Remote Sensing

29 pages, 4822 KB

Open AccessArticle

Depth-Specific Prediction of Coastal Soil Salinization Using Multi-Source Environmental Data and an Optimized GWO–RF–XGBoost Ensemble Model

by Yuanbo Wang, Xiao Yang, Xingjun Lv, Wei He, Ming Shao, Hongmei Liu and Chao Jia

Remote Sens. 2025, 17(24), 4043; https://doi.org/10.3390/rs17244043 - 16 Dec 2025

Abstract

Soil salinization is an escalating global concern threatening agricultural productivity and ecological sustainability, particularly in coastal regions where complex interactions among hydrological, climatic, and anthropogenic factors govern salt accumulation. The vertical differentiation and spatial heterogeneity of salinity drivers remain poorly resolved. We present [...] Read more.

Soil salinization is an escalating global concern threatening agricultural productivity and ecological sustainability, particularly in coastal regions where complex interactions among hydrological, climatic, and anthropogenic factors govern salt accumulation. The vertical differentiation and spatial heterogeneity of salinity drivers remain poorly resolved. We present an integrated modeling framework combining ensemble machine learning and spatial statistics to investigate the depth-specific dynamics of soil salinity in the Yellow River Delta, a vulnerable coastal agroecosystem. Using multi-source environmental predictors and 220 field samples harmonized to 30 m resolution, the hybrid Gray Wolf Optimizer–Random Forest–XGBoost model achieved high predictive accuracy for surface salinity (R² = 0.91, RMSE = 0.03 g/kg, MAE = 0.02 g/kg). Spatial autocorrelation analysis (Global Moran’s I = 0.25, p < 0.01) revealed pronounced clustering of high-salinity hotspots associated with seawater intrusion pathways and capillary rise. The results reveal distinct vertical control mechanisms: vegetation indices and soil water content dominate surface salinity, while total dissolved solids (TDS), pH, and groundwater depth increasingly influence middle and deep layers. By applying SHAP (SHapley Additive Explanations), we quantified nonlinear feature contributions and ranked key predictors across layers, offering mechanistic insights beyond conventional correlation. Our findings highlight the importance of depth-specific monitoring and intervention strategies and demonstrate how explainable machine learning can bridge the gap between black-box prediction and process understanding. This framework offers a generalizable framework that can be adapted to other coastal agroecosystems with similar hydro-environmental conditions. Full article

(This article belongs to the Topic Water Management in the Age of Climate Change)

35 pages, 18756 KB

Open AccessArticle

Monitoring Rubber Plantation Distribution and Biomass with Sentinel-2 Using Deep Learning and Machine Learning Algorithm (2019–2024)

by Yingtan Chen, Jialong Duanmu, Zhongke Feng, Jun Qian, Zhikuan Liu, Huiqing Pei, Pietro Grimaldi and Zixuan Qiu

Remote Sens. 2025, 17(24), 4042; https://doi.org/10.3390/rs17244042 - 16 Dec 2025

Abstract

The number of rubber plantations has increased significantly since 2000, especially in Southeast Asia and China, and their ecological impacts are becoming more evident. A robust rubber supply monitoring system is currently required at both the production and ecological levels. This study used [...] Read more.

The number of rubber plantations has increased significantly since 2000, especially in Southeast Asia and China, and their ecological impacts are becoming more evident. A robust rubber supply monitoring system is currently required at both the production and ecological levels. This study used Sentinel-2 multi-rule remote sensing images and a deep learning method to construct a deep learning model that could generate a distribution map of rubber plantations in Danzhou City, Hainan Province, from 2019 to 2024. For biomass modeling, 52 sample plots (27 of which were historical plots) were integrated, and the canopy structure was extracted as an auxiliary variable from the point cloud data generated by an unmanned aerial vehicle survey. Five algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree, Convolutional Neural Network, Back Propagation Neural Network, and Extreme Gradient Boosting, were used to characterize the spatiotemporal changes in rubber plantation biomass and analyze the driving mechanisms. The developed deep learning model was exceptional at identifying rubber plantations (overall accuracy = 91.63%, Kappa = 0.83). The RF model performed the best in terms of biomass prediction (R² = 0.72, RRMSE = 21.48 Mg/ha). Research shows that canopy height as a characteristic factor enhances the explanatory power and stability of the biomass model. However, due to limitations such as sample plot size, image differences, canopy closure degree, and point cloud density, uncertainties in its generalization across years and regions remain. In summary, the proposed framework effectively captures the spatial and temporal dynamics of rubber plantations and estimates their biomass with high accuracy. This study provides a crucial reference for the refined management and ongoing monitoring of rubber plantations. Full article

(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring (Second Edition))

29 pages, 5197 KB

Open AccessSystematic Review

Mapping Reef Island Shoreline Changes: A Systematic Review of Data Sources and Methods

by Maria Kottermair, Stuart R. Phinn, Chris Roelfsema and Daniel Harris

Remote Sens. 2025, 17(24), 4041; https://doi.org/10.3390/rs17244041 - 16 Dec 2025

Abstract

Reef islands are small, low-lying landforms composed of unconsolidated bioclastic materials and are highly vulnerable to coastal hazards exacerbated by climate change. This vulnerability has driven extensive research interest in shoreline changes across temporal scales ranging from short-term (seasonal) to long-term (decadal) dynamics. [...] Read more.

Reef islands are small, low-lying landforms composed of unconsolidated bioclastic materials and are highly vulnerable to coastal hazards exacerbated by climate change. This vulnerability has driven extensive research interest in shoreline changes across temporal scales ranging from short-term (seasonal) to long-term (decadal) dynamics. In this review, we first conducted an exploratory search of publicly available databases to assess the global distribution of reef islands and their potential for providing baseline data. Based on the PRISMA 2020 framework, we then examined 74 studies to identify data sources and methods commonly used to analyse reef island shoreline changes. Our findings indicate that no global dataset currently exists that specifically identifies reef islands, despite the potential value of such a dataset. Shoreline changes have been assessed for over 91 atolls and 119 non-atoll reef islands (excluding a global study) spanning the Pacific, Indian, and Atlantic Oceans. However, inconsistencies in time spans, reporting practices, and error assessments make cross-study comparisons challenging. Analysis of data sources revealed that 40% of studies were purely desktop-based, while only 11% relied solely on field data. Most used a combination of remote sensing and field-based approaches. Emerging technologies such as drones and LiDAR remain underutilised in reef island research, although they provide promising opportunities for high-resolution mapping and monitoring. This review provides a methodological framework to guide future research on reef island shoreline changes. Full article

(This article belongs to the Special Issue Remote Sensing Application in Coastal Geomorphology and Processes II)

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26 pages, 7144 KB

Open AccessArticle

Slight Change, Huge Loss: Spatiotemporal Evolution of Ecosystem Services and Driving Factors in Inner Mongolia, China

by Zherui Yin, Wenhui Kuang, Geer Hong, Yali Hou, Changqing Guo, Wenxuan Bao, Zhishou Wei and Yinyin Dou

Remote Sens. 2025, 17(24), 4040; https://doi.org/10.3390/rs17244040 - 16 Dec 2025

Abstract

The spatiotemporal evolution of ecosystem services has a profound influence on the fragile eco-environment in Inner Mongolia and the arid/semi-arid and the ecological barrier regions of Northern China; in particular, the small-scale and high-value land variables may lead to large eco-environment effects through [...] Read more.

The spatiotemporal evolution of ecosystem services has a profound influence on the fragile eco-environment in Inner Mongolia and the arid/semi-arid and the ecological barrier regions of Northern China; in particular, the small-scale and high-value land variables may lead to large eco-environment effects through altering the ecosystem services, which is still unclear in this vulnerable area. The differential driving mechanism of both human activities and natural factors on ecosystem services also needs to be revealed. To solve this scientific issue, the synergistic methodology of spatial analysis technology, the improved ecosystem service assessment method, flow gain/loss model, global/local Moran’s I approach, and the Geographically and Temporally Weighted Regression (GTWR) model were applied. Our main results are as follows: remote sensing monitoring showed that the land changes featured a persistent expansion of cropland and built-up areas, with a decline in grassland and wetland, along the east–west gradient from forests, grasslands, and unused-lands, to become the dominant cover type. According to our improved model, the ecosystem services considering the internal structure of build-up lands were first investigated in this ecologically fragile area of China, and the evaluated ecosystem service value (ESV) reduced from CNY 5515.316 billion to CNY 5425.188 billion, with an average annual decrease of CNY 3.004 billion from 1990 to 2020. Another finding was that the small-scale land variables with large ecological service impacts were quantified; namely, the proportion of grassland, woodland, wetland, and water body decreased from 62.71% to 61.34%, with only a relatively minor fluctuation of −1.37%, but this decline resulted in a large ESV loss of CNY 116.141 billion from 1990 to 2020. From the driving perspective, the temperature, digital elevation model (DEM), and slope exhibited negative effects on ESV changes, whereas a positive association was analyzed in terms of the precipitation and human footprint during the studied period. This study provides important support for optimizing land resource allocation, guiding the development of agriculture and animal husbandry, and protecting the ecological environment in arid/semi-arid and ecological barrier regions. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities (Second Edition))

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41 pages, 1981 KB

Open AccessArticle

A Sustainable Agricultural Development Index (SADI): Bridging Soil Health, Management, and Socioeconomic Factors

by Gabriel Pimenta Barbosa de Sousa, José Alexandre Melo Demattê, Sabine Chabrillat, Robert Milewski, Raul Roberto Poppiel, Merilyn Taynara Accorsi Amorim, Bruno dos Anjos Bartsch, Jorge Tadeu Fim Rosas, Maurício Roberto Cherubin, Yuxin Ma, Roney Berti de Oliveira, Marcos Rafael Nanni and Renan Falcioni

Remote Sens. 2025, 17(24), 4039; https://doi.org/10.3390/rs17244039 - 16 Dec 2025

Abstract

Soil Health (SH) is a key concept in discussions on sustainable land use, with implications that extend beyond agriculture. To address the need for integrated assessments, this study developed a Sustainable Agricultural Development Index (SADI) by combining the Soil Health Index (SHI) with [...] Read more.

Soil Health (SH) is a key concept in discussions on sustainable land use, with implications that extend beyond agriculture. To address the need for integrated assessments, this study developed a Sustainable Agricultural Development Index (SADI) by combining the Soil Health Index (SHI) with socioeconomic and management indicators. The analysis was conducted across Germany using 3300 soil analysis sites and environmental covariates, including climate, topography, vegetation indices, and bare soil reflectance. From this foundation, SADI was designed to evaluate agricultural sustainability across German states based on three dimensions: Management (Bare Soil Frequency), Environment (SHI Maps), and Economy (Profit per Hectare). Results revealed that SHI correlated significantly with land surface temperature (R = −0.47), bare soil frequency (R = −0.40), and vegetation indices (R = 0.43). Soil organic carbon also played a key role in explaining degradation patterns. While economically stronger states tended to achieve higher SH scores, environmentally sound and well-managed regions also performed well despite lower economic returns. These findings emphasize that sustainable agriculture depends on balancing economic growth, environmental integrity, and management efficiency. The SADI provides a comprehensive framework for policymakers and land managers to evaluate and guide sustainable agricultural development. Full article

29 pages, 33246 KB

Open AccessArticle

Regional Forest Wildfire Mapping Through Integration of Sentinel-2 and Landsat 8 Data in Google Earth Engine with Semi-Automatic Training Sample Generation

by Yue Chen, Weili Kou, Xiong Yin, Rui Wang, Jiangxia Ye and Qiuhua Wang

Remote Sens. 2025, 17(24), 4038; https://doi.org/10.3390/rs17244038 - 16 Dec 2025

Abstract

Accurate mapping of burned forest areas in mountainous regions is essential for wildfire assessment and post-fire ecological management. This study develops an FS-SNIC-ML workflow that integrates multi-source optical fusion, semi-automatic sample generation, feature selection, and object-based machine-learning classification to support reliable burned-area mapping [...] Read more.

Accurate mapping of burned forest areas in mountainous regions is essential for wildfire assessment and post-fire ecological management. This study develops an FS-SNIC-ML workflow that integrates multi-source optical fusion, semi-automatic sample generation, feature selection, and object-based machine-learning classification to support reliable burned-area mapping under complex terrain conditions. A pseudo-invariant feature (PIFS)-based fusion of Sentinel-2 and Landsat 8 imagery was employed to generate cloud-free, gap-free, and spectrally consistent pre- and post-fire reflectance datasets. Burned and unburned samples were constructed using a semi-automatic SAM–GLCM–PCA–Otsu procedure and county-level stratified sampling to ensure spatial representa-tiveness. Feature selection using LR, RF, and Boruta identified dNBR, dNDVI, and dEVI as the most discriminative variables. Within the SNIC-supported GEOBIA framework, four classifiers were evaluated; RF performed best, achieving overall accuracies of 92.02% for burned areas and 94.04% for unburned areas, outperforming SVM, CART, and KNN. K-means clustering of dNBR revealed spatial variation in fire conditions, while geographical detector analysis showed that NDVI, temperature, soil moisture, and their pairwise interactions were the dominant drivers of wildfire hotspot density. The proposed workflow provides an effective and transferable approach for high-precision burned-area extraction and quantification of wildfire-driving factors in mountainous forest regions. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Burned Area Mapping)

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23 pages, 27042 KB

Open AccessArticle

MSDF-Mamba: Mutual-Spectrum Perception Deformable Fusion Mamba for Drone-Based Visible–Infrared Cross-Modality Vehicle Detection

by Jiashuo Shen, Jun He, Qiuyu Liu, Zhilong Zhang, Guoyan Wang and Dawei Lu

Remote Sens. 2025, 17(24), 4037; https://doi.org/10.3390/rs17244037 - 15 Dec 2025

Abstract

To ensure all-day detection performance, unmanned aerial vehicles (UAVs) usually need both visible and infrared images for dual-modality fusion object detection. However, misalignment between the RGB-IR image pairs and complexity of fusion models constrain the fusion detection performance. Specifically, typical alignment methods choose [...] Read more.

To ensure all-day detection performance, unmanned aerial vehicles (UAVs) usually need both visible and infrared images for dual-modality fusion object detection. However, misalignment between the RGB-IR image pairs and complexity of fusion models constrain the fusion detection performance. Specifically, typical alignment methods choose only one modality as a reference modality, leading to excessive dependence on the chosen modality quality. Furthermore, current multimodal fusion detection methods still struggle to strike a balance between high accuracy and low computational complexity, thus making the deployment of these models on resource-constrained UAV platforms a challenge. In order to solve the above problems, this paper proposes a dual-modality UAV image target detection method named Mutual-Spectrum Perception Deformable Fusion Mamba (MSDF-Mamba). First, we designed a Mutual Spectral Deformable Alignment (MSDA) module. This module employs a bidirectional cross-attention mechanism to enable one modality to actively extract the semantic information of the other, generating fusion features rich in cross-modal context as shared references. These fusion features are then used to predict spatial offsets, with deformable convolutions achieving feature alignment. Based on the MSDA module, a Selective Scan Fusion (SSF) module is carefully designed to project the aligned features onto a unified hidden state space. With this method, we achieve full interaction and enhanced fusion of intermodal features with low computational complexity. Experiment results demonstrate that our method outperforms existing state-of-the-art cross-modality detection methods on the mAP metric, achieving a relative improvement of 3.1% compared to baseline models such as DMM, while still maintaining high computational efficiency. Full article

(This article belongs to the Special Issue Multi-Object Detection and Feature Extraction of Remote Sensing Images)

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29 pages, 55152 KB

Open AccessArticle

A Hybrid Motion Compensation Scheme for THz-SAR with Composite Modulated Waveform

by Chongzheng Wu, Yanpeng Shi, Xijian Zhang and Yifei Zhang

Remote Sens. 2025, 17(24), 4036; https://doi.org/10.3390/rs17244036 - 15 Dec 2025

Abstract

Terahertz Synthetic Aperture Radar (THz-SAR) is highly sensitive to platform vibrations and trajectory deviations, which introduce severe phase errors and limited resolution. Typically, platform vibrations and trajectory deviations are investigated individually, and vibrations are modeled as a stationary sine term. In this work, [...] Read more.

Terahertz Synthetic Aperture Radar (THz-SAR) is highly sensitive to platform vibrations and trajectory deviations, which introduce severe phase errors and limited resolution. Typically, platform vibrations and trajectory deviations are investigated individually, and vibrations are modeled as a stationary sine term. In this work, a hybrid motion compensation (MOCO) scheme is proposed to address both platform vibrations and trajectory deviations simultaneously, achieving improved imaging quality. The scheme initiates with a parameter self-adaptive quadratic Kalman filter designed to resolve severe phase wrapping. Then, platform vibration is modeled as a non-stationary multi-sine term, whose components are accurately extracted using an improved signal decomposition algorithm enhanced by a dynamic noise adjustment mechanism. Subsequently, the trajectory deviation is parameterized following subaperture division, estimated using a hybrid optimizer that combines particle swarm optimization and gradient descent. Additionally, a composite modulated waveform application ensures low sidelobes and a low probability of intercept (LPI). Extensive simulations on point targets and complex scenes under various signal-to-noise-ratio (SNR) conditions are applied for SAR image reconstruction, demonstrating robust suppression of motion errors. Under identical simulated error conditions, the proposed method achieves an azimuth resolution of 4.28 cm, which demonstrates superior performance compared to the reported MOCO techniques. Full article

(This article belongs to the Special Issue Advancing Synthetic Aperture Radar: Imaging, Processing, and Applications in Remote Sensing)

29 pages, 33315 KB

Open AccessArticle

Hyperspectral Image Classification with Multi-Path 3D-CNN and Coordinated Hierarchical Attention

by Wenyi Hu, Wei Shi, Chunjie Lan, Yuxia Li and Lei He

Remote Sens. 2025, 17(24), 4035; https://doi.org/10.3390/rs17244035 - 15 Dec 2025

Abstract

Convolutional Neural Networks (CNNs) have been extensively applied for the extraction of deep features in hyperspectral imagery tasks. However, traditional 3D-CNNs are limited by their fixed-size receptive fields and inherent locality. This restricts their ability to capture multi-scale objects and model long-range dependencies, [...] Read more.

Convolutional Neural Networks (CNNs) have been extensively applied for the extraction of deep features in hyperspectral imagery tasks. However, traditional 3D-CNNs are limited by their fixed-size receptive fields and inherent locality. This restricts their ability to capture multi-scale objects and model long-range dependencies, ultimately hindering the representation of large-area land-cover structures. To overcome these drawbacks, we present a new framework designed to integrate multi-scale feature fusion and a hierarchical attention mechanism for hyperspectral image classification. Channel-wise Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) spatial attention are combined to enhance feature representation from both spectral bands and spatial locations, allowing the network to emphasize critical wavelengths and salient spatial structures. Finally, by integrating the self-attention inherent in the Transformer architecture with a Cross-Attention Fusion (CAF) mechanism, a local-global feature fusion module is developed. This module effectively captures extended-span interdependencies present in hyperspectral remote sensing images, and this process facilitates the effective integration of both localized and holistic attributes. On the Salinas Valley dataset, the proposed method delivers an Overall Accuracy (OA) of 0.9929 and an Average Accuracy (AA) of 0.9949, attaining perfect recognition accuracy for certain classes. The proposed model demonstrates commendable class balance and classification stability. Across multiple publicly available hyperspectral remote sensing image datasets, it systematically produces classification outcomes that significantly outperform those of established benchmark methods, exhibiting distinct advantages in feature representation, structural modeling, and the discrimination of complex ground objects. Full article

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19 pages, 15163 KB

Open AccessArticle

Enhanced Co-Registration Method for Long-Baseline SAR Images

by Dong Zeng, Haiqiang Fu, Jianjun Zhu, Qijin Han, Aichun Wang, Mingxia Zhang, Kefu Wu, Zhiwei Liu and Zhiwei Li

Remote Sens. 2025, 17(24), 4034; https://doi.org/10.3390/rs17244034 - 15 Dec 2025

Abstract

Accurate synthetic aperture radar (SAR) image co-registration is a crucial procedure for high-quality interferometry and its associated applications. Neglecting the effect of terrain elevation, conventional techniques employ simple polynomial models to achieve accurate co-registration between SAR image pairs during fine co-registration processing. However, [...] Read more.

Accurate synthetic aperture radar (SAR) image co-registration is a crucial procedure for high-quality interferometry and its associated applications. Neglecting the effect of terrain elevation, conventional techniques employ simple polynomial models to achieve accurate co-registration between SAR image pairs during fine co-registration processing. However, these methods become inapplicable for tugged terrain, especially under longer spatial baseline conditions. On the basis of this, we introduced an elevation-dependent term into the conventional fine co-registration model to compensate for local offsets caused by variable topography. As a result, a new SAR image fine co-registration method was proposed. To validate the proposed method, experiments were conducted using data from China’s LuTan-1 satellite in two typical study areas (Madrid, Spain, and Shannan, China), across diverse land-cover types and terrain conditions. At the Madrid test site, the proposed co-registration algorithm can effectively improve the phase quality (average coherence improves from 0.57 to 0.77), and topography accuracy (quantified by root-mean-square-error, RMSE) improved from 3.67 m to 3.59 m in mountainous regions, and it shows similar performance in relatively flat areas to that of the conventional methods. At the Shannan test site, characterized by rugged terrain, the average coherence of the interferogram obtained by our method increased from 0.32 to 0.48 compared to the conventional co-registration approach. Against the reference topographic data, the InSAR DEM retrieved by our proposed method achieved an RMSE of 6.31 m, indicating an improvement of 23%. This study provides an effective method to enhance the quality of co-registration and interferometry in areas with complex terrain. Full article

(This article belongs to the Special Issue Advances in InSAR Processing: Algorithmic Developments and Diverse Applications)

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Graphical abstract

48 pages, 111927 KB

Open AccessArticle

Insights into Seagrass Distribution, Persistence, and Resilience from Decades of Satellite Monitoring

by Dylan Cowley, David E. Carrasco Rivera, Joanna N. Smart, Nicholas M. Hammerman, Kirsten M. Golding, Faye F. Diederiks and Chris M. Roelfsema

Remote Sens. 2025, 17(24), 4033; https://doi.org/10.3390/rs17244033 - 15 Dec 2025

Abstract

Persistence of seagrass meadows varies depending on community composition, substrate stability, environmental forcing, and water quality/clarity. Spatial trends in decadal scale persistence are difficult to assess at the meadow scale using in situ approaches and assessments using Earth Observation often lack temporal consistency. [...] Read more.

Persistence of seagrass meadows varies depending on community composition, substrate stability, environmental forcing, and water quality/clarity. Spatial trends in decadal scale persistence are difficult to assess at the meadow scale using in situ approaches and assessments using Earth Observation often lack temporal consistency. This study utilises a multi-decadal field monitoring dataset and high-resolution multispectral satellite imagery in a cloud-processing environment to assess species distribution, seagrass cover, and meadow persistence. In this work, we investigate long-term trends in overall meadow and species-specific persistence in the Eastern Banks, Moreton Bay, Australia, a shallow, semi-enclosed, subtropical embayment (∼200 km²). Here, we have identified an overall decline in seagrass cover (−15% of the total study area), between 2011 and 2025, through contraction of meadow extent, with most losses in colonising species (Halophila spinulosa and Halophila ovalis) across the deeper sections of the study area. We have also quantified the spatial extent of a previously identified broad-scale ecosystem shift from meadows dominated by Zostera muelleri to a prevalence of Oceana serrulata, and reduction in the sparse cover species H. spinulosa and H. ovalis. We have presented a semi-automated cloud-processing based pipeline to combine in situ seagrass observations, derived from an expertly trained machine learning model, with high resolution multispectral data to assess seagrass cover and persistence. The variability in decadal-scale persistence between the six key species found in this region has been assessed, with dense cover species (e.g., O. serrulata and Z. muelleri) exhibiting moderate persistence (>0.32) and sparse cover species (H. ovalis and H. spinulosa) with low persistence (∼0.15). Colonising/opportunistic growth patterns characterise the species examined in this study, indicating quick response to disturbance but a lack temporal consistency in meadow form, which has critical implications for resilience. Full article

(This article belongs to the Section Ecological Remote Sensing)

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24 pages, 2729 KB

Open AccessArticle

Analysis of Ionospheric TEC Anomalies Using BDS High-Orbit Satellite Data: A Regional Statistical Study and a Case Study of the 2023 Jishishan Ms6.2 Earthquake

by Xiao Gao, Hanyi Cao, Ranran Shen, Meiting Xin, Penggang Tian and Lin Pan

Remote Sens. 2025, 17(24), 4032; https://doi.org/10.3390/rs17244032 - 14 Dec 2025

Abstract

This study presents a comprehensive analysis of pre- and co-seismic ionospheric disturbances associated with the 2023 Ms6.2 Jishishan earthquake by leveraging the unique observational strengths of BDS, particularly its high-orbit satellites. A multi-parameter space weather index was employed to effectively isolate seismogenic signals [...] Read more.

This study presents a comprehensive analysis of pre- and co-seismic ionospheric disturbances associated with the 2023 Ms6.2 Jishishan earthquake by leveraging the unique observational strengths of BDS, particularly its high-orbit satellites. A multi-parameter space weather index was employed to effectively isolate seismogenic signals from geomagnetic disturbances, confirming that the main shock occurred during geomagnetically quiet conditions. Statistical analysis of 41 historical earthquakes (Mw ≥ 5.5) reveals that 47.2% were associated with detectable Total Electron Content (TEC) anomalies. An inverse correlation between earthquake magnitude and anomaly detectability within a 31-day window suggests prolonged precursor durations for larger events may produce longer-duration precursory signals, which challenge conventional detection methods. The synergistic capabilities of BDS Geostationary Earth Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO) satellites were demonstrated: GEO satellites provide unprecedented temporal stability for continuous TEC monitoring, while IGSO satellites enable high-resolution spatial mapping of Co-seismic Ionospheric Disturbances (CIDs). The detected CIDs propagated at velocities below 1.6 km/s, consistent with acoustic gravity wave (AGW) mechanisms. A case study during a geomagnetically active period further reveals modulated CID propagation characteristics, indicating potential coupling between seismic forcing and space weather. Our findings validate BDS as a powerful and precise tool for ionospheric seismology and provide critical insights into Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) dynamics. Full article

(This article belongs to the Section Earth Observation Data)

26 pages, 10331 KB

Open AccessArticle

STM-Net: A Multiscale Spectral–Spatial Representation Hybrid CNN–Transformer Model for Hyperspectral Image Classification

by Yicheng Hu, Jia Ge and Shufang Tian

Remote Sens. 2025, 17(24), 4031; https://doi.org/10.3390/rs17244031 - 14 Dec 2025

Abstract

Hyperspectral images (HSIs) have been broadly applied in remote sensing, environmental monitoring, agriculture, and other fields due to their rich spectral information and complex spatial properties. However, the inherent redundancy, spectral aliasing, and spatial heterogeneity of high-dimensional data pose significant challenges to classification [...] Read more.

Hyperspectral images (HSIs) have been broadly applied in remote sensing, environmental monitoring, agriculture, and other fields due to their rich spectral information and complex spatial properties. However, the inherent redundancy, spectral aliasing, and spatial heterogeneity of high-dimensional data pose significant challenges to classification accuracy. Therefore, this study proposes STM-Net, a hybrid deep learning model that integrates SSRE (Spectral–Spatial Residual Extraction Module), Transformer, and MDRM (Multi-scale Differential Residual Module) architectures to comprehensively exploit spectral–spatial features and enhance classification performance. First, the SSRE module employs 3D convolutional layers combined with residual connections to extract multi-scale spectral–spatial features, thereby improving the representation of both local and deep-level characteristics. Second, the MDRM incorporates multi-scale differential convolution and the Convolutional Block Attention Module mechanism to refine local feature extraction and enhance inter-class discriminability at category boundaries. Finally, the Transformer branch equipped with a Dual-Branch Global-Local (DBGL) mechanism integrates local convolutional attention and global self-attention, enabling synergistic optimization of long-range dependency modeling and local feature enhancement. In this study, STM-Net is extensively evaluated on three benchmark HSI datasets: Indian Pines, Pavia University, and Salinas. Additionally, experimental results demonstrate that the proposed model consistently outperforms existing methods regarding OA, AA, and the Kappa coefficient, exhibiting superior generalization capability and stability. Furthermore, ablation studies validate that the SSRE, MDRM, and Transformer components each contribute significantly to improving classification performance. This study presents an effective spectral–spatial feature fusion framework for hyperspectral image classification, offering a novel technical solution for remote sensing data analysis. Full article

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28 pages, 27767 KB

Open AccessArticle

Scalable Context-Preserving Model-Aware Deep Clustering for Hyperspectral Images

by Xianlu Li, Nicolas Nadisic, Shaoguang Huang, Nikos Deligiannis and Aleksandra Pižurica

Remote Sens. 2025, 17(24), 4030; https://doi.org/10.3390/rs17244030 - 14 Dec 2025

Abstract

Subspace clustering has become widely adopted for the unsupervised analysis of hyperspectral images (HSIs). Recent model-aware deep subspace clustering methods often use a two-stage framework, involving the calculation of a self-representation matrix with complexity of

O (n^{2})

, followed by [...] Read more.

Subspace clustering has become widely adopted for the unsupervised analysis of hyperspectral images (HSIs). Recent model-aware deep subspace clustering methods often use a two-stage framework, involving the calculation of a self-representation matrix with complexity of

O (n^{2})

, followed by spectral clustering. However, these methods are computationally intensive, generally incorporating only local or non-local structure constraints, and their structural constraints fall short of effectively supervising the entire clustering process. We propose a scalable, context-preserving deep clustering method based on basis representation, which jointly captures local and non-local structures for efficient HSI clustering. To preserve local structure—i.e., spatial continuity within subspaces—we introduce a spatial smoothness constraint that aligns clustering predictions with their spatially filtered versions. For non-local structure—i.e., spectral continuity—we employ a mini-cluster-based scheme that refines predictions at the group level, encouraging spectrally similar pixels to belong to the same subspace. These two constraints are jointly optimized to reinforce each other. Specifically, our model is designed as a one-stage approach, in which the structural constraints are applied to the entire clustering process. The time and space complexity of our method are

O (n)

, making it applicable to large-scale HSI data. Experiments on real-world datasets show that our method outperforms state-of-the-art techniques. Full article

32 pages, 8405 KB

Open AccessArticle

From Graph Synchronization to Policy Learning: Angle-Synchronized Graph and Bilevel Policy Network for Remote Sensing Object Detection

by Jie Yan, Jialang Liu, Lixing Tang, Xiaoxiang Wang and Yanming Guo

Remote Sens. 2025, 17(24), 4029; https://doi.org/10.3390/rs17244029 - 14 Dec 2025

Abstract

Detection of rotating targets in complex remote sensing scenarios often suffers from angular inconsistencies and boundary jitter, especially for small-to-medium objects with rapid pose changes or indistinct boundaries in dense environments. To address this, we propose ASBPNet, a unified framework coupling geometric alignment [...] Read more.

Detection of rotating targets in complex remote sensing scenarios often suffers from angular inconsistencies and boundary jitter, especially for small-to-medium objects with rapid pose changes or indistinct boundaries in dense environments. To address this, we propose ASBPNet, a unified framework coupling geometric alignment with policy adaptation. It features the following: (1) Angle-Synchronized Graph (ASG), which injects angle–alignment relationships and residual-based boundary refinement to improve rotational consistency and reduce boundary errors for small objects; (2) Bilevel Policy Optimization (BPO), which unifies control over rotation enhancement, sample allocation, block scanning, and rotational NMS for cross-stage policy coordination and improved recall. Together, ASG and BPO form a tightly coupled pipeline in which geometric alignment directly reinforces policy optimization, yielding mutually enhanced rotation robustness, boundary stability, and detection recall across densely distributed remote sensing scenes. We conducted systematic evaluations on datasets including DIOR-R, HRSC2016, and DOTAv1.0: compared to baselines, overall accuracy achieved significant improvement on DIOR-R, with performance reaching 98.2% on HRSC2016. Simultaneously, enhanced robustness and boundary stability were demonstrated in complex backgrounds and dense small-object scenarios, validating the synergistic value of geometric alignment and policy adaptation. Full article

(This article belongs to the Special Issue Efficient Object Detection Based on Remote Sensing Images)

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19 pages, 4966 KB

Open AccessArticle

RFANSR: Receptive Field Aggregation Network for Lightweight Remote Sensing Image Super-Resolution

by Xiaoyu Yan, Wei Song, Xiaotong Feng, Wei Guo and Keqing Ning

Remote Sens. 2025, 17(24), 4028; https://doi.org/10.3390/rs17244028 - 14 Dec 2025

Abstract

Expanding the receptive field while maintaining efficiency is a key challenge in lightweight remote sensing super-resolution. Existing methods often suffer from parameter redundancy or insufficient channel utilization. To address these issues, we propose the Receptive Field Aggregation Network (RFANSR). First, we design a [...] Read more.

Expanding the receptive field while maintaining efficiency is a key challenge in lightweight remote sensing super-resolution. Existing methods often suffer from parameter redundancy or insufficient channel utilization. To address these issues, we propose the Receptive Field Aggregation Network (RFANSR). First, we design a Progressive Receptive Field Aggregator (PRFA). It expands the receptive field by cascading medium-sized kernels, avoiding the heavy overhead of extremely large kernels. Second, we introduce a Statistical Guidance Module (SGM). This module replaces inefficient identity mappings with statistical channel recalibration to maximize feature utility. Additionally, we propose a Spatial-Gated Feed-Forward Network (SGFN) to reduce information loss via spatial attention. Extensive experiments demonstrate that RFANSR outperforms state-of-the-art lightweight models. Notably, RFANSR achieves PSNR improvements of 0.06 dB on RSCNN7 and 0.14 dB on DOTA datasets. Remarkably, it requires only 383 K parameters, representing a 45.4% reduction compared to DLKN. Full article

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

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19 pages, 12414 KB

Open AccessArticle

A Spatiotemporal Subgrid Least Squares Approach to DEM Generation of the Greenland Ice Sheet from ICESat-2 Laser Altimetry

by Qiyu Wang, Jinyun Guo, Tao Jiang and Xin Liu

Remote Sens. 2025, 17(24), 4027; https://doi.org/10.3390/rs17244027 - 13 Dec 2025

Abstract

Greenland, home to the largest ice sheet in the Northern Hemisphere, provides a crucial digital elevation model (DEM) for understanding polar climate evolution and valuable data for global climate change research. Based on ICESat-2 laser altimetry data collected from satellite observations over Greenland [...] Read more.

Greenland, home to the largest ice sheet in the Northern Hemisphere, provides a crucial digital elevation model (DEM) for understanding polar climate evolution and valuable data for global climate change research. Based on ICESat-2 laser altimetry data collected from satellite observations over Greenland between November 2020 and November 2021, the Shandong University of Science and Technology 2021 DEM (SDUST2021DEM) with 500 m grid resolution at the epoch of May 2021 was constructed using a spatiotemporally fitted subgrid least squares method. The precision of the DEM was evaluated by comparison with National Aeronautics and Space Administration IceBridge data and supplemented by GNSS station measurements. The median difference between the DEM and IceBridge data was −0.33 m, the mean deviation −0.58 m, and the median absolute deviation 2.31 m. The accuracy of SDUST2021DEM exhibits a clear spatial pattern: it is higher in the central ice sheet than at the margins, decreases in regions with complex terrain, and remains more reliable in areas characterized by gentle slopes and flat terrain. Overall, the SDUST2021DEM demonstrates stable accuracy and can reliably produce high-precision DEMs for a specific temporal epoch. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods (Fourth Edition))

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21 pages, 6509 KB

Open AccessArticle

Quantitative Assessment of Satellite-Observed Atmospheric CO₂ Concentrations over Oceanic Regions

by Xinyu He, Shuangling Chen, Jingyuan Xi and Yuntao Wang

Remote Sens. 2025, 17(24), 4026; https://doi.org/10.3390/rs17244026 - 13 Dec 2025

Abstract

Atmospheric carbon dioxide in mole fraction (XCO₂) is one of the key parameters in estimating CO₂ fluxes at the air–sea interface. Satellite-derived column-averaged XCO₂ has been widely used in the estimates of air–sea CO₂ fluxes, yet the uncertainties [...] Read more.

Atmospheric carbon dioxide in mole fraction (XCO₂) is one of the key parameters in estimating CO₂ fluxes at the air–sea interface. Satellite-derived column-averaged XCO₂ has been widely used in the estimates of air–sea CO₂ fluxes, yet the uncertainties induced by using column-averaged XCO₂ instead of atmospheric XCO₂ in the ocean boundary layer have been generally unknown. In this study, based on an extensive dataset of atmospheric XCO₂ measured in the ocean boundary layer from global ocean mooring arrays (N = 945,243) and historical cruises (N = 170,000) between 2002 and 2024, for the first time, we quantitatively evaluated the performance of four satellites, including the Greenhouse gases Observing SATellite (GOSAT and GOSAT-2), the Orbiting Carbon Observatory-2 (OCO-2), and the Atmospheric InfraRed Sounder (AIRS), in monitoring the atmospheric XCO₂ over oceanic regions. The atmospheric XCO₂ has been increasing from 375 ppm in 2002 to 417 ppm in 2024 based on the longest data record from AIRS. We found that the column-averaged atmospheric XCO₂ can serve as a good proxy for atmospheric XCO₂ in the ocean boundary layer, with associated uncertainties of 2.48 ppm (0.46%) for GOSAT, 1.01 ppm (0.24%) for GOSAT-2, 2.45 ppm (0.45%) for OCO-2, and 4.22 ppm (0.83%) for AIRS. We also investigated the consistency of these satellites in monitoring the growth rates of atmospheric XCO₂ in the global ocean basins. Based on the longest data record from AIRS, the atmospheric XCO₂ has been increasing at a rate of 1.87–1.97 ppm year⁻¹ over oceanic regions in the past two decades. These findings contribute to improving the reliability of satellite-derived column-averaged XCO₂ observations in the estimates of air–sea CO₂ fluxes and support future efforts in monitoring ocean carbon dynamics through satellite remote sensing. Full article

(This article belongs to the Section Ocean Remote Sensing)

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18 pages, 5361 KB

Open AccessArticle

Enhancing Plant Ecological Unit Mapping Accuracy with Auxiliary Data from Landsat-8 in a Heterogeneous Rangeland

by Masoumeh Aghababaei, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi and Jochem Verrelst

Remote Sens. 2025, 17(24), 4025; https://doi.org/10.3390/rs17244025 - 13 Dec 2025

Abstract

Mapping Plant Ecological Units (PEUs) support sustainable rangeland management. Yet, distinguishing them from multispectral imagery remains challenging due to high intra-class variability and spectral overlap. This study evaluates the contribution of auxiliary data layers to improve PEU classification from Landsat-8 OLI imagery in [...] Read more.

Mapping Plant Ecological Units (PEUs) support sustainable rangeland management. Yet, distinguishing them from multispectral imagery remains challenging due to high intra-class variability and spectral overlap. This study evaluates the contribution of auxiliary data layers to improve PEU classification from Landsat-8 OLI imagery in semi-arid rangelands of northeastern Iran. A random forest (RF) classifier was trained using field samples and multiple feature combinations, including spectral indices, topographic variables (DEM, slope, aspect), and principal component analysis (PCA) components. Classification performance was assessed using overall accuracy (OA), kappa coefficient, and non-parametric Friedman and post hoc tests to determine significant differences among scenarios. The results show that auxiliary features consistently enhanced classification performance as opposed to spectral bands alone. Integrating DEM and PCA layers yielded the highest accuracy (OA = 79.3%, κ = 0.71), with statistically significant improvement (p < 0.05). The findings demonstrate that incorporating topographic and transformed spectral information can effectively reduce class confusion and improve the separability of PEUs in complex rangeland environments. The proposed workflow provides a transferable approach for ecological unit mapping in other semi-arid regions facing similar environmental and management challenges. Full article

(This article belongs to the Special Issue Vegetation Mapping through Multiscale Remote Sensing)

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