Remote Sensing

27 pages, 13821 KB

Open AccessArticle

High-Resolution Mapping of Forest Vegetation Types Using Multiplatform Imagery and Advanced Classification Techniques

by Javier Marcello, Francisco Eugenio, Antonio Mederos-Barrera, Consuelo Gonzalo-Martín, Ángel García-Pedrero and Meryeme Boumahdi

Remote Sens. 2026, 18(17), 2871; https://doi.org/10.3390/rs18172871 (registering DOI) - 24 Aug 2026

Abstract

Accurate and up-to-date information is essential for environmental monitoring, particularly in regions characterized by complex topography and heterogeneous landscapes. This study presents a multisource remote sensing–based approach for forest vegetation classification on La Palma Island (Canary Islands, Spain), which was further used to [...] Read more.

Accurate and up-to-date information is essential for environmental monitoring, particularly in regions characterized by complex topography and heterogeneous landscapes. This study presents a multisource remote sensing–based approach for forest vegetation classification on La Palma Island (Canary Islands, Spain), which was further used to illustrate its potential for monitoring the temporal dynamics of different forest habitat types. Very high-resolution multispectral data from the WorldView-2/3 satellites were used, complemented by multispectral and LiDAR data acquired by an unmanned aerial vehicle (UAV). Four target forest vegetation types were mapped within a six-class classification scheme that also included “Other vegetation” and “Soil/Others” as non-target/background classes. The performance of ten supervised classification algorithms was evaluated, including Minimum Distance, Mahalanobis Distance, Parallelepiped, Spectral Angle Mapper, Maximum Likelihood, Naïve Bayes, K-Nearest Neighbors, Random Forest, Support Vector Machine, and the transformer-based deep learning model SegFormer. The results indicate that Random Forest achieved the highest overall accuracy, while Support Vector Machine and SegFormer also showed competitive performance, particularly when spectral information was integrated with vegetation indices and topographic variables. The study provides practical evidence on the selection of input data and classifiers for detailed forest vegetation mapping in a large and topographically complex island. Full article

(This article belongs to the Section Forest Remote Sensing)

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22 pages, 7678 KB

Open AccessArticle

Marine Radar Oil Spill Detection Method Based on RBM and Improved Quantum Golden Jackal Optimization Algorithm

by Jin Xu, Boxi Yao, Qiqing Qiu, Bo Li, Yutong Deng, Bo Xu, Zekun Guo and Peng Liu

Remote Sens. 2026, 18(17), 2870; https://doi.org/10.3390/rs18172870 (registering DOI) - 24 Aug 2026

Abstract

Marine ecological systems are severely compromised by oil spill incidents, while oceanic economic growth and public well-being are also put at considerable risk. Therefore, the development of high-efficiency and precise detection techniques for marine pollution prevention and control holds substantial practical importance. A [...] Read more.

Marine ecological systems are severely compromised by oil spill incidents, while oceanic economic growth and public well-being are also put at considerable risk. Therefore, the development of high-efficiency and precise detection techniques for marine pollution prevention and control holds substantial practical importance. A marine radar oil spill detection approach is presented herein, integrating Restricted Boltzmann Machine (RBM) feature learning with the Improved Quantum Golden Jackal Optimization (IQGJO) algorithm. Initially, raw radar imagery is preprocessed. Subsequently, unsupervised deep feature extraction is executed via RBM, and oil spill regions of interest are identified using K-means clustering. Ultimately, the IQGJO algorithm is employed to perform adaptive threshold optimization, along with fine segmentation of oil film targets. When benchmarked against conventional Golden Jackal Optimization (GJO) and gravity search algorithm (GSA), quantum mechanisms, along with Lévy flight perturbation, are incorporated within IQGJO. Consequently, the tendency of traditional approaches to converge toward high-gray local optima is effectively mitigated. Moreover, recognition capability for both weak-reflection and fragmented oil films is substantially enhanced. Experimental outcomes reveal that an accuracy of 99.7% is achieved by the IQGJO algorithm, representing a marked improvement over both GJO and GSA counterparts. High-precision automated segmentation of surface oil spills under intricate maritime conditions is thus enabled, while effective technical guidance is offered for radar-based oil spill surveillance and emergency response. Full article

22 pages, 11479 KB

Open AccessArticle

Hybrid Cloud Segmentation Approach Combining YOLOv8 Instance Segmentation with HSV Thresholding for Multi-Site Assessment

by Augustin Alexandru Besu, Enrique García-Campos, Gabriel López, Mauricio Trigo-González and Joaquín Alonso-Montesinos

Remote Sens. 2026, 18(17), 2869; https://doi.org/10.3390/rs18172869 - 24 Aug 2026

Abstract

Accurate cloud segmentation from ground-based fisheye camera imagery is essential for solar irradiance forecasting and photovoltaic system optimization. Traditional computer vision approaches, such as HSV thresholding and K-means clustering, face significant limitations when applied globally to sky images due to the spectral similarity [...] Read more.

Accurate cloud segmentation from ground-based fisheye camera imagery is essential for solar irradiance forecasting and photovoltaic system optimization. Traditional computer vision approaches, such as HSV thresholding and K-means clustering, face significant limitations when applied globally to sky images due to the spectral similarity between cloud regions and sky areas under varying atmospheric conditions. This study presents a hybrid methodology that leverages YOLOv8 instance segmentation to provide contextual cloud regions followed by refined HSV thresholding within these detected areas. The approach incorporates solar trajectory modeling using pvlib for accurate sun disk detection and exclusion, preventing false cloud classification. The methodology was developed and validated at the CIESOL using Mobotix Q71 fisheye cameras, and later tested in Antofagasta (Chile) and Huelva (Spain). The YOLOv8l-seg model achieved a mask precision of 0.821 and box mAP@0.5 of 0.680 on validation data. The results show a promising correlation with radiometric measurements such as clearness index

k_{t}

and diffuse fraction

k_{d}

in preliminary validation cases. While YOLOv8 demonstrates good cross-site generalization, HSV thresholding requires camera-specific calibration for optimal performance. The method addresses the context-dependency limitations of traditional algorithms, though computational performance and broader validation remain areas for future work. Full article

31 pages, 7589 KB

Open AccessArticle

ACBDT: SAR-Optical Cross-Modal Distillation for Sentinel-1/2 Building-Footprint Mapping in Heterogeneous Yangtze River Delta Cities

by Xianlong Zhang, Bin Pan and Jianhua Li

Remote Sens. 2026, 18(17), 2868; https://doi.org/10.3390/rs18172868 - 24 Aug 2026

Abstract

Medium-resolution building-footprint mapping is limited by two coupled problems: 10 m optical pixels mix roofs with roads and bare surfaces, and SAR observations are degraded by speckle and viewing geometry. We present ACBDT, a Sentinel-1/2 framework that encodes each modality separately, learns a [...] Read more.

Medium-resolution building-footprint mapping is limited by two coupled problems: 10 m optical pixels mix roofs with roads and bare surfaces, and SAR observations are degraded by speckle and viewing geometry. We present ACBDT, a Sentinel-1/2 framework that encodes each modality separately, learns a diffusion-inspired time-step-conditioned fused teacher representation, transforms it through a Cross-Modal Distillation Bridge (CMDB), and refines the output with a Student Refinement Decoder. The time-step variable is used only as a stochastic conditioning index; ACBDT does not implement a forward noising schedule, reverse diffusion, or iterative diffusion sampling. Training and evaluation used 2680 paired 256 × 256 patches over eight Yangtze River Delta cities with a spatially disjoint block partition. In three independent runs on the held-out test partition, ACBDT achieved 85.61 ± 0.32% building IoU, 92.24 ± 0.19% F1, and 83.74 ± 0.34% dataset-level boundary F1, compared with 83.21 ± 0.24% IoU for the strongest baseline, FTransUNet. Repeated-seed ablation showed 79.01 ± 0.42% IoU without CMDB and 84.53 ± 0.20% IoU without time-step conditioning. The separate density diagnostic retained a positive full-minus-optical IoU difference across all five building-density strata. Conclusions are limited to this Yangtze River Delta evaluation; city-held-out and cross-season transfer were not tested. Full article

(This article belongs to the Special Issue Multi-Task Remote Sensing Image Analysis: Classification, Segmentation, and Change Detection (Second Edition))

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22 pages, 5153 KB

Open AccessArticle

Cross-Scale Performance Evaluation of GPM IMERG V07 Precipitation Products in a Typical Mountainous Monsoon Region

by Shaoe Yang, Yanli Chen, Guoxue Xie and Qiting Huang

Remote Sens. 2026, 18(17), 2867; https://doi.org/10.3390/rs18172867 - 24 Aug 2026

Abstract

Satellite precipitation products like GPM IMERG are crucial for hydrological modeling and disaster prevention; yet, their reliability in complex mountainous monsoon regions remains challenging. While the latest IMERG V07 introduces key upgrades, including a Climatological Calibration Algorithm (CCA), its cross-scale error propagation mechanisms [...] Read more.

Satellite precipitation products like GPM IMERG are crucial for hydrological modeling and disaster prevention; yet, their reliability in complex mountainous monsoon regions remains challenging. While the latest IMERG V07 introduces key upgrades, including a Climatological Calibration Algorithm (CCA), its cross-scale error propagation mechanisms and performance heterogeneity in complex underlying surfaces are poorly understood. This study evaluates the daily and monthly performance of IMERG V07 and V06 (Early, Late, and Final Runs) from 2014 to 2020 against 91 rain gauges in Guangxi, China—a typical mountainous monsoon region. The evaluation employs multiple statistical metrics and a multi-dimensional stratification approach based on elevation, precipitation intensity, and seasonality to quantify error propagation and climate-topography coupling effects. The results reveal that V07, particularly the Late Run, enhances daily precipitation detection capabilities, it significantly increases the proportion of systematic positive bias from 62.3 to 64.8% (V06) to 67.2–68.9% (V07). Consequently, upon temporal aggregation to the monthly scale, this systematic overestimation is severely amplified, leading to degraded performance, with the Final Run suffering the most substantial accuracy loss. Furthermore, retrieval accuracy is heavily constrained by surface heterogeneity, with systematic overestimation surging in areas where relatively dry (mean annual precipitation < 1300 mm) and complex terrain (elevation 100–500 m) coincide. The introduced CCA effectively improved dry season estimations but failed during wet season by introducing substantial positive biases. Ultimately, while V07 better captures short-term precipitation dynamics, its structural systematic biases compromise long-term cumulative reliability, highlighting the necessity for physics-based bias correction in hydrological applications and dynamic calibration in future algorithm upgrades. Full article

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26 pages, 6012 KB

Open AccessArticle

Retrieval of Warm-Season Radar Composite Reflectivity in Sichuan by Integrating FY-4A Multi-Channel Satellite Data and DEM Topographic Information

by Wen Kang, Hao Wang, Qiangyu Zeng, Tiantian Yu, Jiafeng Zheng, Zhi Li and Jinzhi Liao

Remote Sens. 2026, 18(17), 2866; https://doi.org/10.3390/rs18172866 - 24 Aug 2026

Abstract

Warm-season precipitation over Sichuan, China, is jointly modulated by complex terrain, monsoon water vapor transport, and local convective activities, leading to significant spatiotemporal heterogeneity. However, radar observations over mountainous areas are frequently impaired by terrain blockage, beam shielding, and insufficient network coverage, which [...] Read more.

Warm-season precipitation over Sichuan, China, is jointly modulated by complex terrain, monsoon water vapor transport, and local convective activities, leading to significant spatiotemporal heterogeneity. However, radar observations over mountainous areas are frequently impaired by terrain blockage, beam shielding, and insufficient network coverage, which cause missing data and spatial discontinuity, thereby restricting the accurate monitoring of precipitation systems. To alleviate these problems, this study develops an Efficient Multi-Scale Attention (EMA) U-Net model integrated with Digital Elevation Model (DEM) information, termed EMA-U-Net-DEM, to retrieve radar composite reflectivity by utilizing multi-channel observations from the Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI). In the experiments, FY-4A AGRI multi-spectral measurements were used as model inputs, while radar composite reflectivity products from the Severe Weather Automatic Nowcasting (SWAN) system were applied as reference labels. The modeling and validation were carried out using warm-season (June–August) datasets over Sichuan Province. The results indicate that the proposed EMA-U-Net-DEM exhibits better performance than the traditional U-Net and several typical attention-based benchmark models. Quantitatively, the model achieves a root mean square error (RMSE) of 6.728 dBZ, a mean absolute error (MAE) of 4.788 dBZ, a coefficient of determination

R^{2}

of 0.656, a peak signal-to-noise ratio (PSNR) of 25.243 dB, and a structural similarity index measure (SSIM) of 0.793. Categorical verification further reveals that the model yields the highest critical success indices (CSI) of 0.850, 0.560, and 0.364 in the reflectivity ranges of 0–25 dBZ, 25–45 dBZ, and 45–70 dBZ, respectively, demonstrating its superior ability in characterizing weak precipitation backgrounds, moderate precipitation structures, and intense convective cores. The performance enhancements are mainly attributed to the strengthened multi-scale feature extraction by the EMA module and the effective topographic constraints introduced by DEM data. This study confirms that the fusion of FY-4A multi-spectral observations and topographic information can effectively improve radar composite reflectivity retrieval over complex terrain, providing a feasible solution for precipitation monitoring, quantitative precipitation estimation, and severe weather nowcasting in mountainous regions with limited radar coverage. Full article

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31 pages, 21987 KB

Open AccessArticle

An Enhanced Nonlinear Grid Transformation Method for Weather Radar Echo Extrapolation

by Tao Yang, Huiling Yang, Yue Sun, Shengchao Li and Zhaowu Liu

Remote Sens. 2026, 18(17), 2865; https://doi.org/10.3390/rs18172865 - 24 Aug 2026

Abstract

In this study, a method capable of simultaneously extrapolating the position, shape, and intensity of weather radar echoes is proposed. As the method is an improved version of the previously proposed nonlinear grid transformation (NGT) method, it is referred to as the enhanced [...] Read more.

In this study, a method capable of simultaneously extrapolating the position, shape, and intensity of weather radar echoes is proposed. As the method is an improved version of the previously proposed nonlinear grid transformation (NGT) method, it is referred to as the enhanced NGT (ENGT) method. By extending the nonlinear transformation matrix to include radar reflectivity as the third dimension in addition to the grid coordinates X and Y, a 3 × 9 transformation matrix is used to describe the continuous spatial variation in the radar reflectivity field. The transformation matrix is solved using historical near-term data, enabling the extrapolation of subsequent time steps. In a set of ideal extrapolation experiments combining translation, temporal increments, and path variations, the ENGT method demonstrated better qualitative and conceptual performance than the NGT and traditional optical flow (OF) methods. In a real squall line case, the ENGT method could predict the overall movement direction of the cloud system synthesized by moving and emerging cells. In a real enhanced convective cloud cluster case, the ENGT method achieved higher scores because it generated stronger reflectivity. Although there are still mathematically unsolved and statistically insignificant problems, the ENGT method shows potential in predicting strong reflectivity, and the computational efficiency for a single weather radar is considerable. Full article

(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)

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28 pages, 17888 KB

Open AccessArticle

Quantitative Assessment of LiDAR Availability in Smoke-Filled Tunnels Using a Degradation Scoring Algorithm

by Marlies Mischinger-Rodziewicz, Pamela Innerwinkler, Relindis Rott, Jan Kowalczyk and Robert Wenighofer

Remote Sens. 2026, 18(17), 2864; https://doi.org/10.3390/rs18172864 - 24 Aug 2026

Abstract

Reliable perception in smoke-filled tunnels is essential for rescue robots, yet the temporal evolution of LiDAR degradation under realistic smoke conditions is not well quantified. This paper investigates the degradation of LiDAR data induced by environmental factors in a full-scale tunnel experiment involving [...] Read more.

Reliable perception in smoke-filled tunnels is essential for rescue robots, yet the temporal evolution of LiDAR degradation under realistic smoke conditions is not well quantified. This paper investigates the degradation of LiDAR data induced by environmental factors in a full-scale tunnel experiment involving three smoke scenarios, with real combustion smoke and theatrical smoke. To enable consistent comparison across experiments with different smoke dynamics, an RGB-based visibility reference is first used for temporal alignment across measurements. Based on this alignment, physically interpretable LiDAR indicators, such as intensity attenuation and range-dependent point density loss, are used to characterize smoke-induced changes in the LiDAR data. In addition, an established Deep semi-supervised anomaly detection (DeepSAD) model is employed to derive a continuous data-driven degradation score that indicates deviations from nominal LiDAR range image patterns. The learned score remains stable under clear-air conditions, despite geometric variations caused by object and sensor movement. During smoke exposure, the score increases in all smoke scenarios, although the temporal evolution differs between scenarios. The results show that the degradation score derived from DeepSAD provides a continuous data-driven description of changes in LiDAR range images under smoke exposure. Overall, the study presents an experimental analysis of LiDAR degradation using full-scale tunnel experiments with smoke, reporting physically interpretable LiDAR indicators and a continuous data-driven degradation score. Full article

(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Fourth Edition))

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Graphical abstract

33 pages, 15233 KB

Open AccessArticle

HNGT-Net: Hard-Negative Guided Topology Transfer for Lightweight Hyperspectral Small-Target Detection

by Ruhan A, Rong Wang, Pengkun Liu and Hang Xiao

Remote Sens. 2026, 18(17), 2863; https://doi.org/10.3390/rs18172863 - 24 Aug 2026

Abstract

Detecting small anomalous targets in hyperspectral imagery is challenging when neither target spectra nor spatial morphologies are known, because most unsupervised detectors still encode implicit assumptions about background statistics or target structures and therefore generalize poorly to genuinely unknown threats. This paper introduces [...] Read more.

Detecting small anomalous targets in hyperspectral imagery is challenging when neither target spectra nor spatial morphologies are known, because most unsupervised detectors still encode implicit assumptions about background statistics or target structures and therefore generalize poorly to genuinely unknown threats. This paper introduces the Hard-Negative-Guided Topology Transfer Network (HNGT-Net), a lightweight teacher–student framework that addresses this problem from two perspectives. On the representation side, a similarity graph coupling spatial adjacency with feature-space nearest neighbors characterizes the spectral–spatial topology of normal backgrounds, and a three-level consistency objective over node embeddings, structural relations, and graph-Laplacian responses transfers this topology from a frozen teacher to a compact student. On the discrimination side, hard negatives are synthesized directly on pure normal pixels through sparsity-gated projected gradient perturbation, while a response-margin constraint forces the student to score such negatives above normal samples and mitigates distribution overfitting. Experiments on four public benchmark scenes and one Salinas-derived synthetic dataset show that HNGT-Net achieves an average AUC of 0.9953 with the smallest variance among all competitors. The student branch provides 10.5-fold parameter compression relative to the teacher, while the complete teacher–student stack remains compact, supporting resource-constrained remote sensing deployment. Full article

(This article belongs to the Special Issue The Recent Progression of Machine Learning in Remote Sensing: Theory and Modelling (Second Edition))

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20 pages, 22108 KB

Open AccessArticle

Aerosol Optical Depth Retrieval from MODIS Using a Physically Informed Machine Learning Framework

by Tianchen Liang, Linqing Zou, Qiaoning He and Lin Sun

Remote Sens. 2026, 18(17), 2862; https://doi.org/10.3390/rs18172862 - 24 Aug 2026

Abstract

Retrieving aerosol optical depth (AOD) over land remains challenging because the relatively weak aerosol signal in top-of-atmosphere (TOA) observations must be separated from strong and spatially heterogeneous surface reflectance. Here, we develop a physically informed random forest framework for global 1 km land [...] Read more.

Retrieving aerosol optical depth (AOD) over land remains challenging because the relatively weak aerosol signal in top-of-atmosphere (TOA) observations must be separated from strong and spatially heterogeneous surface reflectance. Here, we develop a physically informed random forest framework for global 1 km land AOD retrieval from MODIS. The framework integrates multispectral TOA reflectance, surface properties, observation geometry, meteorological conditions, topography, and physically informed aerosol–surface features. Long-term Aerosol Robotic Network (AERONET) observations from 2001 to 2017 were collocated with MODIS and ancillary datasets for model development and evaluation. Two physically informed features were introduced to improve retrieval robustness across diverse aerosol and surface conditions, including minimum AOD derived from long-term AERONET observations and time-series clear-sky reflectance (TSCR) in the blue, red, and shortwave-infrared bands derived using the 6S radiative-transfer model. Independent retrieval evaluation for 2013–2014 showed good agreement with AERONET observations, with R = 0.81, MAE = 0.063, RMSE = 0.096, and 74.93% of matched samples falling within the MODIS land expected-error envelope, although increasing underestimation was observed at high aerosol loading (AOD > 1). The proposed retrievals also showed better agreement with AERONET than the MOD04 Dark Target and Deep Blue products. These results demonstrate the value of incorporating physically interpretable aerosol-background and surface-reflectance information into data-driven retrievals for AOD over land surfaces. Full article

(This article belongs to the Special Issue Observation of Optical and Radiative Properties of Atmospheric Aerosols by Ground-Based Remote Sensing Measurements)

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23 pages, 5760 KB

Open AccessArticle

HSAR-DETR: Hierarchical Spatial–Frequency Attention Network for UAV Small Object Detection

by Cheng Zhang and Zhibo Guo

Remote Sens. 2026, 18(17), 2861; https://doi.org/10.3390/rs18172861 - 24 Aug 2026

Abstract

Small object detection in UAV remote sensing imagery plays a crucial role in applications such as infrastructure inspection, disaster assessment, and precision agriculture, where targets of interest frequently occupy fewer than

32 \times 32

pixels under large ground sampling distance variation and complex [...] Read more.

Small object detection in UAV remote sensing imagery plays a crucial role in applications such as infrastructure inspection, disaster assessment, and precision agriculture, where targets of interest frequently occupy fewer than

32 \times 32

pixels under large ground sampling distance variation and complex cluttered backgrounds. Existing methods still face three main challenges in UAV small-object detection: fine-grained detail loss caused by repeated downsampling, feature inconsistency during cross-scale fusion, and unstable boundary regression in densely distributed aerial scenes. To address these issues, this paper proposes HSAR-DETR, a detection framework that jointly improves hierarchical feature representation, cross-scale refinement, and geometry-aware localization. Specifically, a Hierarchical Enhancement Network (HENet) is introduced to preserve shallow spatial details while strengthening deep semantic-context representation. A Dual-Stream Feature Refinement module (DSFR) is designed at the P4-to-P3 fusion stage, combining spatial-domain structural modeling with frequency-domain phase refinement to improve cross-scale feature consistency. A Coordinate-Guided Adaptive Convolution module (CGAC) is further deployed before the detection head, converting coordinate-guided offset magnitudes into modulation weights for adaptive feature recalibration and improved localization stability. In addition, a conventional high-resolution P2 detection branch is incorporated to enhance small-object representation. Experimental results on the VisDrone, RSOD, and TinyPerson datasets demonstrate improved detection performance. On the VisDrone validation set, HSAR-DETR achieves 50.8%

m A P_{50}

and 31.4%

m A P_{50 : 95}

, outperforming the RT-DETR baseline by 4.2 and 3.0 percentage points, respectively. Full article

(This article belongs to the Special Issue Small Target Detection, Recognition, and Tracking in Remote Sensing)

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24 pages, 51622 KB

Open AccessArticle

CL-LGFM: Early-Season Winter Wheat Mapping by Integrating Sentinel-2 NDVI and GPM Precipitation Data—A Case Study in the Chaohu Basin, China

by Ning Su, Peng Li, Huiliang Yang, Fei Lin, Yimin Hu and Taosheng Xu

Remote Sens. 2026, 18(17), 2860; https://doi.org/10.3390/rs18172860 - 24 Aug 2026

Abstract

Early-season winter wheat mapping is crucial for agricultural management and food security, but reliable identification remains challenging under weak spectral conditions during early growth stages. To address this challenge, this study developed a CNN–LSTM with a lag-aware gated fusion model (CL-LGFM) for winter [...] Read more.

Early-season winter wheat mapping is crucial for agricultural management and food security, but reliable identification remains challenging under weak spectral conditions during early growth stages. To address this challenge, this study developed a CNN–LSTM with a lag-aware gated fusion model (CL-LGFM) for winter wheat mapping in the Chaohu Basin, China, using a reconstructed 5-day Sentinel-2 NDVI time series and precipitation data from the Global Precipitation Measurement (GPM) mission. The model employs a dual-branch architecture to jointly learn vegetation and precipitation features and introduces a lag-aware dynamic gated fusion module to capture the delayed response of vegetation to precipitation and enhance multi-source feature fusion. The results show that the proposed method achieved reliable early-season winter wheat mapping (OA ≥ 0.90, Kappa ≥ 0.80) on 26 January, at least 10 days earlier than traditional methods, including SVM, RF, DTW, and TCN, using the same reconstructed 5-day NDVI time series. Optimal performance uses a 7 × 7 patch size and 30-day precipitation window. Full article

(This article belongs to the Special Issue Machine Learning for Feature Extraction and Classification in Remote Sensing Images)

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44 pages, 49336 KB

Open AccessArticle

Digital Mapping of Soil and Water Indicators in Arid Regions Driven by High-Dimensional Environmental Covariates: A Comprehensive Evaluation of Metaheuristic Feature Selection and Hybrid Deep Learning Frameworks

by Yang Wei, Hongjiang Hu, Rongrong Li, Xiaojing Li and Fei Wang

Remote Sens. 2026, 18(17), 2859; https://doi.org/10.3390/rs18172859 - 23 Aug 2026

Abstract

High-dimensional environmental covariates are increasingly available for digital soil mapping (DSM), but their effective use depends on both the feature-selection strategy and the predictive model architecture. However, systematic evidence remains limited regarding how different metaheuristic feature-selection methods interact with standalone and hybrid learning [...] Read more.

High-dimensional environmental covariates are increasingly available for digital soil mapping (DSM), but their effective use depends on both the feature-selection strategy and the predictive model architecture. However, systematic evidence remains limited regarding how different metaheuristic feature-selection methods interact with standalone and hybrid learning models across multiple soil and groundwater prediction tasks. This study systematically evaluated the interactions between 10 metaheuristic feature-selection algorithms and 13 predictive models, including random forest (RF), convolutional neural network (CNN), recurrent architectures, CNN–recurrent neural network (RNN) hybrids, squeeze-and-excitation (SE)-enhanced hybrids, and iTransformer-based hybrids, across four prediction tasks involving soil organic carbon (SOC), soil–water extract electrical conductivity (ECe), apparent electrical conductivity (ECa), and groundwater level (GWL) in Xinjiang, China. A total of 149 candidate environmental covariates were considered for ECe, SOC, and ECa, whereas 122 candidate covariates were considered for GWL. The results showed that no single feature-selection method consistently performed best across all four targets; instead, predictive performance depended on the interaction among the feature-selection strategy, predictive architecture, and target variable. CNN–RNN hybrid architectures generally achieved higher predictive performance than standalone models, although their benefits varied among prediction targets. The best-performing combinations yielded coefficient of determination (R²) values of 0.9826, 0.6981, 0.8429, and 0.8085 for GWL, SOC, ECe, and ECa, respectively. These findings indicate that target-specific compatibility, rather than aggressive dimensionality reduction or a universally superior algorithm, is a key determinant of predictive performance in high-dimensional DSM. By demonstrating that feature-selection effectiveness is jointly influenced by model architecture and target characteristics, this study provides a methodological reference for developing target-specific digital soil mapping models in arid regions. Full article

(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring (Second Edition))

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20 pages, 37147 KB

Open AccessArticle

Spatio-Temporal Dynamics of Mining-Induced Surface Disturbance and Backfilling in Open-Pit Coal Mines Across China’s Arid and Desert Regions (1990–2023)

by Yaling Xu, Chengye Zhang, Jun Li, Li Guo and Lijun Pu

Remote Sens. 2026, 18(17), 2858; https://doi.org/10.3390/rs18172858 - 23 Aug 2026

Abstract

Open-pit coal mining in arid and desert regions causes extensive and persistent surface disturbance, yet long-term monitoring of disturbance and backfilling processes remains challenging. Existing time-series change detection approaches can identify spectral changes but provide limited information on mining disturbance types and their [...] Read more.

Open-pit coal mining in arid and desert regions causes extensive and persistent surface disturbance, yet long-term monitoring of disturbance and backfilling processes remains challenging. Existing time-series change detection approaches can identify spectral changes but provide limited information on mining disturbance types and their evolution pathways. To address this issue, an automated surface disturbance detection method (Auto-SD) was developed for open-pit coal mines in arid and desert environments. This method integrates disturbance-type identification and temporal information extraction using the tasseled cap brightness (TCB) component to characterize changes associated with surface material exposure and accumulation. Using Landsat imagery from 1990 to 2023, Auto-SD was applied to 89 open-pit coal mines in China’s arid and desert regions, achieving an overall classification accuracy of 0.84. The cumulative disturbed area reached 423.10 km², while the internal dumping area reached 94.25 km², indicating limited backfilling recovery. Disturbance intensified after 2006, whereas backfilling lagged behind, forming a trajectory of rapid expansion, delayed recovery, and gradual stabilization. Spatially, mining areas exhibited a progressive transition from external dumping to internal dumping and backfilling. Furthermore, cumulative pit area generally followed an S-shaped growth pattern with mining duration. These findings provide new insights into long-term mining landscape evolution and support ecological restoration assessment and sustainable resource management in arid mining regions. Full article

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30 pages, 31042 KB

Open AccessArticle

Cross-Domain Mixup for Parcel-Level Crop Mapping on a Multi-Year Sentinel-2 Dataset from Slovakia

by Antonela-Adelina Dinescu and Corneliu Florea

Remote Sens. 2026, 18(17), 2857; https://doi.org/10.3390/rs18172857 - 23 Aug 2026

Abstract

Reliable crop-type mapping from satellite image time series is affected by distribution shifts across geographic regions, agricultural years, and heterogeneous label systems. To address this challenge, we propose Cross-Domain Mixup (CDMix), a supervised domain-adaptation method designed to leverage a larger labeled source dataset [...] Read more.

Reliable crop-type mapping from satellite image time series is affected by distribution shifts across geographic regions, agricultural years, and heterogeneous label systems. To address this challenge, we propose Cross-Domain Mixup (CDMix), a supervised domain-adaptation method designed to leverage a larger labeled source dataset to improve performance on a smaller labeled target dataset under distribution shifts. We also introduce PixelSet-Slovakia, a new multi-year, parcel-level Sentinel-2 dataset covering three Slovak study regions and several growing seasons. Using a common backbone, we compare CDMix against three families of adaptation strategies: (i) no adaptation, (ii) weight transfer through fine-tuning and encoder freezing, and (iii) feature-space alignment using Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL). All methods are evaluated in two scenarios: geographic supervised adaptation across datasets from two countries and temporal supervised adaptation across different growing seasons. Across both tested source–target settings, CDMix generally achieves competitive performance when initialized from pretrained representations. Under the region-held-out validation protocol, several pretrained adaptation strategies outperform training from scratch. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based Remote Sensing for Crop Information Extraction and Status Monitoring)

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27 pages, 29057 KB

Open AccessArticle

Spatiotemporal Dynamics and Climatic Responses of Rubber Plantations’ Aboveground Biomass in Western Hainan Island Based on Multi-Source Remote Sensing and Explainable Machine Learning

by Xiaoxiao Zhang, Jinyao Xing, Wenfeng Gong, Mingjiang Mao, Miao Wang, Jing Chen, Jiaxin Ouyang, Renhao Chen and Junting Jia

Remote Sens. 2026, 18(17), 2856; https://doi.org/10.3390/rs18172856 - 23 Aug 2026

Abstract

The dynamics of aboveground biomass (AGB) in rubber plantations (RPs) provide an important basis for evaluating carbon stocks and environmental adaptability in tropical plantations. However, continuous monitoring of AGB of RPs at the regional scale is lacking, and its nonlinear responses to hydrothermal [...] Read more.

The dynamics of aboveground biomass (AGB) in rubber plantations (RPs) provide an important basis for evaluating carbon stocks and environmental adaptability in tropical plantations. However, continuous monitoring of AGB of RPs at the regional scale is lacking, and its nonlinear responses to hydrothermal conditions remain insufficiently understood. This study focused on RPs in western Hainan Island (WHI), including Danzhou, Baisha, Lingao, and Chengmai, and integrated field plot data with multi-source remote sensing datasets. A framework for mapping RPs combining rule-based constraints and phenology-based random forest (RF) classification was developed. After key variable screening, extreme gradient boosting (XGBoost), Shapley additive explanations (SHAP), and generalized additive model (GAM) were used for AGB estimation and identification of climatic responses. The results showed that mapping of RPs achieved an overall accuracy of 92.89% and a Kappa coefficient of 0.854. The XGBoost-derived estimates showed that AGB of RPs in the study area increased by approximately 1.43 × 10⁶ Mg from 2017 to 2025, with growth areas mainly concentrated in the Danzhou–Baisha and western Chengmai. AGB exhibited significant nonlinear responses to climatic factors. Specifically, the effect of precipitation (PRE) shifted to negative after approximately 1945 mm yr⁻¹, whereas annual mean maximum temperature (TMAX) shifted to a positive effect after about 29.72 °C, although this effect gradually weakened as temperature continued to rise. Combinations such as PRE × annual mean temperature (PRE × TMP), PRE × TMAX, and PRE × potential evapotranspiration (PRE × PET) exhibited significant nonlinear interactions, indicating that the direction and magnitude of the effect of PRE shifted with changes in temperature and PET levels. These findings link the spatiotemporal changes in AGB of RPs in WHI with hydrothermal thresholds and their interacting effects, deepening our understanding of the climatic response characteristics of AGB in RPs in this region. They also provide a scientific basis for RP monitoring, carbon stock assessment, and climate-adaptive management in WHI. Full article

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24 pages, 5855 KB

Open AccessArticle

Reliability-Aware Adaptive Band Gating with Domain Expansion for Cross-Scene Hyperspectral Band Selection

by Huaixi Zhu, Fang Gao, Tong Zhu, Ran Zhou, Jiaoyang Xing, Jingyan Fan, Mingzhong Pan, Peipei Fang and Yikun Wang

Remote Sens. 2026, 18(17), 2855; https://doi.org/10.3390/rs18172855 - 23 Aug 2026

Abstract

Cross-scene hyperspectral band selection must reduce spectral redundancy while retaining channels that remain useful beyond the source scene. We propose Adaptive Band Gating (ABG), a source-only selector that combines frequency-domain decoupling enhancement, global and sample-specific gating, source-side spectral, spatial, morphology-inspired, and sensor-noise perturbations, [...] Read more.

Cross-scene hyperspectral band selection must reduce spectral redundancy while retaining channels that remain useful beyond the source scene. We propose Adaptive Band Gating (ABG), a source-only selector that combines frequency-domain decoupling enhancement, global and sample-specific gating, source-side spectral, spatial, morphology-inspired, and sensor-noise perturbations, and a dual-head evaluator. The selector is trained with source data and frozen before downstream evaluation. Selected bands are assessed with a radial-basis-function support vector machine on Pavia Center and HyRANK under fixed band budgets and target-label fractions from 0% to 10%. At 5% target labels, 15 selected bands achieve 96.05% overall accuracy on Pavia Center, compared with 95.41% using all 102 bands; 20 selected bands achieve 81.49% on HyRANK, compared with 79.20% using all 176 bands. Across evaluated band budgets, ABG is comparable to XGBS on Pavia Center and provides stronger results on HyRANK. Ablation experiments show that adaptive gating, frequency-domain enhancement, and source-side expansion each contribute to performance. Together, these results demonstrate that ABG learns compact and traceable original-band subsets with strong downstream transfer utility across the evaluated cross-scene settings. Full article

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

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23 pages, 10260 KB

Open AccessArticle

A Novel Calibration Method for Networked X-Band Radar Based on Opposing RHI Scans

by Hui Wang, Siteng Li, Yue Lai, Yu Wang, Jingheng Zhou and Jiping Quan

Remote Sens. 2026, 18(17), 2854; https://doi.org/10.3390/rs18172854 - 23 Aug 2026

Abstract

Weather radar calibration is essential for ensuring data consistency and quantitative precipitation estimation in X-band radar networks. Existing absolute calibration methods (e.g., metal sphere, horn antenna) suffer from high cost, poor timeliness, and difficulty in automation due to meteorological conditions and airspace restrictions, [...] Read more.

Weather radar calibration is essential for ensuring data consistency and quantitative precipitation estimation in X-band radar networks. Existing absolute calibration methods (e.g., metal sphere, horn antenna) suffer from high cost, poor timeliness, and difficulty in automation due to meteorological conditions and airspace restrictions, while spatiotemporal matching methods based on volume scan data suffer from interpolation and matching inaccuracies. To address these issues, this study proposes a collaborative calibration method for X-band radar networks based on opposing Range–Height Indicator (RHI) scans. The method uses a rigorously calibrated reference radar as a benchmark and performs opposing RHI scans with the radar under calibration to obtain synchronized observations within the spatial overlap region. Precise spatial matching is achieved using the nearest-neighbor algorithm based on beam-broadening cross-coverage thresholds, and bias is extracted using both the midline 9-point averaging method (midline method) and spatially constrained regional Statistics method (regional method). Based on a total of 58 sets of opposing RHI scanning cases conducted under stratiform precipitation, scattered precipitation, and weak cloud conditions, the results show that under conditions where echo continuity is maintained near the midline of stratiform and scattered precipitation, both the midline method and the regional method can obtain stable matching data. The midline method achieves a median correlation coefficient (0.821–0.942) higher than that of the regional method (0.860–0.872), and its bias standard deviation remains relatively stable (midline method: 1.39–2.20 dB; regional method: 2.61–3.16 dB). Continuous RHI calibration tests confirm that within a 30-min window, the fluctuation of the data matching correlation coefficient is less than 0.05, and the fluctuation of the bias mean is controlled within ±0.3 dB. Under weak cloud conditions, although the midline method can still achieve a high correlation coefficient, the correctness of its results still requires auxiliary validation through other calibration means. This study provides a relatively efficient and effective technical approach for the automated collaborative calibration of dense X-band radar networks. Full article

(This article belongs to the Special Issue Radar Technologies for Meteorological and Atmospheric Observations)

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20 pages, 59438 KB

Open AccessArticle

Three-Dimensional Displacement Analysis and Statistical Modeling of the Pubugou Rockfill Dam Using Multi-Track InSAR

by Ping Bao, Xuguo Shi, Weitao Han and Yuanzheng Cui

Remote Sens. 2026, 18(17), 2853; https://doi.org/10.3390/rs18172853 - 23 Aug 2026

Abstract

Long-term displacement in ultra-high rockfill dams is spatially heterogeneous, yet point-based monitoring and single-track InSAR provide only a limited account of its three-dimensional evolution. To address this limitation, we develop a workflow combining multi-track three-dimensional InSAR reconstruction, temporal feature clustering and modified HST/HTT [...] Read more.

Long-term displacement in ultra-high rockfill dams is spatially heterogeneous, yet point-based monitoring and single-track InSAR provide only a limited account of its three-dimensional evolution. To address this limitation, we develop a workflow combining multi-track three-dimensional InSAR reconstruction, temporal feature clustering and modified HST/HTT modeling. In the modified models, the conventional linear time term is replaced by a predominant displacement component, allowing nonlinear long-term behavior to be represented alongside hydraulic and thermal responses. Three Sentinel-1 tracks acquired between 2014 and 2023 were used to analyze the Pubugou Dam. Maximum vertical and eastward displacement rates reached 17.70 and 19.41 mm/yr, respectively, while cumulative vertical displacement reached approximately 178 mm. Three coherent displacement zones were resolved: the upper dam was dominated by long-term accumulation, the central section displayed the strongest periodic response, and the lower dam and abutments remained comparatively stable. The modified models reproduced the observed series more closely in sample than the conventional formulations, while the fitted coefficients indicated a stronger and more spatially variable response to reservoir level than to air temperature. This integrated analysis provides a spatially resolved account of long-term displacement and environmental response across an ultra-high rockfill dam. Full article

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

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