aliased signals from different subscenes, the unambiguous subscene echoes are extracted through a set of bandpass filters. Subsequently, interpolation along the line-of-sight is employed to accurately reconstruct the original sub-images based on the squint geometry of each subscene. Ultimately, following both geometric and radiometric corrections, the complete azimuth-extended spotlight image is generated by merging subscene imagery according to their geometric relationships. Specifically, the key imaging characteristics and parameter design strategies are discussed. The effectiveness of the proposed method is validated through comparisons with traditional methods, demonstrating its capability to expand azimuth width without degrading azimuth resolution or introducing severe wavefront-curvature errors. Full article

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

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