Remote Sensing

28 pages, 3153 KB

Open AccessArticle

LiteScan-Net: A Lightweight Scanning Network and a Large-Scale Dataset for Cropland Change Detection

by Zhengfang Lou, Xiaoping Lu, Yao Lu, Siyi Li, Guosheng Cai and Ling Song

Remote Sens. 2026, 18(9), 1447; https://doi.org/10.3390/rs18091447 - 6 May 2026

Viewed by 623

Aiming at the dual dilemma in high-resolution cropland change detection, where CNNs are constrained by limited local receptive fields and Transformers suffer from heavy computational costs, we propose LiteScan-Net, a lightweight and robust network architecture incorporating scanning principles from state-space modeling. The network [...] Read more.

Aiming at the dual dilemma in high-resolution cropland change detection, where CNNs are constrained by limited local receptive fields and Transformers suffer from heavy computational costs, we propose LiteScan-Net, a lightweight and robust network architecture incorporating scanning principles from state-space modeling. The network innovatively introduces the Multi-Directional Global Scanning (MDGS) mechanism as an efficient engineering surrogate, which simulates the selective scanning process using large-kernel 1D convolutions. This achieves global context modeling with linear complexity while avoiding the hardware limitations imposed by recurrent computations. Based on this mechanism, a three-stage collaborative architecture is constructed: the Coordinate-Aware Feature Purification (CAFP) module is designed to mitigate shallow phenological noise via coordinate sensitivity; the Context Difference Verification (CDV) module aims to alleviate pseudo-changes caused by registration errors through global alignment; and the State-Space Guided Refinement (SSGR) module promotes the generation of change masks with precise boundaries and compact interiors. To verify the model generalization, we construct a Massive Specialized Cropland Change Detection dataset named MSCC, which exhibits significant cross-scale characteristics. Experimental results demonstrate that LiteScan-Net achieves state-of-the-art (SOTA) performance across the CLCD, Hi-CNA, and MSCC datasets, with F1-scores of 79.43%, 84.82%, and 89.62%, respectively. With a low computational cost of only 1.78 GFLOPs and a real-time inference speed of 37.9 FPS, LiteScan-Net demonstrates high potential for future deployment on resource-constrained edge devices. Full article

(This article belongs to the Special Issue Artificial Intelligence Remote Sensing Change Detection: Development and Challenges)

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24 pages, 4873 KB

Open AccessArticle

Improving Satellite-Based Estimation and Mapping of Soil Lead by Using an Enhanced Spectral Feature Set and XGBoost Model

by Xibo Xu, Ying Wang, Xinrui Dai, Qi Shen, Quanyuan Wu, Zeqiang Wang and Jianfei Cao

Remote Sens. 2026, 18(9), 1446; https://doi.org/10.3390/rs18091446 - 6 May 2026

Cited by 3 | Viewed by 775

Abstract

Satellite hyperspectral remote sensing offers an efficient and cost-effective approach for estimating and mapping soil lead (Pb), thereby supporting pollution identification and environmental sustainability. However, the development of satellite-based spectral estimation models depends on the availability of a robust spectral feature set for [...] Read more.

Satellite hyperspectral remote sensing offers an efficient and cost-effective approach for estimating and mapping soil lead (Pb), thereby supporting pollution identification and environmental sustainability. However, the development of satellite-based spectral estimation models depends on the availability of a robust spectral feature set for soil Pb as input, which is difficult to obtain under field conditions due to interference from moisture, particle size, and light scattering. To address this issue, controlled spectral experiments were conducted on laboratory-prepared soil samples with varying Pb contamination levels. The spectral characteristics associated with Pb contamination were analyzed, and an enhanced spectral feature set (ESFS) was constructed using the successive projections algorithm–Shapley additive explanations method. Two new spectral indices for Pb-contaminated soils, named SPPI-2 and SPPI-3, were developed and incorporated into the ESFS. The ESFS was then applied to satellite hyperspectral data calibrated via direct standardization, with its spectral parameters used as input variables and measured Pb concentrations from field soil samples as the dependent variable. Finally, a satellite-based spectral model for soil Pb estimation was developed using the XGBoost (eXtreme Gradient Boosting) algorithm. Results showed that the spectral parameters in the ESFS included four characteristic bands (R₈₄₀, R₁₀₁₃, R₁₂₁₅, and R₂₂₃₉) and two newly developed spectral indices (SPPI-2 and SPPI-3). The satellite-based spectral estimation model based on the ESFS and XGBoost algorithm achieved the best performance, with R² (coefficient of determination) and RPD (ratio of performance to deviation) values of 0.78 and 2.10, respectively, representing a maximum improvement of 164.10% and a minimum improvement of 12.86% (in terms of RPD values) compared to common methods. Hotspot areas of Pb-contaminated soils were mainly found in the eastern part of the abandoned coal mining area, which is associated with improper coal mining and transportation activities. This study presents a satellite hyperspectral framework for effectively estimating the distribution pattern of soil Pb and supporting the regional-scale soil management and environmental sustainability. Full article

(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)

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25 pages, 28382 KB

Open AccessArticle

Glacial Lake Changes in the Donglin Tsangpo Watershed of China–Nepal Economic Corridor from 2016 to 2024

by Zhe Chen, Changlu Cui, Daxiang Xiang and Ying Jiang

Remote Sens. 2026, 18(9), 1445; https://doi.org/10.3390/rs18091445 - 6 May 2026

Viewed by 678

Abstract

Glacial lake dynamics in high-mountain regions serve as a sensitive proxy for cryospheric responses to climate warming. This study utilizes multi-temporal Sentinel-2 imagery and digital elevation model (DEM) data to quantify glacial lake evolution in the Donglin Tsangpo Watershed, a strategically important section [...] Read more.

Glacial lake dynamics in high-mountain regions serve as a sensitive proxy for cryospheric responses to climate warming. This study utilizes multi-temporal Sentinel-2 imagery and digital elevation model (DEM) data to quantify glacial lake evolution in the Donglin Tsangpo Watershed, a strategically important section of the China–Nepal Economic Corridor, from 2016 to 2024. The results show a significant expansion in both the number (from 43 to 56) and total area (from 3.97 km² to 4.94 km², +24.43%) of glacial lakes, primarily driven by the rapid emergence of very small lakes (0.02–0.05 km²) and a clear upward shift in elevation distribution, with new lakes forming above 5300 m and extending to elevations exceeding 5500 m. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) reveals that this expansion coincided with pronounced positive thermal anomalies, particularly the 2020 extreme warm event (daytime +3.88 °C, nighttime +1.61 °C). Mechanistic analysis using the ERA5-Land reanalysis dataset further demonstrates that persistent positive downward longwave radiation (LW) anomalies (peaking at +10.71 W/m² in 2021) effectively compensated for reduced shortwave input, inhibiting nocturnal refreezing and extending the effective ablation period. Furthermore, a rising liquid-to-solid precipitation ratio and extreme melt-day anomalies (up to +39.36 days) provided intensified hydrothermal inputs, driving the pronounced expansion of glacier-contact lakes despite non-linear interannual responses. This study also estimates individual lake volumes, identifying a transition toward rapid lake development that elevates potential downstream hazard exposure. These findings provide a high-resolution dataset and a robust physical framework for transboundary environmental monitoring and risk assessment in this climate-sensitive region. Full article

(This article belongs to the Special Issue Mapping the Blue: Remote Sensing in Water Resource Management)

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25 pages, 4816 KB

Open AccessArticle

SASR: Sensor-Agnostic Semantic Representation Unification for Cross-Modal RGB and Hyperspectral Aerial Scene Recognition

by Muhammad Zaheer Sajid, Muhammad Fareed Hamid, Kamran Bashir Taas, Muhammad Attique Khan, Latifah Almuqren, Mohammad Alhefdi, Yunyoung Nam and Zepa Yang

Remote Sens. 2026, 18(9), 1444; https://doi.org/10.3390/rs18091444 - 6 May 2026

Cited by 1 | Viewed by 781

Abstract

Aerial scene recognition has progressed substantially with deep learning methods for RGB and hyperspectral imagery; however, existing approaches typically operate on single modalities or rely on explicit multimodal fusion, limiting scalability, flexibility, and deployment in heterogeneous sensing environments. To address this limitation, we [...] Read more.

Aerial scene recognition has progressed substantially with deep learning methods for RGB and hyperspectral imagery; however, existing approaches typically operate on single modalities or rely on explicit multimodal fusion, limiting scalability, flexibility, and deployment in heterogeneous sensing environments. To address this limitation, we propose a sensor-agnostic semantic representation learning framework that formulates multimodal learning as the unification of semantic representations rather than feature-level fusion. The proposed architecture employs modality-specific encoders and projection heads to map spatial and spectral–spatial features into a shared semantic embedding space, enabling modality-invariant representation learning while preserving discriminative characteristics of each sensing modality. A composite objective integrating cross-spectral alignment, intra-class compactness regularization, and prototype-based semantic anchoring is introduced to enforce consistent embedding geometry and improve class separability across modalities. A unified classifier operating within this shared space enables reliable inference from a single modality input without requiring paired data or explicit fusion. Extensive evaluations on multiple benchmark datasets, including Houston 2013 for cross-modality RGB–hyperspectral analysis, UC Merced for independent RGB aerial scene classification, and Indian Pines for hyperspectral land-cover recognition, demonstrate the robustness and generalization capability of the proposed framework. In Houston 2013, the method achieves 96.4% (RGB) and 97.3% (hyperspectral) overall accuracy, with cross-modality transfer performance of 87.2% (RGB → HSI) and 88.7% (HSI → RGB), further improving to 97.0% and 97.8% under joint training. On UC Merced and Indian Pines, the model attains 98.7% and 97.6% overall accuracy, respectively. These results establish semantic representation unification as a scalable and effective alternative to conventional multimodal fusion for heterogeneous remote sensing environments. Full article

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34 pages, 17465 KB

Open AccessArticle

Backpack System Development and Image-LiDAR Integration for Improved Geospatial Data Alignment in Forest Mapping

by Raja Manish, Songlin Fei and Ayman Habib

Remote Sens. 2026, 18(9), 1443; https://doi.org/10.3390/rs18091443 - 6 May 2026

Viewed by 528

Abstract

Backpack mobile mapping systems (MMS) equipped with LiDAR and RGB cameras, as well as an optional GNSS/INS direct georeferencing unit, are increasingly utilized in forest inventory applications. In general, LiDAR point clouds provide detailed structural information, whereas imagery offers visual specifics of surface [...] Read more.

Backpack mobile mapping systems (MMS) equipped with LiDAR and RGB cameras, as well as an optional GNSS/INS direct georeferencing unit, are increasingly utilized in forest inventory applications. In general, LiDAR point clouds provide detailed structural information, whereas imagery offers visual specifics of surface features. However, cameras typically operate at lower acquisition rates compared to LiDAR. In proximal mapping, another challenge is the inconsistent reception of GNSS signals beneath forest canopies. Additionally, georeferencing accuracy may differ between LiDAR and imagery due to biases in the system calibration parameters and variations in post-processing approaches. To address these challenges, this study introduces a Backpack MMS that uses cameras configured at elevated frame rates to enhance image overlap. Concurrently, this study presents an algorithmic approach to addressing georeferencing issues by integrating imagery and LiDAR data, thereby enhancing system calibration and improving platform trajectory. The method is based on the hypothesis that forest environments are rich with geometrically well-defined features, such as tree trunks and ground patches. By identifying conjugate primitives in point clouds from both imagery and LiDAR, the procedure optimizes feature models while simultaneously minimizing calibration biases and/or trajectory errors. The proposed approach is validated using multiple field datasets collected in diverse forest environments. Quantitative results show that the procedure reduces image–LiDAR feature misalignment across all datasets from up to 1.1 m in the planimetric direction and 2 m in the vertical direction to within 5 cm in both. The feature fitting accuracy also improves from 2.9 cm to 0.85 cm for LiDAR point clouds and from 10 cm to 0.9 cm for image-based point clouds. However, the results indicate that despite increased data availability, imagery alone remains less reliable than LiDAR for extracting structural information. Nevertheless, the proposed image–LiDAR alignment strategy represents a crucial step toward developing a comprehensive tree inventory. Full article

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25 pages, 44347 KB

Open AccessArticle

Assessing Debris-Flow Susceptibility at Local and Global Scales: A Deep-Learning-Based Comparative Study ofSichuan, China, and Worldwide

by Andreas Nienkötter, Ang Bian, Baofeng Di, Jierui Li and Tian Deng

Remote Sens. 2026, 18(9), 1442; https://doi.org/10.3390/rs18091442 - 6 May 2026

Viewed by 680

Abstract

Debris flows pose a significant global geohazard, causing a large number of deaths and infrastructure damage every year. Effective protection and land-use planning in the affected regions requires understanding susceptibility to these events. Although a global phenomenon, previous studies have focused extensively on [...] Read more.

Debris flows pose a significant global geohazard, causing a large number of deaths and infrastructure damage every year. Effective protection and land-use planning in the affected regions requires understanding susceptibility to these events. Although a global phenomenon, previous studies have focused extensively on local areas with specialized models and accordingly complex feature selections. In this study, we investigate whether a unified debris-flow susceptibility prediction paradigm can be achieved regardless of regional scale, using only very few global public remote sensing data sources. To this end, this work contributes in the following ways: (1) A novel two-step negative sample generation scheme is proposed, and two open debris-flow datasets are constructed based on global debris-flow locations and locations in Sichuan, China. (2) An open-source end-to-end machine learning platform using remote sensing features directly is proposed, which achieves state-of-the-art results with 0.947 and 0.957 AUC in both scales compared to 0.88 for previous methods on the same location data, while using far fewer features. (3) A comparative feature importance analysis shows that, given the significant feature distribution difference on global vs local datasets, alleviating the scale-level gap is possible by leveraging the advanced deep learning technologies. This allows our unified framework to be easily applied to any regional study of debris-flow susceptibility prediction. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Investigations: Mapping, Monitoring and Forecasting)

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21 pages, 24811 KB

Open AccessArticle

A 2025 High-Resolution Glacier Inventory of the Greater Caucasus Reveals Accelerated Area Loss

by Levan G. Tielidze, Gennady A. Nosenko, Akaki Nadaraia, Tatiana E. Khromova, Roman M. Kumladze, Caroline C. Clason, Mikheil Elashvili and Lela Gadrani

Remote Sens. 2026, 18(9), 1441; https://doi.org/10.3390/rs18091441 - 6 May 2026

Cited by 1 | Viewed by 1717

Abstract

The Greater Caucasus is one of the most extensively glacierized mountain systems in mid-latitude Eurasia and has experienced substantial glacier retreat in recent decades. Continuous monitoring using high-resolution satellite observations is therefore essential for accurately quantifying ongoing and future changes. In this study, [...] Read more.

The Greater Caucasus is one of the most extensively glacierized mountain systems in mid-latitude Eurasia and has experienced substantial glacier retreat in recent decades. Continuous monitoring using high-resolution satellite observations is therefore essential for accurately quantifying ongoing and future changes. In this study, we present a new glacier inventory for 2025 derived from high-resolution (3 m) PlanetScope satellite imagery combined with topographic information from the 30 m Advanced Land Observing Satellite (ALOS) Global Digital Surface Model (2006–2011). A total of 101 cloud-free PlanetScope scenes, acquired primarily during August–September 2025, were manually delineated to ensure precise glacier boundary detection. Regional climatic data, including summer temperature and winter precipitation from the ERA5 reanalysis, were compiled to support interpretation of glacier changes since the 1960s. The new inventory identifies 2341 glaciers covering 964.0 ± 22.8 km² across the Greater Caucasus. Glacier distribution is highly uneven: most of the glacier-covered area is found in the Central Caucasus (730.2 ± 15.5 km²), whereas considerably smaller glacierized areas occur in the Western and Eastern sectors. Most glaciers are located on northern slopes (687.7 ± 16.0 km²), reflecting strong topographic and climatic asymmetry. Mean glacier elevations range from ~3300 to 3600 m a.s.l., increasing eastward in response to decreasing precipitation. Size-class analysis shows that small glaciers (<0.5 km²) dominate numerically, whereas a limited number of large valley glaciers (>5.0 km²) contribute disproportionately to total glacier area. Comparison with previous inventories indicates continued and accelerated glacier retreat, particularly since 2014, with a mean area loss rate of −1.8% yr⁻¹. These comparisons further show that a total of 965 glaciers (~122.9 km²) have become extinct across the Greater Caucasus since the 1960s. This trend is primarily driven by increasing summer temperatures and declining winter precipitation. This high-resolution inventory provides the most detailed glacier dataset currently available for the Greater Caucasus and establishes an updated benchmark for future glacier monitoring, climate change studies, and hydrological assessments. Full article

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37 pages, 4673 KB

Open AccessArticle

Hyperspectral Band Selection for Ground Fuel Classification for Prescribed Fires

by Mahmad Isaq Karankot, Ethan M. Glenn, Muhammad Umer Masood, Xiaobing Zhou and Bradley M. Whitaker

Remote Sens. 2026, 18(9), 1440; https://doi.org/10.3390/rs18091440 - 6 May 2026

Viewed by 605

Abstract

Hyperspectral image (HSI) analysis plays a central role in remote sensing tasks requiring fine-grained material discrimination, vegetation health assessment, and post-disturbance monitoring. Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These [...] Read more.

Hyperspectral image (HSI) analysis plays a central role in remote sensing tasks requiring fine-grained material discrimination, vegetation health assessment, and post-disturbance monitoring. Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These challenges are especially important in wildfire science and prescribed-fire monitoring, where spectral responses vary due to burn severity, char deposition, canopy structure, and early vegetation recovery. Benchmark datasets such as Indian Pines and Pavia University and others provide controlled environments for algorithms’ evaluation, but real-world post-fire forest conditions pose additional complexity. This study presents a unified and comprehensive evaluation of five dimensionality reduction strategies: Principal Component Analysis (PCA), Spatial–Spectral Edge Preservation (SSEP), Spectral-Redundancy Penalized Attention (SRPA), and a Deep Reinforcement Learning (DRL)-based selector together with a clustering based baseline, K-Means Clustering-Based Band Selection (KMCBS). These strategies are combined with classical machine learning and deep learning classifiers: Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and 3D Convolutional Neural Networks (3D-CNN). The full pipeline includes exploratory data analysis, preprocessing, patch-based spatial–spectral modeling, consistent train–validation protocols, and multi-dataset evaluation across Indian Pines, Pavia University, and a new custom VNIR hyperspectral dataset collected after prescribed burns at the Lubrecht Experimental Forest in Montana, USA. By systematically comparing statistical, edge-aware, attention-guided, and reinforcement learning-based band-selection strategies, this work identifies compact yet informative spectral subsets that enhance classification performance while reducing computational cost. Importantly, the inclusion of the Montana prescribed-burn dataset provides a unique real-world testbed for understanding band selection behavior in fire-affected forest environments. Overall, this study contributes a generalizable and extensible framework for HSI dimensionality reduction and classification, laying the groundwork for future applications in wildfire assessment, vegetation recovery monitoring, and remote sensing. Full article

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22 pages, 6292 KB

Open AccessArticle

Rid-HRNet: A Lightweight Multi-Scale Network for Sand Ridge Line Extraction from Landsat Imagery

by Xuanjing Huang, Xinchao Liu, Jiayue Mu, Ye Zhu, Zhaobin Wang and Yaonan Zhang

Remote Sens. 2026, 18(9), 1439; https://doi.org/10.3390/rs18091439 - 6 May 2026

Viewed by 551

Abstract

Sand ridge lines serve as key geomorphological indicators for interpreting aeolian dynamics and assessing desertification intensity. However, automated extraction of continuous ridge structures from remote sensing imagery remains challenging due to the multi-scale morphology of dunes, complex surface textures, and strong shadow interference. [...] Read more.

Sand ridge lines serve as key geomorphological indicators for interpreting aeolian dynamics and assessing desertification intensity. However, automated extraction of continuous ridge structures from remote sensing imagery remains challenging due to the multi-scale morphology of dunes, complex surface textures, and strong shadow interference. Conventional edge detection models often rely on computationally heavy backbones or suffer from structural discontinuities in subtle ridge branches, limiting their applicability in large-scale desert monitoring. To address these challenges, we propose Rid-HRNet, a lightweight high-resolution network specifically designed for efficient and structurally coherent sand ridge extraction. Unlike traditional encoder–decoder architectures, Rid-HRNet maintains parallel high-resolution representations throughout the network to preserve fine spatial details. A Multi-Scale Information Aggregation (MSIA) module enhances cross-scale feature interaction by integrating shallow structural cues with deeper semantic representations. In addition, an Improved Contextual Fusion Module (ICFM) employs pixel-wise attention to adaptively fuse multi-level predictions, reinforcing ridge continuity while suppressing background interference. Experiments on Landsat-8 desert imagery demonstrate that Rid-HRNet achieves an Optimal Dataset Scale (ODS) of 0.790, an Optimal Image Scale (OIS) of 0.806, an Average Precision (AP) of 0.710, and an AC(R50) score of 0.744. The proposed model outperforms classical VGG-based detectors, including HED and RCF, as well as recent lightweight baselines such as PiDiNet and LDC, in terms of overall accuracy and structural consistency. Notably, Rid-HRNet contains only 0.20M parameters and requires 0.55 GFLOPs, operating at 279.23 FPS with a GPU memory footprint of 0.02 GB. These results indicate that Rid-HRNet achieves a favorable balance between detection performance and computational efficiency, supporting large-scale geomorphological mapping and operational desert monitoring based on high-resolution satellite imagery. Full article

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

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24 pages, 11299 KB

Open AccessArticle

Optical River Ice Spectral Subclassification on the Tibetan Plateau: A Landsat 5–9 and Sentinel-2 Benchmark with Interpretable Machine Learning

by Hanwen Zhang and Hongyi Li

Remote Sens. 2026, 18(9), 1437; https://doi.org/10.3390/rs18091437 - 6 May 2026

Viewed by 666

Abstract

River ice products from optical satellites are still dominated by binary ice–water or ice–snow discrimination, leaving within-ice spectral heterogeneity largely unresolved. This study benchmarks how far river ice can be subclassified from multispectral reflectance alone on the Tibetan Plateau using Landsat 5/7, Landsat [...] Read more.

River ice products from optical satellites are still dominated by binary ice–water or ice–snow discrimination, leaving within-ice spectral heterogeneity largely unresolved. This study benchmarks how far river ice can be subclassified from multispectral reflectance alone on the Tibetan Plateau using Landsat 5/7, Landsat 8/9, and Sentinel-2 surface-reflectance imagery. We compiled 356 winter scenes acquired between 2000 and 2024 across eight Tibetan Plateau basins, delineated river ice using NDSI and RDRI, and extracted 24,674 pixel-level spectra. To define reproducible subclasses, we applied K-means clustering guided by the Silhouette Coefficient, Davies–Bouldin index, Calinski–Harabasz index, and Gap Statistic. Combined with stratified visual interpretation, this approach consistently supported four optical spectral subclasses: thin-snow-covered ice, thick ice cover, thin ice, and frazil ice. Within-sensor classification accuracy remained extremely high (overall accuracy ≥ 0.948; kappa ≥ 0.929), with the Backpropagation Neural Network (BPNN) and tree ensembles performing best. Crucially, evaluating the optimal BPNN architecture revealed exceptional multi-dimensional generalizability: a Leave-One-Basin-Out spatial cross-validation yielded a stable average OA > 99% with an average Kappa > 0.98, while a unified multi-sensor model achieved a robust OA of 90.14% and a Kappa of 0.86. The most stable discriminative cues were visible-band brightness, reflectance turnover near ~0.7 μm, and shortwave-infrared sensitivity to effective thickness and surface wetness. These results provide a sensor-aware benchmark for practical optical river ice spectral subclassification and clarify which multispectral bands most strongly constrain subclass separability. Full article

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23 pages, 3260 KB

Open AccessArticle

Characterizing Rainfall Discrepancies Between Landslide Sites and the Nearest Rain Gauges Using Radar Estimates: A Case Study from Italy

by Carmela Vennari, Francesco Chiaravalloti and Roberto Coscarelli

Remote Sens. 2026, 18(9), 1435; https://doi.org/10.3390/rs18091435 - 6 May 2026

Viewed by 1003

Abstract

The spatial representativeness of rain gauges is critical for accurately estimating rainfall that triggers landslides and for defining operational thresholds. This study evaluates the potential error in conventional rain-gauge-based methods for estimating landslide-triggering rainfall, using 548 landslide events across Italy from the e-ITALICA [...] Read more.

The spatial representativeness of rain gauges is critical for accurately estimating rainfall that triggers landslides and for defining operational thresholds. This study evaluates the potential error in conventional rain-gauge-based methods for estimating landslide-triggering rainfall, using 548 landslide events across Italy from the e-ITALICA database, which reports the duration of each rainfall event and the location of the nearest available rain gauge. A radar-based assessment, using the Surface Rainfall Intensity (SRI) product (1 km² resolution) provided by the Italian Department of Civil Protection, quantified discrepancies between rainfall at landslide locations and at the nearest rain gauges. Seasonal analysis was performed, considering summer events (April–September), typically associated with convective and spatially variable rainfall, and winter events (October–March), generally more stratiform and uniform rainfall. Results indicate that the probability of large discrepancies increases with distance. Summer events show larger discrepancies at short distances compared to winter events, but seasonal distributions converge at larger distances. These findings provide useful insights into rain gauge representativeness in studies of rainfall-induced landslides. Full article

(This article belongs to the Special Issue Weather and Climate Extremes Monitoring Based on Remote Sensing Methods)

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28 pages, 9413 KB

Open AccessArticle

Long-Term Wildfire Emissions and Smoke-Plume Dynamics in Greece

by Thanos Kourantos, Anna Kampouri, Marios Mermigkas, Konstantinos Michailidis, Apostolos Voulgarakis, Mark Parrington, Dimitris Vallianatos, Dimitris Melas, Ioannis Kioutsioukis and Vassilis Amiridis

Remote Sens. 2026, 18(9), 1438; https://doi.org/10.3390/rs18091438 - 5 May 2026

Viewed by 1444

Abstract

This study investigates long-term wildfire emissions and smoke-plume geospatial characteristics in Greece by analyzing a multi-pollutant dataset spanning January 2003 to August 2025. Details of emissions of carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), particulate matter (PM_2.5 [...] Read more.

This study investigates long-term wildfire emissions and smoke-plume geospatial characteristics in Greece by analyzing a multi-pollutant dataset spanning January 2003 to August 2025. Details of emissions of carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), particulate matter (PM_2.5), organic carbon (OC), and black carbon (BC) were derived from the Global Fire Assimilation System (GFAS), which converts MODIS fire radiative power into trace gas and aerosol fluxes at 0.1° resolution, and also accounts for the land type. Burned-area statistics from the European Forest Fire Information System (EFFIS) were used for cross-validation. Data were processed into daily, monthly, annual, and cumulative time series, with spatial mapping at the municipality scale and information regarding long-term trends. The analysis shows that while there are several sizeable wildfire events in the country every year, the bulk of the total of Greek wildfire emissions for the last 23 years is attributable to a few extreme fire seasons (2007, 2021, and 2023) that produced abrupt emission surges and accounted for a disproportionate share of national totals. Analysis of spatial data identifies the areas of Evia, East Attica, Messinia, and Evros as persistent emission hotspots. Although wildfire CO₂ emissions are generally a minor fraction of Greece’s anthropogenic totals (<5%), they reached 15–17% during peak fire years. Plume-injection height analysis reveals that most smoke remains below ~1 km but can reach 3–6 km during extreme events, facilitating long-range transport. Overall, the dataset demonstrates a shift toward more intense and concentrated wildfire events in recent years, highlighting both their growing climatic relevance and their acute impacts on regional air quality. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Technologies for Carbon Emissions Monitoring, Reporting, and Verification)

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32 pages, 11623 KB

Open AccessArticle

Changes in Glaciers of the Vakhsh River Basin, Tajikistan Under Global Climate Change

by Farhod Nasrulloev, Yaning Chen, Aminjon Gulakhmadov, Amirkhamza Murodov and Xueqi Zhang

Remote Sens. 2026, 18(9), 1436; https://doi.org/10.3390/rs18091436 - 5 May 2026

Viewed by 1076

Abstract

The VRB represents one of the most important glacierized regions in the upper Amu Darya Basin (UADB), where glacier and snow dynamics play a key role in regional water resources. This study investigates glacier changes in the VRB during 2000–2025 based on multi-source [...] Read more.

The VRB represents one of the most important glacierized regions in the upper Amu Darya Basin (UADB), where glacier and snow dynamics play a key role in regional water resources. This study investigates glacier changes in the VRB during 2000–2025 based on multi-source remote sensing and GIS analysis, while long-term climatic variability since 1970 is used to provide background context for regional climate conditions. The results show a significant reduction in glacier area from 4440.9 km² in 2000 to 3955.2 km² in 2025, corresponding to a loss of 485.7 km² (10.94%). The glaciers are mainly distributed on northern and northeastern slopes at elevations between 4000 and 5000 m a.s.l., where climatic conditions favor their preservation. The basin also contains numerous surge-type glaciers, accounting for approximately 60% of all surge-type glaciers in the Pamir region, with advances ranging from 0.4 to 3.6 km. Climatic analysis indicates a warming trend of 0.15–0.31 °C per decade during 1970–2025, accompanied by pronounced seasonal variability in snow cover and gradual decreases in surface albedo associated with increased dust and black carbon concentrations. Glacier thinning is particularly evident in the lower glacier zones, while hydrological analysis shows that glacier and snow meltwater strongly influence river runoff. These results highlight the sensitivity of glaciers in the VRB to climatic and environmental changes and emphasize the importance of continued monitoring and adaptive water resource management in the VRB. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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18 pages, 3630 KB

Open AccessArticle

Assessing Satellite-Based Data Products Estimating Daily Means of Solar Irradiance at Surface over South Cameroon Plateau and Potential Improvements

by Delphin Aymar Ngah Onana, Pascal Brice Owona Atangana, Murielle Mbuko Tcheutchoua and William Wandji Nyamsi

Remote Sens. 2026, 18(9), 1434; https://doi.org/10.3390/rs18091434 - 5 May 2026

Cited by 1 | Viewed by 610

Abstract

The assessment of satellite-derived solar radiation products has been performed over several parts of the world by various authors. The case of Central Africa has so far hardly been addressed. This study takes a step forward by evaluating the performance of three existing [...] Read more.

The assessment of satellite-derived solar radiation products has been performed over several parts of the world by various authors. The case of Central Africa has so far hardly been addressed. This study takes a step forward by evaluating the performance of three existing state-of-the-art satellite-based data products, namely CAMS-RAD 4.6, CERES SYN1deg Ed4.2 and SARAH-3, in estimating the daily mean surface solar irradiance at five ground-based stations located in the South Cameroon plateau. The correlation coefficient varies between 0.59 and 0.92, with the highest level always seen for CAMS-RAD 4.6 data at each station. The bias (RMSE) is large and always positive for satellite products, confirming a general overestimation. It ranges between 34 W m⁻² and 77 W m⁻² (40 W m⁻² and 86 W m⁻²), i.e., 22 % and 53 % (26 % and 60 %) in relative value. The lowest (highest) bias is seen with CERES SYN1deg Ed4.2 (SARAH-3) data when each station is taken individually. An approach for improvement based on a simple linear regression model without the intercept was developed with CAMS data. In general, the approach significantly reduces the bias by at least 30 W m⁻², i.e., 20 % in relative value at each station. The RMSE also clearly reduces, by at least 30 W m⁻², i.e., a reduction of at least 20 % in relative value. This work shows the way toward further improvements. Full article

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27 pages, 8483 KB

Open AccessArticle

Impact of Data Modality and Batch Normalization Layers on Very High-Resolution Impervious Surface Mapping Using DeepLabv3+ and U-Net Under Regional Cross-City and Cross-Season Domain Shifts

by Jan-Philipp Langenkamp and Andreas Rienow

Remote Sens. 2026, 18(9), 1433; https://doi.org/10.3390/rs18091433 - 4 May 2026

Viewed by 665

Abstract

Urban planning, climatology, or hydrology require continuous and spatially explicit information about impervious surfaces. Semantic segmentation using very high-resolution remote sensing data increased the performance of their detection. However, semantic segmentation models (SSMs) suffer from domains shifts when applied across cities or seasons. [...] Read more.

Urban planning, climatology, or hydrology require continuous and spatially explicit information about impervious surfaces. Semantic segmentation using very high-resolution remote sensing data increased the performance of their detection. However, semantic segmentation models (SSMs) suffer from domains shifts when applied across cities or seasons. While domain adaptation (DA) techniques exist, the current literature provides little information on the level of sensitivity expected for baseline SSMs in mapping impervious surfaces in such scenarios. This study evaluates how data modality (e. g. spectral or height information) and adaptive batch normalization (AdaBN) affect the robustness of SSMs in cross-city and cross-season scenarios. Potsdam and Vaihingen benchmark datasets were used and merged into classes of impervious surfaces, buildings, and background. The impervious surface class was found to be the most sensitive to cross-domain shifts. Multimodal datasets and AdaBN increased model robustness, while in comparison, the impact of AdaBN was 3.46 percentage points lower regarding the mean intersection over union (mIoU). The combination of multimodal datasets and AdaBN exhibited the best results throughout the experiments, increasing mIoU by an additional 10.06 percentage points compared to the multimodal model versions. When DA techniques are unavailable, using multimodal datasets in combination with AdaBN holds a practical approach for cross-domain scenarios in impervious surface mapping. Full article

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27 pages, 15933 KB

Open AccessArticle

DSFNet: A Directional Statistical Fusion Network for Cloud and Cloud Shadow Segmentation

by Yuqi Fang, Zhiyong Fan, Min Xia, Ni Li and Xiaolin Yang

Remote Sens. 2026, 18(9), 1432; https://doi.org/10.3390/rs18091432 - 4 May 2026

Viewed by 569

Abstract

Accurate cloud and cloud shadow segmentation is a critical prerequisite for remote sensing image preprocessing. However, this task remains challenging due to the directional continuity of projected cloud shadows, the radiometric ambiguity between low-reflectance shadows and other dark surfaces, and the difficulty of [...] Read more.

Accurate cloud and cloud shadow segmentation is a critical prerequisite for remote sensing image preprocessing. However, this task remains challenging due to the directional continuity of projected cloud shadows, the radiometric ambiguity between low-reflectance shadows and other dark surfaces, and the difficulty of preserving semantic consistency and fine boundaries in complex scenes. To address these issues, this paper proposes a Directional Statistical Fusion Network (DSFNet) based on an enhanced DeepLabV3+ architecture. Specifically, a Directional Scale Refinement Module (DSRM) is introduced in parallel with Atrous Spatial Pyramid Pooling to strengthen the representation of direction-sensitive cloud-shadow structures and multi-scale cloud regions. An Adaptive Statistical Context Attention (ASCA) module is further designed to perform robust feature modulation by jointly exploiting global statistics, edge-aware statistics, and median-based normalization, thereby suppressing anomalous responses under heterogeneous backgrounds. In the decoder, an Adaptive Grouped Multi-scale Fusion (AGMF) module is employed to adaptively fuse shallow detail features and high-level semantic features through discrepancy-guided grouped gating, improving structural consistency and boundary recovery. In addition, a hybrid loss is adopted to further optimize segmentation. Experiments on the GF1_WHU dataset show that DSFNet achieves 76.97% mIoU, demonstrating strong effectiveness and robustness in complex remote sensing scenes. Full article

(This article belongs to the Special Issue Advanced Deep Learning Techniques for Information Extraction and Analysis of Remote Sensing Imagery)

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29 pages, 30646 KB

Open AccessArticle

Precision Estimation of Aboveground Carbon Stock in Acidosasa edulis Bamboo Forests: A Fusion Approach with UAV-LiDAR, Allometric Equations, and Machine Learning

by Xiaoyu Guo, Weisen Wang, Zhanghua Xu, Mingjing Li, Kele Yang, Yan Tan, Ze Shi, Haohao Yue and Juncheng Zhang

Remote Sens. 2026, 18(9), 1431; https://doi.org/10.3390/rs18091431 - 4 May 2026

Viewed by 739

Abstract

As a fast-growing and multifunctional crop, bamboo plays a pivotal role in food security and climate change mitigation by leveraging its high carbon sequestration potential. Monitoring aboveground carbon (AGC) stock in bamboo forests is crucial for guiding field management, growth observation, and yield [...] Read more.

As a fast-growing and multifunctional crop, bamboo plays a pivotal role in food security and climate change mitigation by leveraging its high carbon sequestration potential. Monitoring aboveground carbon (AGC) stock in bamboo forests is crucial for guiding field management, growth observation, and yield prediction. Unmanned aerial vehicle (UAV)-based point cloud sensors offer a rapid and scalable solution for measuring bamboo AGC. This study evaluates the potential of UAV-LiDAR and machine learning (ML) for organ-level AGC estimation in bamboo forests. From LiDAR point clouds, we extracted structural features—including height, density, canopy, and intensity metrics—aggregated by mean plot-level metric (Mean-PM) and maximum plot-level metric (Max-PM) values at a 1 m² grid scale. Key predictors were selected using ML-based recursive feature elimination (ML-RFE) to develop organ-specific AGC inversion models. Results showed that organ-specific carbon content and allometric equations effectively eliminated biases associated with a uniform coefficient. Max-PM features outperformed Mean-PM features in stem and leaf AGCs, with the XGBoost and Random Forest models achieving the highest accuracy (R² = 0.82 for stems, 0.73 for leaves). Height percentiles and canopy structural metrics emerged as dominant predictors. This UAV-LiDAR-ML framework provides a cost-effective solution for precise bamboo carbon estimation, offering critical insights for carbon neutrality management and informed decision-making in bamboo forest ecosystems. Full article

(This article belongs to the Topic AI-Enabled Precise Forest Monitoring Through UAV and Satellite Remote Sensing)

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32 pages, 8414 KB

Open AccessArticle

TVLightFormer: A Lightweight Cross-Modal Transformer for Language-Guided Target Localization in SAR Imagery

by Yuqiao Zhong, Haoqi Quan, Chenyu Nie, Yingmei Wei and Yanming Guo

Remote Sens. 2026, 18(9), 1430; https://doi.org/10.3390/rs18091430 - 4 May 2026

Viewed by 471

Abstract

We study language-guided target localization in synthetic aperture radar (SAR) imagery for deployment on resource-constrained platforms. Existing vision-language models either rely on heavy backbones unsuitable for edge devices or are designed for natural images, overlooking SAR-specific characteristics such as speckle noise, weak scattering [...] Read more.

We study language-guided target localization in synthetic aperture radar (SAR) imagery for deployment on resource-constrained platforms. Existing vision-language models either rely on heavy backbones unsuitable for edge devices or are designed for natural images, overlooking SAR-specific characteristics such as speckle noise, weak scattering responses, and geometric distortions. The proposed model, TVLightFormer, combines a lightweight dual-modal encoder (MobileNetV3 and TinyBERT) with a grouped-query attention (GQA) mechanism for efficient cross-modal interaction and an activation-free lightweight feature pyramid network (LFPN) to handle scale variation while preserving weak scattering signals. The individual modules are not claimed as newly invented components; the main contribution lies in their SAR-aware integration for edge-oriented cross-modal localization. We evaluate the model on five remote sensing datasets—SOMA-1M, ATRNet-STAR, GAIA, MLRSNet, and SODAS—under a unified localization setting, and we explicitly discuss the limitations introduced by weak or scene-level annotations. The results show that TVLightFormer achieves a favorable trade-off between accuracy and efficiency, reaching an average mIoU of 69.8% with 27.4 M parameters and 9.7 GFLOPs. Ablation studies quantify the contribution of each component. The model is suited for edge-oriented scenarios where computational resources are limited. We also provide a critical analysis of failure cases, SAR-specific disturbance factors, loss-function choices, and dataset-protocol sensitivity. Full article

(This article belongs to the Special Issue Radar and Photo-Electronic Multi-Modal Intelligent Fusion)

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32 pages, 30573 KB

Open AccessArticle

Attribution of Evapotranspiration Variation in the Yellow River Basin with a Simplified Water–Energy Partitioning Method Based on Multi-Source Datasets

by Dayang Wang, Yanyu Ma, Ya Huang, Kaihao Long, Shaobo Liu, Xiaohang Ma, Minghao Song and Zequn Lin

Remote Sens. 2026, 18(9), 1429; https://doi.org/10.3390/rs18091429 - 4 May 2026

Viewed by 675

Abstract

Terrestrial evapotranspiration (ET) serves as a critical nexus between the hydrological cycle and energy process, which is highly sensitive to climate change (CC) and underlying characteristic change (ULCC), particularly in the regions with rapid environmental changes. This study designed a data combination scheme [...] Read more.

Terrestrial evapotranspiration (ET) serves as a critical nexus between the hydrological cycle and energy process, which is highly sensitive to climate change (CC) and underlying characteristic change (ULCC), particularly in the regions with rapid environmental changes. This study designed a data combination scheme for investigating the ET variation and quantifying its drivers in the Yellow River Basin (YRB), using a simplified water–energy partitioning (WEP) method based on nine multi-source ET, precipitation and potential ET datasets. Results reveal that all ET datasets demonstrate significant increasing trends with the rates of 0.82–2.04 mm/yr² during the period of 1982–2022, and the ET increments are 13.4–45.2 mm/yr from the base period (1982–2000) to the change period (2001–2022). For the whole YRB, ULCC has slightly larger averaged absolute and relative contribution (15.8 mm/yr and 52.9%) than those of CC (12.2 mm/yr and 47.1%) to ET increases among the different dataset triplets. For most sub-basins, ULCC exhibits higher contributions than CC, with relative contributions of nearly two-thirds, although considerable variabilities exist in their absolute contributions. However, the opposite results occur in the source region of the YRB, where CC has a primary contribution to ET variation. In summary, while ULCC is the primary driver of ET increases, its estimated contributions entail substantial uncertainty. In contrast, CC acts as a secondary driver, exhibiting greater robustness and lower sensitivity to multi-source dataset variability. This study provides a valuable multi-source-dataset-based ET attribution framework with the WEP method that advances our understanding of hydrological responses to the changing environment in the YRB. Full article

(This article belongs to the Special Issue AI-Enhanced Remote Sensing and Land Surface Modeling for Terrestrial Hydrology and Climate Systems)

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26 pages, 7582 KB

Open AccessArticle

Understanding the Optical Behavior and Spectral Signature of Dredging-Induced Plumes in Coastal Waters

by David Doxaran, Isabella Mayot, Liesbeth De Keukelaere, Robrecht Moelans, Niels Verdoodt and Els Knaeps

Remote Sens. 2026, 18(9), 1428; https://doi.org/10.3390/rs18091428 - 4 May 2026

Cited by 1 | Viewed by 479

Abstract

Dredging activities regularly occurring in near-shore and coastal waters generate turbid waters within the surface layer with high concentrations of suspended particulate matter collected in bottom sediments. The potential impact of these dredge plumes on natural ecosystems must be monitored using cost-effective methods [...] Read more.

Dredging activities regularly occurring in near-shore and coastal waters generate turbid waters within the surface layer with high concentrations of suspended particulate matter collected in bottom sediments. The potential impact of these dredge plumes on natural ecosystems must be monitored using cost-effective methods and observations. Here, we investigate the biogeochemical and optical properties of dredge plumes selected mainly in European and African coastal waters. Laboratory analyses realized on numerous water samples collected in dredge plumes reveal (extremely) high water turbidity and high concentrations of inorganic particles in suspension, sometimes mixed with high concentrations of phytoplankton particles. The most peculiar optical property of these particles is a spectral light absorption coefficient significantly flatter than that of suspended particles in natural turbid waters (e.g., river plumes or estuarine maximum turbidity zones). This peculiar optical property is also detected on ocean color satellite data corrected for atmospheric effects, with a water reflectance signal higher than natural turbid waters at short visible wavebands (400–550 nm). Such an atypical spectral signature, which can be detected and mapped from space, makes the operational monitoring of dredge plumes in coastal waters using high-spatial-resolution (e.g., Sentinel2-MSI) satellite data possible. Full article

(This article belongs to the Section Environmental Remote Sensing)

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23 pages, 6367 KB

Open AccessArticle

SCNAnet: Structure-Aware Contrastive with Noise-Augmented Network for Unsupervised Change Detection

by Yijie Sun, Qingxi Wu and Nan Wang

Remote Sens. 2026, 18(9), 1427; https://doi.org/10.3390/rs18091427 - 4 May 2026

Viewed by 474

Abstract

Unsupervised change detection (UCD) is a key technique in Earth observation, aiming to identify and quantify surface changes over time by analyzing multi-temporal remote sensing images without manual annotations. Unlike supervised approaches that rely on ground reference to directly guide discriminative semantic learning, [...] Read more.

Unsupervised change detection (UCD) is a key technique in Earth observation, aiming to identify and quantify surface changes over time by analyzing multi-temporal remote sensing images without manual annotations. Unlike supervised approaches that rely on ground reference to directly guide discriminative semantic learning, UCD methods must construct their own reference. A mainstream strategy employs one temporal image as the reference and uses transformation models (e.g., style transfer networks) to align the other image in unchanged regions. Loss is then reduced by labeling hard-to-align pixels as “changes” and excluding them from the objective. However, this optimization process is dominated by style losses, which cause the model to learn to exclude regions that make only limited contributions to style-loss minimization, rather than to acquire discriminative representations of true geospatial changes. Such shortcut-driven optimization results in insufficient modeling of genuine change features and frequent misclassification of unchanged yet stylistically similar regions. To address these limitations, we propose SCNAnet, a novel framework that integrates three modules: a noise-perturbation consistency branch to suppress shortcut-driven learning, a structure-aware style transformation encoder to strengthen semantic representations of structural changes, and a frequency-attention decoder to refine the delineation of change regions. Extensive experiments on three benchmark datasets (GF-2, OSCD, and QuickBird) demonstrate the effectiveness of SCNAnet. Specifically, SCNAnet improves the F1 score by approximately 8% on the Montpellier dataset compared with the second-best method, demonstrating its effectiveness under challenging conditions. Full article

(This article belongs to the Special Issue New Image Processing Theories and Methods in Hyperspectral Remote Sensing)

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30 pages, 6341 KB

Open AccessArticle

Long-Term Assessment of Inter-Sensor Radiometric Biases Among SNPP, NOAA-20, NOAA-21 ATMS, and NOAA-19 AMSU-A Instruments Using the NOAA ICVS Framework

by Banghua Yan, Ninghai Sun, Flavio Iturbide-Sanchez, Changyong Cao and Lihang Zhou

Remote Sens. 2026, 18(9), 1426; https://doi.org/10.3390/rs18091426 - 3 May 2026

Viewed by 591

Abstract

This study evaluates mission-long inter-sensor radiometric calibration biases in Sensor Data Record (SDR) and/or Temperature Data Record (TDR) radiances from NOAA microwave sounders, including Advanced Technology Microwave Sounder (ATMS) (Suomi National Polar-orbiting Partnership or SNPP, NOAA-20, NOAA-21) and Advanced Microwave Sounding Unit-A (AMSU-A) [...] Read more.

This study evaluates mission-long inter-sensor radiometric calibration biases in Sensor Data Record (SDR) and/or Temperature Data Record (TDR) radiances from NOAA microwave sounders, including Advanced Technology Microwave Sounder (ATMS) (Suomi National Polar-orbiting Partnership or SNPP, NOAA-20, NOAA-21) and Advanced Microwave Sounding Unit-A (AMSU-A) (NOAA-19). Using four complementary validation techniques within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) system—32-day averaging, Community Radiative Transfer Model (CRTM) Double Difference (DD), Simultaneously Nadir Overpass (SNO), and sensor-DD via SNO—we characterize long-term performance. Results indicate that the SDR/TDR radiance quality remains stable and generally meets scientific requirements throughout their operational lifetimes with minimal anomalies; observed anomalies were infrequent and primarily correlated with calibration-table updates or spacecraft events or instrument degradation. Moreover, this research examines how radiometric calibration biases for the three ATMS instruments vary with Earth scene radiance or temperatures using the CRTM and SNO methods, as well as the radiance-dependency of inter-sensor calibration biases across the three instruments. Notably, due to its exceptional stability over 14 years, despite an approximate two-month data gap, the SNPP ATMS TDR and SDR datasets are recommended as the ideal reference to link legacy AMSU-A and Microwave Humidity Sounder (MHS) with Joint Polar Satellite System (JPSS), QuickSounder, and MetOp-Second Generation (MetOp-SG) microwave instruments. Beyond quantifying data quality, our multi-method framework with iSensor-RCBA effectively diagnosed critical issues, including a simulation error for CRTM ATMS radiance related to the CRTM spectral-response approximation and a NOAA-19 AMSU-A channel-8 performance anomaly. These findings confirm the long-term integrity of NOAA microwave sounder records and reinforce the value of integrated cross-sensor calibration assessments. Full article

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

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29 pages, 5239 KB

Open AccessArticle

Global Flood Vulnerability Model: Building-Level Assessment Using Multi-Source Remote Sensing

by Sakiru Olarewaju Olagunju, Ademi Sharipova, Adina Serikkyzy, Dariga Satybaldiyeva, Huseyin Atakan Varol and Ferhat Karaca

Remote Sens. 2026, 18(9), 1425; https://doi.org/10.3390/rs18091425 - 3 May 2026

Cited by 1 | Viewed by 864

Abstract

Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to [...] Read more.

Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to water, building height, and basement depth) through geographic context classification to quantify vulnerability from terrain and structural characteristics across coastal, fluvial, and pluvial settings. Building heights are extracted primarily from the Global Building Atlas, with gaps filled using a ConvNeXt neural network trained on high-resolution Light Detection and Ranging (LiDAR) ground truth from four cities (within-city MAE 1.35–1.91 m, cross-city MAE 2.05–3.47 m). Terrain metrics are derived from a combination of hierarchical digital elevation models (DEM) (USGS 3DEP 10 m, AHN LiDAR 0.5 m, UK Environment Agency DTM 1 m, Australia 5 m) and global datasets (NASADEM 30 m, Copernicus GLO-30). Hydrographic networks are sourced from OpenStreetMap and Natural Earth. Implementation through Google Earth Engine requires only coordinates as input, returning a five-level vulnerability index with multi-hazard decomposition (fluvial, coastal, pluvial) and SHapley Additive exPlanations (SHAP)-based attribution identifying dominant drivers. Validation across 183 independent locations in Germany, UK, and USA demonstrates robust performance: Area Under Curve 0.855 for separating flooded from non-flooded sites, weighted Cohen’s kappa 0.493 across regulatory zones, and Spearman ρ 0.746 against Federal Emergency Management Agency (FEMA) classifications. Sensitivity analysis across 625 parameter configurations confirms stability, and DEM resolution experiments show that global 30 m elevation data produces category reclassification in only 5.3–8.6% of locations compared to high-resolution sources. Application to the 2024 Kazakhstan floods identifies 118 high-vulnerability locations across 581 assessment points, with vulnerability patterns matching documented inundation. GFVM advances remote sensing applications for disaster risk assessment by demonstrating that multi-source geospatial data fusion enables building-level vulnerability screening without local calibration or field surveys. Full article

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19 pages, 28907 KB

Open AccessArticle

Long-Term Surface Uplift Driven by Groundwater Recovery in Xi’an, China: InSAR Constraints on Aquifer Storage and Hydraulic Diffusivity

by Weilai Sun, Rongrong Zhou, Xiaojuan Wu and Teng Wang

Remote Sens. 2026, 18(9), 1424; https://doi.org/10.3390/rs18091424 - 3 May 2026

Viewed by 608

Abstract

Vertical land motion in urban areas is a critical manifestation of groundwater, directly affecting infrastructure stability and groundwater sustainability. While land subsidence caused by groundwater extraction has been widely investigated, the opposite process—surface uplift induced by groundwater recovery—remains poorly documented or understood, particularly [...] Read more.

Vertical land motion in urban areas is a critical manifestation of groundwater, directly affecting infrastructure stability and groundwater sustainability. While land subsidence caused by groundwater extraction has been widely investigated, the opposite process—surface uplift induced by groundwater recovery—remains poorly documented or understood, particularly regarding its hydrological mechanisms and potential hazards. Here, we integrate InSAR time-series analysis of Sentinel-1 imagery (2017–2025) with groundwater well records to quantify the spatial–temporal characteristics of uplift in Xi’an, China, and to evaluate its hydrogeological drivers. Results reveal a persistent surface uplift zone south of the ancient city in Xi’an, with rates up to 20 mm/yr. The uplift correlates closely with rising groundwater levels in the shallow confined aquifer, indicating a strong coupling between aquifer recharge and surface uplift. Calculated storage coefficients and hydraulic diffusivity values highlight marked spatial variations, constrained by some ground fissures that act as both mechanical discontinuities and hydrological barriers controlling pressure diffusion. Time-series analysis further identifies the eastward propagation of subsidence-to-uplift reversal in Yuhuazhai, an urban village with groundwater injection, which is used to quantify the diffusivity coefficients. Field investigations show that rapid groundwater rebound can lead to uplift-related hazards, such as basement seepage, underscoring that surface uplift must be considered alongside subsidence in urban water management. Full article

(This article belongs to the Special Issue Role of SAR/InSAR Techniques in Investigating Ground Deformation)

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38 pages, 26491 KB

Open AccessArticle

A Hierarchical Multi-Scale Denoising Framework for UAV-Derived Digital Subsidence Models in Coal Mining Areas

by Xi Zhang, Jiazheng Han, Zhanjie Feng, Lingtong Meng, Ruihao Cui and Zhenqi Hu

Remote Sens. 2026, 18(9), 1423; https://doi.org/10.3390/rs18091423 - 3 May 2026

Viewed by 619

Abstract

Mining-induced subsidence monitoring is essential for safe coal production and ecological protection in mining areas. UAV photogrammetry has become a widely adopted technique for constructing Digital Subsidence Models (DSuM); however, multi-scale composite noise significantly limits model accuracy and parameter extraction reliability. Taking the [...] Read more.

Mining-induced subsidence monitoring is essential for safe coal production and ecological protection in mining areas. UAV photogrammetry has become a widely adopted technique for constructing Digital Subsidence Models (DSuM); however, multi-scale composite noise significantly limits model accuracy and parameter extraction reliability. Taking the 2S201 working face of Wangjiata Coal Mine in a western arid–semi-arid region as the study area, this study systematically investigates DSuM noise characteristics and proposes a hierarchical multi-scale denoising framework. First, subsidence value interval stratification is employed to analyze the spatial distribution of noise. Based on this analysis, a two-stage strategy is developed. In the first stage, large-scale outliers are identified and removed using an improved DBSCAN algorithm with empirically calibrated and density-adaptive parameter computation. In the second stage, small-scale mixed noise is suppressed through a curvature-adaptive multi-stage denoising method. Validation using 20 ground monitoring points demonstrates that the RMSE decreases from 154 mm to 86 mm after large-scale denoising and further to 59 mm, achieving a 61.5% overall accuracy improvement. The denoised model exhibits enhanced surface continuity, smoother deformation profiles, and clearer subsidence boundaries while preserving overall deformation trends. The proposed framework effectively improves DSuM geometric accuracy and spatial consistency, providing reliable technical support for subsidence monitoring with improved accuracy in complex mining environments. Full article

(This article belongs to the Special Issue Intelligent Processing, Mining and Application of Remote Sensing Information)

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26 pages, 26133 KB

Open AccessArticle

DFS-YOLO: A Dynamic Feature Collaboration and State Space Framework for UAV-Based Infrared Object Detection

by Ziyan Wang, Wangbin Li and Kaimin Sun

Remote Sens. 2026, 18(9), 1422; https://doi.org/10.3390/rs18091422 - 3 May 2026

Viewed by 1018

Abstract

UAV-based infrared target detection presents inherent challenges, including low signal-to-noise ratios, texture degradation, and severe scale variations. To address these issues, we propose DFS-YOLO, an approach based on dynamic feature collaboration and efficient state-space modeling. We introduce a Dynamic Range-Calibrated Area Attention (DRCAA) [...] Read more.

UAV-based infrared target detection presents inherent challenges, including low signal-to-noise ratios, texture degradation, and severe scale variations. To address these issues, we propose DFS-YOLO, an approach based on dynamic feature collaboration and efficient state-space modeling. We introduce a Dynamic Range-Calibrated Area Attention (DRCAA) module in the backbone to stabilize feature activations under strong thermal clutter. Within the neck architecture, an Efficient Attentional Scale-Sequence Fusion (EASF) strategy reduces cross-scale semantic misalignment and ensures precise spatial coherence. Additionally, an EfficientViM-based state-space module captures global contextual dependencies while maintaining linear computational complexity. Finally, the Content-Guided Triple-Attention Fusion (CGTAFusion) module maximizes feature discriminability by calibrating fusion representations across the channel, spatial, and pixel dimensions. Extensive experiments on the HIT-UAV and IRSTD-1k benchmarks validate the efficacy of the DFS-YOLO framework. Compared to the baseline YOLOv12, DFS-YOLO’s performance has been significantly improved, increasing mAP@50 and mAP@50-95 by 10.16% and 7.55% on HIT-UAV, and by 1.84% and 3.18% on IRSTD-1k, respectively. These quantitative gains establish DFS-YOLO as a highly robust and state-of-the-art solution for complex infrared aerial surveillance. Full article

(This article belongs to the Special Issue Remote Sensing Image Change Detection and Feature Enhancement Based on Deep Learning)

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18 pages, 2521 KB

Open AccessArticle

Evaluation of the Potential of Very-High-Resolution Satellite Imagery in Large-Scale Mapping

by Ilyas Afa, Adnane Labbaci, Laila El Ghazouani and Hassan Radoine

Remote Sens. 2026, 18(9), 1421; https://doi.org/10.3390/rs18091421 - 3 May 2026

Viewed by 1199

Abstract

With the rapid and ongoing expansion of urban areas, the need for accurate, reliable, and regularly updated topographic maps has become increasingly critical for planning and sustainable development. While traditional aerial photogrammetry—whether analog or digital—has long been the standard for such tasks, it [...] Read more.

With the rapid and ongoing expansion of urban areas, the need for accurate, reliable, and regularly updated topographic maps has become increasingly critical for planning and sustainable development. While traditional aerial photogrammetry—whether analog or digital—has long been the standard for such tasks, it remains costly, time-consuming, and logistically demanding, particularly when large or inaccessible regions are involved. This study proposes an alternative approach based on very-high-resolution satellite imagery, focusing specifically on data acquired from Morocco’s Mohammed VI A and B satellites. The research evaluates the capacity of this satellite imagery to support large-scale topographic mapping, both in terms of geometric accuracy and the ability to identify essential urban features. To validate the results, we conducted a comparative analysis of satellite data with conventional photogrammetric imagery from analog cameras (RMK TOP) and digital sensors (ADS, DMC), using ground control points (GCPs) and differential GPS (DGPS) measurements for calibration and accuracy assessment. The outcomes demonstrate that planimetric accuracy from satellite imagery meets the required standards for mapping at 1:10,000 and 1:5000 scales. However, altimetric accuracy is closer to the upper permissible limits, especially in applications requiring finer detail. While major urban elements such as roads, buildings, and vegetation are well identified, smaller infrastructure components, such as power lines, remain challenging to detect. Despite these limitations, the study highlights the growing potential of satellite imagery as a cost-effective and operationally efficient alternative to traditional methods, particularly in rapidly evolving urban environments where frequent map updates are essential. Integration with GeoAI workflows is identified as a key direction for future research and is not part of the current methodology. Full article

(This article belongs to the Special Issue Remote Sensing in Geomatics (Second Edition))

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36 pages, 2097 KB

Open AccessReview

Role of Crop Salt Tolerance in Enhancing Remote Sensing-Based Soil Salinity Mapping Across Irrigated Agroecosystems: A Review

by Zhassulan Smanov, Jilili Abuduwaili, Alim Samat, Kanat Samarkhanov, Shakhislam Laiskhanov, Kanat Kulymbet, Azamat Yershibul, Saken Duisekov, Assiya Massakbayeva and Zhanerke Sharapkhanova

Remote Sens. 2026, 18(9), 1420; https://doi.org/10.3390/rs18091420 - 3 May 2026

Cited by 1 | Viewed by 809

Abstract

Soil salinization poses a persistent threat to irrigated agroecosystems, yet remote sensing-based salinity assessment remains predominantly calibrated against bulk electrical conductivity without fully integrating crop physiological variability. This review examines the evolution of remote sensing approaches for soil salinity mapping (1994–2024), with particular [...] Read more.

Soil salinization poses a persistent threat to irrigated agroecosystems, yet remote sensing-based salinity assessment remains predominantly calibrated against bulk electrical conductivity without fully integrating crop physiological variability. This review examines the evolution of remote sensing approaches for soil salinity mapping (1994–2024), with particular emphasis on the role of crop salt tolerance in shaping spectral interpretation and mapping accuracy. A systematic synthesis of 58 peer-reviewed studies retrieved from the Scopus database was conducted using bibliometric analysis and structured full-text thematic classification to evaluate methodological trends and conceptual integration across soil, crops, and spectral domains. The results reveal substantial technological advancement, including multispectral and hyperspectral sensing, machine learning frameworks, and multi-source data integration. However, most approaches remain surface-oriented and statistically calibrated, with limited operationalization of crop-specific tolerance thresholds, root-zone salinity dynamics, and hydrochemical variability. The findings indicate that crop salt tolerance functions as a mediating factor within the soil–plant–spectral continuum, influencing the stability and transferability of spectral–salinity relationships. Integrating physiological tolerance parameters and subsurface processes into modeling frameworks is essential for improving agronomic interpretability and supporting more reliable salinity management in irrigated systems. Full article

(This article belongs to the Special Issue Crop Yield Prediction Using Remote Sensing Techniques)

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35 pages, 14363 KB

Open AccessArticle

Assessing GAN Super-Resolution in Grasslands: The Role of Spatial Heterogeneity and Textural Complexity

by Efrain Noa-Yarasca, Javier Osorio Leyton, Nada Jumaa, Haoyu Niu and Lonesome Malambo

Remote Sens. 2026, 18(9), 1419; https://doi.org/10.3390/rs18091419 - 3 May 2026

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Abstract

High-resolution imagery is essential for monitoring heterogeneous grassland ecosystems, yet the performance of generative adversarial network (GAN) super-resolution under varying landscape heterogeneity and operational application scenarios remains unclear. This study presents a landscape-aware evaluation of super-resolution methods in semi-arid savanna grasslands of the [...] Read more.

High-resolution imagery is essential for monitoring heterogeneous grassland ecosystems, yet the performance of generative adversarial network (GAN) super-resolution under varying landscape heterogeneity and operational application scenarios remains unclear. This study presents a landscape-aware evaluation of super-resolution methods in semi-arid savanna grasslands of the Edwards Plateau (Texas, USA) using paired multispectral imagery from PlanetScope (3 m) and unmanned aerial vehicle (UAV) platforms (0.03 m). Two GAN models, SRGAN and ESRGAN, were compared with a bicubic interpolation baseline. Image tiles were systematically stratified along ecologically relevant gradients of vegetation condition (NDVI quartiles), spatial structure (woody patch-based clusters), and textural complexity (GLCM entropy quartiles). Model performance was evaluated across three operational frameworks: intra-sensor downscaling, cross-sensor downscaling, and intra-to-cross generalization. Reconstruction fidelity was quantified using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), complemented by variability analysis to assess performance stability. Landscape heterogeneity strongly influenced downscaling outcomes. SRGAN performance declined in areas with dense vegetation, aggregated woody structure, and high-entropy textures, with large variability under cross-sensor and generalization scenarios. In contrast, ESRGAN demonstrated consistently robust performance across landscape gradients, whereas bicubic interpolation performed well only under intra-sensor conditions and drastically degraded under sensor transfer. These results demonstrate that vegetation condition, structural heterogeneity, and sensor-transfer scenarios jointly constrain super-resolution performance. Rather than serving as a model comparison exercise, this study emphasizes a landscape-aware framework for understanding how ecological heterogeneity and operational domain shifts jointly shape super-resolution behavior in grassland ecosystems, providing guidance for more reliable applications of deep learning-based remote sensing methods. Full article

(This article belongs to the Special Issue AI-Driven Mapping Using Remote Sensing Data)

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Open AccessArticle

Gated Lightweight CNN-Transformer Fusion for Real-Time Flood Segmentation on Satellite Internet Terminals Under Triple-Disruption Emergency Conditions

by Yungui Nie, Zhiguo Shi, Jianing Li and HuiLing Ge

Remote Sens. 2026, 18(9), 1418; https://doi.org/10.3390/rs18091418 - 3 May 2026

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Abstract

During flood disasters, on-site operations often face the “triple disruption” of network outages, power cuts and blocked roads. This renders terrestrial cellular infrastructure inoperable and disrupts communication links. Satellite internet can partially restore emergency communications thanks to its wide-area coverage and resistance to [...] Read more.

During flood disasters, on-site operations often face the “triple disruption” of network outages, power cuts and blocked roads. This renders terrestrial cellular infrastructure inoperable and disrupts communication links. Satellite internet can partially restore emergency communications thanks to its wide-area coverage and resistance to ground damage. However, limited computing power, memory and unstable bandwidth at the terminal prevent cloud-based flood segmentation from providing near-real-time situational awareness. This paper therefore proposes a lightweight semantic flood segmentation framework for emergency terminals that uses satellite internet. This comprises a parallel dual-branch design with a lightweight U-Net-style convolutional neural network (CNN) branch for local boundary details and a compact Transformer branch for global context. A dynamic gated fusion mechanism (DGFM) balances local texture and global information adaptively. Experiments on the public synthetic aperture radar (SAR) dataset Sen1Floods11 demonstrate that the hybrid architecture strikes a balance between accuracy and inference efficiency. The proposed method combines gated fusion with quality-aware training. Compared to a lightweight CNN baseline and state-of-the-art segmentation models using the same protocol, the proposed configuration (Hybrid-Gated with Quality-Aware Training) achieves the highest mean intersection over union and F1 score among the compared fusion variants, while maintaining competitive false alarm and risk-sensitive performance under deployment constraints. This aligns with the preferences of emergency decision makers. The framework provides a deployable perception module for emergency systems supported by low-orbit satellites and terrestrial networks under triple-disruption conditions. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management (Second Edition))

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Remote Sens., Volume 18, Issue 9 (May-1 2026) – 186 articles

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