Remote Sensing

26 pages, 2406 KB

Open AccessArticle

Ecological Change in Minnesota’s Carbon Sequestration and Oxygen Release Service: A Multidimensional Assessment Using Multi-Temporal Remote Sensing Data

by Donghui Shi

Remote Sens. 2026, 18(3), 391; https://doi.org/10.3390/rs18030391 - 23 Jan 2026

Abstract

Carbon sequestration and oxygen release (CSOR) are core regulating functions of terrestrial ecosystems. However, regional assessments often fail to (i) separate scale-driven high supply from per-area efficiency, (ii) detect structural instability and degradation risk from long-term trajectories, and (iii) provide evidence that is [...] Read more.

Carbon sequestration and oxygen release (CSOR) are core regulating functions of terrestrial ecosystems. However, regional assessments often fail to (i) separate scale-driven high supply from per-area efficiency, (ii) detect structural instability and degradation risk from long-term trajectories, and (iii) provide evidence that is comparable across units for management prioritization. Using Minnesota, USA, we integrated satellite-derived net primary productivity (NPP; 1998–2021) with a Quantity–Intensity–Structure (Q–I–S) framework to quantify CSOR, detect trends and change points (Mann–Kendall and Pettitt tests), map spatial clustering and degradation risk (Exploratory Spatial Data Analysis, ESDA), and attribute natural and human drivers (principal component regression and GeoDetector). CSOR increased overall from 1998 to 2021, with a marked shift around 2013 from a slight, variable decline to sustained recovery. Spatially, CSOR showed a persistent north–south gradient, with higher and improving services in northern Minnesota and lower, more degraded services in the south; persistent degradation was concentrated in a central high-risk belt. The Q–I–S framework also revealed inconsistencies between total supply and condition, identifying high-supply yet degrading areas and low-supply areas with recovery potential that are not evident from the totals alone. Climate variables primarily controlled CSOR quantity and structure, whereas human factors more strongly influenced intensity; the interactions of the two further shaped observed patterns. These results provide an interpretable and transferable basis for diagnosing degradation and prioritizing restoration under long-term environmental change. Full article

(This article belongs to the Special Issue Ecological Change with Multi-Scale Spatial-Temporal Remote Sensing Data)

51 pages, 1843 KB

Open AccessSystematic Review

Remote Sensing of Woody Plant Encroachment: A Global Systematic Review of Drivers, Ecological Impacts, Methods, and Emerging Innovations

by Abdullah Toqeer, Andrew Hall, Ana Horta and Skye Wassens

Remote Sens. 2026, 18(3), 390; https://doi.org/10.3390/rs18030390 - 23 Jan 2026

Abstract

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified [...] Read more.

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

(This article belongs to the Special Issue Remote Sensing Tools for Monitoring Vegetation and Enhancing Biodiversity Conservation Strategies)

27 pages, 1996 KB

Open AccessArticle

Salient Object Detection for Optical Remote Sensing Images Based on Gated Differential Unit

by Mingsi Sun, Ting Lan, Wei Wang and Pingping Liu

Remote Sens. 2026, 18(3), 389; https://doi.org/10.3390/rs18030389 - 23 Jan 2026

Abstract

Salient object detection in optical remote sensing images has attracted extensive research interest in recent years. However, CNN-based methods are generally limited by local receptive fields, while ViT-based methods suffer from common defects in noise suppression, channel selection, foreground-background distinction, and detail enhancement. [...] Read more.

Salient object detection in optical remote sensing images has attracted extensive research interest in recent years. However, CNN-based methods are generally limited by local receptive fields, while ViT-based methods suffer from common defects in noise suppression, channel selection, foreground-background distinction, and detail enhancement. To address these issues and integrate long-distance contextual dependencies, we introduce GDUFormer, an ORSI-SOD detection method based on the ViT backbone and Gated Differential Units (GDU). Specifically, the GDU consists of two key components—Full-Dimensional Gated Attention (FGA) and Hierarchical Differential Dynamic Convolution (HDDC). FGA consists of two branches aimed at filtering effective features from the information flow. The first branch focuses on aggregating spatial local information under multiple receptive fields and filters the local feature maps via a grouping mechanism. The second branch imitates the Vision Mamba to acquire high-level reasoning and abstraction capabilities, enabling weak channel filtering. HDDC primarily utilizes distance decay and hierarchical intensity difference capture mechanisms to generate dynamic kernel spatial weights, thereby facilitating the convolution kernel to fully mix long-range contextual dependencies. Among these, the intensity difference capture mechanism can adaptively divide hierarchies and allocate parameters according to kernel size, thus realizing varying levels of difference capture in the kernel space. Extensive quantitative and qualitative experiments demonstrate the effectiveness and rationality of GDUFormer and its internal components. Full article

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24 pages, 10934 KB

Open AccessArticle

A Few-Shot Object Detection Framework for Remote Sensing Images Based on Adaptive Decision Boundary and Multi-Scale Feature Enhancement

by Lijiale Yang, Bangjie Li, Dongdong Guan and Deliang Xiang

Remote Sens. 2026, 18(3), 388; https://doi.org/10.3390/rs18030388 - 23 Jan 2026

Abstract

Given the high cost of acquiring large-scale annotated datasets, few-shot object detection (FSOD) has emerged as an increasingly important research direction. However, existing FSOD methods face two critical challenges in remote sensing images (RSIs): (1) features of small targets within remote sensing images [...] Read more.

Given the high cost of acquiring large-scale annotated datasets, few-shot object detection (FSOD) has emerged as an increasingly important research direction. However, existing FSOD methods face two critical challenges in remote sensing images (RSIs): (1) features of small targets within remote sensing images are incompletely represented due to extremely small-scale and cluttered backgrounds, which weakens discriminability and leads to significant detection degradation; (2) unified classification boundaries fail to handle the distinct confidence distributions between well-sampled base classes and sparsely sampled novel classes, leading to ineffective knowledge transfer. To address these issues, we propose TS-FSOD, a Transfer-Stable FSOD framework with two key innovations. First, the proposed detector integrates a Feature Enhancement Module (FEM) leveraging hierarchical attention mechanisms to alleviate small target feature attenuation, and an Adaptive Fusion Unit (AFU) utilizing spatial-channel selection to strengthen target feature representations while mitigating background interference. Second, Dynamic Temperature-scaling Learnable Classifier (DTLC) employs separate learnable temperature parameters for base and novel classes, combined with difficulty-aware weighting and dynamic adjustment, to adaptively calibrate decision boundaries for stable knowledge transfer. Experiments on DIOR and NWPU VHR-10 datasets show that TS-FSOD achieves competitive or superior performance compared to state-of-the-art methods, with improvements up to 4.30% mAP, particularly excelling in 3-shot and 5-shot scenarios. Full article

(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)

28 pages, 8611 KB

Open AccessArticle

Interpretable Deep Learning for Forecasting Camellia oleifera Yield in Complex Landscapes by Integrating Improved Spectral Bloom Index and Environmental Parameters

by Tong Shi, Shi Cao, Xia Lu, Lina Ping, Xiang Fan, Meiling Liu and Xiangnan Liu

Remote Sens. 2026, 18(3), 387; https://doi.org/10.3390/rs18030387 - 23 Jan 2026

Abstract

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote [...] Read more.

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

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23 pages, 1277 KB

Open AccessArticle

A Few-Shot Optical Classification Approach for Meteorological Lightning Monitoring: Leveraging Frame Difference and Triplet Network

by Mengmeng Xiao, Yulong Yan, Qilin Zhang, Yan Liu, Xingke Pan, Bingzhe Dai and Chunxu Duan

Remote Sens. 2026, 18(3), 386; https://doi.org/10.3390/rs18030386 - 23 Jan 2026

Abstract

To address the challenges of scarce labeled samples, strong instantaneity, and variable morphology in lightning optical classification—issues that traditional methods struggle to handle efficiently and often require extensive manual intervention—we propose a frame difference triplet network (FD-TripletNet) tailored for few-shot lightning recognition. The [...] Read more.

To address the challenges of scarce labeled samples, strong instantaneity, and variable morphology in lightning optical classification—issues that traditional methods struggle to handle efficiently and often require extensive manual intervention—we propose a frame difference triplet network (FD-TripletNet) tailored for few-shot lightning recognition. The lightning optical dataset used in this study was collected from two observation stations over six months, comprising 459 video samples that include lightning events with diverse morphologies (e.g., branched, spherical) and non-lightning events prone to misclassification (e.g., strong light interference, moving objects). Considering the critical feature of lightning—abrupt single-frame changes—we introduce adjacent frame difference matrices as model input to explicitly capture transient brightness variations, reducing noise from static backgrounds. To enhance discriminative ability in few-shot scenarios, the model leverages Triplet Loss to compact intra-class features and separate inter-class features, combined with a dynamic sample matching strategy to focus on challenging cases. The experimental results show that FD-TripletNet achieves a classification accuracy of 94.8% on the dataset, outperforming traditional methods and baseline deep learning models. It effectively reduces the False Negative Rate (FNR) to 3.2% and False Positive Rate (FPR) to 7.4%, successfully distinguishing between lightning and non-lightning events, thus providing an efficient solution for real-time lightning monitoring in meteorological applications. Full article

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22 pages, 14353 KB

Open AccessArticle

Moho Fold Structure Beneath the East China Sea and Its Tectonic Implications

by Hangtao Yu, Chuang Xu, Mingming Wen and Chunhong Wu

Remote Sens. 2026, 18(3), 385; https://doi.org/10.3390/rs18030385 - 23 Jan 2026

Abstract

Moho fold structures provide critical insights into the tectonic evolution of the East China Sea. However, previous models exhibit substantial uncertainties, primarily resulting from the unaccounted gravitational effects of crustal sources and insufficient constraints on inversion parameters. In this study, we applied wavelet [...] Read more.

Moho fold structures provide critical insights into the tectonic evolution of the East China Sea. However, previous models exhibit substantial uncertainties, primarily resulting from the unaccounted gravitational effects of crustal sources and insufficient constraints on inversion parameters. In this study, we applied wavelet multi-scale analysis and the power spectrum method to remove crustal contributions, combined with an improved Bott’s method to achieve robust hyperparameter estimations. The Moho topographic model obtained through this method exhibits a significantly enhanced accuracy, with a root mean square deviation from seismic control points reduced by approximately 30% compared to other models. The resulting Moho fold structure reveals three key findings: (1) The South China Block has undergone vertical stress that forced the mantle to subduct. (2) In the northeastern and central parts of the Ryukyu Arc, vertical subduction forces are dominant. In the southwestern part of the Ryukyu Arc, vertical subduction forces are in balance with another force associated with mantle upwelling. (3) There is no interplate stress beneath the Okinawa Trough, and its crustal thinning may have been influenced by upwelling in the mantle. Full article

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

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21 pages, 45200 KB

Open AccessArticle

SWOT Observations of Bimodal Seasonal Submesoscale Processes in the Kuroshio Large Meander

by Xiaoyu Zhao and Yanjiang Lin

Remote Sens. 2026, 18(3), 384; https://doi.org/10.3390/rs18030384 - 23 Jan 2026

Abstract

Wide-swath satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission provides an unprecedented opportunity to directly observe kilometer-scale ocean dynamics in two dimensions. In this study, we identify an atypical bimodal seasonal cycle of submesoscale processes in the Kuroshio Large Meander [...] Read more.

Wide-swath satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission provides an unprecedented opportunity to directly observe kilometer-scale ocean dynamics in two dimensions. In this study, we identify an atypical bimodal seasonal cycle of submesoscale processes in the Kuroshio Large Meander (KLM) region south of Japan using SWOT observations during 2023–2025. Submesoscale eddy kinetic energy (EKE) displays a pronounced winter maximum (December–January) as expected for midlatitude oceans, but also a distinct secondary maximum in late summer (August–September) that coincides with the Northwest Pacific typhoon season. SWOT-based eddy statistics reveal that cyclonic and anticyclonic eddies exhibit enhanced occurrence and intensity in winter and late summer. MITgcm LLC4320 outputs demonstrate that the late-summer EKE peak is primarily driven by typhoons, which rapidly deepen the mixed layer and intensify frontal gradients, leading to an intensification of submesoscale eddies. The Kuroshio path further modulates this response. During the KLM state, buoyancy gradients and mixed-layer available potential energy are amplified, allowing storm forcing to generate strong submesoscale activity. Together, typhoon forcing and current-path variability modify the traditionally winter-dominated submesoscale regime. These findings highlight the unique capability of SWOT to resolve submesoscale processes in western boundary currents during extreme weather events. Full article

(This article belongs to the Special Issue Advances of Ocean Circulation and Air-Sea Interaction Using Remote Sensing Techniques)

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30 pages, 25744 KB

Open AccessArticle

Long-Term Dynamics and Transitions of Surface Water Extent in the Dryland Wetlands of Central Asia Using a Hybrid Ensemble–Occurrence Approach

by Kanchan Mishra, Hervé Piégay, Kathryn E. Fitzsimmons and Philip Weber

Remote Sens. 2026, 18(3), 383; https://doi.org/10.3390/rs18030383 - 23 Jan 2026

Abstract

Wetlands in dryland regions are rapidly degrading under the combined effects of climate change and human regulation, yet long-term, seasonally resolved assessments of surface water extent (SWE) and its dynamics remain scarce. Here, we map and analyze seasonal surface water extent (SWE) over [...] Read more.

Wetlands in dryland regions are rapidly degrading under the combined effects of climate change and human regulation, yet long-term, seasonally resolved assessments of surface water extent (SWE) and its dynamics remain scarce. Here, we map and analyze seasonal surface water extent (SWE) over the period 2000–2024 in the Ile River Delta (IRD), south-eastern Kazakhstan, using Landsat TM/ETM+/OLI data within the Google Earth Engine (GEE) framework. We integrate multiple indices using the modified Normalized Difference Water Index (mNDWI), Automated Water Extraction Index (AWEI) variants, Water Index 2015 (WI2015), and Multi-Band Water Index (MBWI) with dynamic Otsu thresholding. The resulting index-wise binary water maps are merged via ensemble agreement (intersection, majority, union) to delineate three SWE regimes: stable (persists most of the time), periodic (appears regularly but not in every season), and ephemeral (appears only occasionally). Validation against Sentinel-2 imagery showed high accuracy F1-Score/Overall accuracy (F1/OA ≈ 0.85/85%), confirming our workflow to be robust. Hydroclimatic drivers were evaluated through modified Mann–Kendall (MMK) and Spearman’s (r) correlations between SWE, discharge (D), water level (WL), precipitation (P), and air temperature (AT), while a hybrid ensemble–occurrence framework was applied to identify degradation and transition patterns. Trend analysis revealed significant long–term declines, most pronounced during summer and fall. Discharge is predominantly controlled by stable spring SWE, while discharge and temperature jointly influence periodic SWE in summer–fall, with warming reducing the delta surface water. Ephemeral SWE responds episodically to flow pulses, whereas precipitation played a limited role in this semi–arid region. Spatially, area(s) of interest (AOI)-II/III (the main distributary system) support the most extensive yet dynamic wetlands. In contrast, AOI-I and AOI-IV host smaller, more constrained wetland mosaics. AOI-I shows persistence under steady low flows, while AOI-IV reflects a stressed system with sporadic high-water levels. Overall, the results highlight the dominant influence of flow regulation and distributary allocation on IRD hydrology and the need for ecologically timed releases, targeted restoration, and transboundary cooperation to sustain delta resilience. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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36 pages, 39268 KB

Open AccessArticle

Spectral Feature Integration and Ensemble Learning Optimization for Regional-Scale Landslide Susceptibility Mapping in Mountainous Areas

by Yun Tian, Taorui Zeng, Linfeng Wang, Gang Chen, Sihang Yang, Hao Chen and Ligang Wang

Remote Sens. 2026, 18(3), 382; https://doi.org/10.3390/rs18030382 - 23 Jan 2026

Abstract

Current research on landslide susceptibility modeling is often constrained by reliance on conventional topographic and geological features, potentially overlooking the discriminative power of surface material properties derived from multi-source remote sensing. This study aims to enhance the accuracy and reliability of susceptibility assessment [...] Read more.

Current research on landslide susceptibility modeling is often constrained by reliance on conventional topographic and geological features, potentially overlooking the discriminative power of surface material properties derived from multi-source remote sensing. This study aims to enhance the accuracy and reliability of susceptibility assessment by innovatively integrating spectral information and advanced machine learning techniques. Focusing on Chongqing, a landslide-prone mountainous region in China, this work conducted three innovative investigations: it (i) introduced 12 spectral features into the feature set; (ii) systematically evaluated spectral features contribution, redundancy, and set completeness through feature engineering; and (iii) implemented a comprehensive Stacking ensemble framework with multiple meta-learners and enhancement strategies (Bagging and Cross-Training) to identify the optimal integration scheme. The key results show that spectral features provided a significant positive impact, boosting the AUC of tree-based ensemble models by up to 4.52%. The optimal model, a Stacking ensemble with Bagging_XGBoost as the meta-learner, achieved a superior test AUC of 0.8611, outperforming all individual base learners. Furthermore, the spatial analysis revealed a concentration of high and very high susceptibility areas in Engineering Geological Zone I, which represents approximately 38% of such areas. This study provides a replicable framework for enhancing landslide susceptibility mapping through the integration of spectral features and ensemble learning, offering a scientific basis for targeted risk management and mitigation planning in complex mountainous terrains. Full article

(This article belongs to the Special Issue Multiplatform and Multisensor Applications for Landslide Characterization and Monitoring)

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23 pages, 53605 KB

Open AccessArticle

Multispectral Sparse Cross-Attention Guided Mamba Network for Small Object Detection in Remote Sensing

by Wen Xiang, Yamin Li, Liu Duan, Qifeng Wu, Jiaqi Ruan, Yucheng Wan and Sihan Wu

Remote Sens. 2026, 18(3), 381; https://doi.org/10.3390/rs18030381 - 23 Jan 2026

Abstract

Remote sensing small object detection remains a challenging task due to limited feature representation and interference from complex backgrounds. Existing methods that rely exclusively on either visible or infrared modalities often fail to achieve both accuracy and robustness in detection. Effectively integrating cross-modal [...] Read more.

Remote sensing small object detection remains a challenging task due to limited feature representation and interference from complex backgrounds. Existing methods that rely exclusively on either visible or infrared modalities often fail to achieve both accuracy and robustness in detection. Effectively integrating cross-modal information to enhance detection performance remains a critical challenge. To address this issue, we propose a novel Multispectral Sparse Cross-Attention Guided Mamba Network (MSCGMN) for small object detection in remote sensing. The proposed MSCGMN architecture comprises three key components: Multispectral Sparse Cross-Attention Guidance Module (MSCAG), Dynamic Grouped Mamba Block (DGMB), and Gated Enhanced Attention Module (GEAM). Specifically, the MSCAG module selectively fuses RGB and infrared (IR) features using sparse cross-modal attention, effectively capturing complementary information across modalities while suppressing redundancy. The DGMB introduces a dynamic grouping strategy to improve the computational efficiency of Mamba, enabling effective global context modeling. In remote sensing images, small objects occupy limited areas, making it difficult to capture their critical features. We design the GEAM module to enhance both global and local feature representations for small object detection. Experiments on the VEDAI and DroneVehicle datasets show that MSCGMN achieves mAP50 scores of 83.9% and 84.4%, outperforming existing state-of-the-art methods and demonstrating strong competitiveness in small object detection tasks. Full article

(This article belongs to the Special Issue Image Fusion and Object Detection Using Multi-Modal Remote Sensing Data)

30 pages, 24852 KB

Open AccessArticle

Multi-Source Remote Sensing Data-Driven Susceptibility Mapping of Retrogressive Thaw Slumps in the Yangtze River Source Region

by Yun Tian, Taorui Zeng, Qing Lü, Hongwei Jiang, Sihan Yang, Hang Cao and Wenbing Yu

Remote Sens. 2026, 18(3), 380; https://doi.org/10.3390/rs18030380 - 23 Jan 2026

Abstract

Despite the ecological sensitivity of the Yangtze River Source Region (YRSR), the current research critically lacks a quantified assessment of the spatial occurrence probability of Retrogressive Thaw Slumps (RTSs) in this specific high-altitude terrain. This study aims to bridge this knowledge gap by [...] Read more.

Despite the ecological sensitivity of the Yangtze River Source Region (YRSR), the current research critically lacks a quantified assessment of the spatial occurrence probability of Retrogressive Thaw Slumps (RTSs) in this specific high-altitude terrain. This study aims to bridge this knowledge gap by establishing a robust susceptibility assessment framework to accurately model the spatial distribution and risk levels of RTSs. The innovations of this research include (i) the construction of a complete and up-to-date 2024 RTS inventory for the entire YRSR based on high-resolution optical remote sensing; (ii) the integration of time-series spectral features (e.g., vegetation and moisture trends) alongside static topographic variables to enhance the physical interpretability of machine learning models; and (iii) the application of advanced ensemble learning algorithms combined with SHAP analysis to establish a comprehensive RTS susceptibility zonation. The results reveal a rapid intensification of instability, evidenced by an 83.5% surge in RTS abundance, with the CatBoost model achieving exceptional accuracy (AUC = 0.994), and identifying that specific static topographic factors (particularly elevations between 4693 and 4812 m and north-to-east aspect) and dynamic spectral anomalies (indicated by declining vegetation vigor and increasing surface wetness) are the dominant drivers controlling RTS distribution. This study provides essential baseline data and spatial guidance for ecological conservation and engineering maintenance in the Asian Water Tower, demonstrating a highly effective paradigm for monitoring permafrost hazards under climate warming. Full article

(This article belongs to the Special Issue Landslide Detection Using Machine and Deep Learning)

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30 pages, 16556 KB

Open AccessArticle

Assimilating FY4A AMV Winds with the Nudging–Forced–3DVar Method for Promoting the Numerical Nowcasting of “7.20” Rainstorm over Zhengzhou

by Yakai Guo, Aifang Su, Changliang Shao, Guanjun Niu, Dongmei Xu and Yanna Gao

Remote Sens. 2026, 18(3), 379; https://doi.org/10.3390/rs18030379 - 23 Jan 2026

Abstract

Geostationary atmospheric motion vectors (e.g., FY4A AMVs) are routine mid-upper atmospheric observations used in numerical weather prediction (NWP) models, yet their complex spatiotemporal errors and assimilation limitations, i.e., high-temporal/coarse-spatial data and large-scale-adjustment/direct-assimilation scheme, leave unclear impacts of AMVs assimilation on nowcasting forecasts. To [...] Read more.

Geostationary atmospheric motion vectors (e.g., FY4A AMVs) are routine mid-upper atmospheric observations used in numerical weather prediction (NWP) models, yet their complex spatiotemporal errors and assimilation limitations, i.e., high-temporal/coarse-spatial data and large-scale-adjustment/direct-assimilation scheme, leave unclear impacts of AMVs assimilation on nowcasting forecasts. To this end, a Nudging-Forced–3DVar scheme (NFV) is designed within a multi-scale (i.e., 12, 4, and 1 km) regional NWP framework to exploit AMVs characteristics; ablation experiments for the Zhengzhou “7.20” rainstorm isolate Nudging and 3DVar impacts on assimilation and nowcasting. Results show the following: (1) large-scale Nudging and high-resolution 3DVar both improve mid-upper analyses, with the former ingesting more observations; (2) Nudging retains large-scale background updates but yields significant misses, whereas 3DVar intensifies rainfall extremes yet blurs fine structures; (3) NFV merges its strengths, modulating deep convection through upper-level systems and markedly improving rainfall spatiotemporal patterns. Therefore, NFV is recommended for the FY4A AMVs’ future numerical nowcasting, which provides useful guidance for the regional application of geostationary 3D winds. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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27 pages, 16408 KB

Open AccessArticle

A SNR-Based Adaptive Goldstein Filter for Ionospheric Faraday Rotation Estimation Using Spaceborne Full-Polarimetric SAR Data

by Zelin Wang, Xun Wang, Dong Li and Yunhua Zhang

Remote Sens. 2026, 18(2), 378; https://doi.org/10.3390/rs18020378 - 22 Jan 2026

Abstract

The spaceborne full-polarimetric (FP) synthetic aperture radar (SAR) is an advanced sensor for high-resolution Earth observation. However, FP data acquired by such a system are prone to distortions induced by ionospheric Faraday rotation (FR). From the perspective of exploiting these distortions, this enables [...] Read more.

The spaceborne full-polarimetric (FP) synthetic aperture radar (SAR) is an advanced sensor for high-resolution Earth observation. However, FP data acquired by such a system are prone to distortions induced by ionospheric Faraday rotation (FR). From the perspective of exploiting these distortions, this enables the estimation of the ionospheric FR angle (FRA), and consequently the total electron content, across most global regions (including the extensive ocean areas) using spaceborne FP SAR measurements. The accuracy of FRA estimation, however, is highly sensitive to noise interference. This study addresses denoising in FRA retrieval based on the Bickel–Bates estimator, with a specific focus on noise reduction methods built upon the adaptive Goldstein filter (AGF) that was originally designed for radar interferometric processing. For the first time, three signal-to-noise ratio (SNR)-based AGFs suitable for FRA estimation are investigated. A key feature of these filters is that their SNRs are all defined using the amplitude of the Bickel–Bates estimator signal rather than the FRA estimates themselves. Accordingly, these AGFs are applied to the estimator signal instead of the estimated FRAs. Two of the three AGFs are developed by adopting the mathematical forms of SNRs and filter parameters consistent with the existing SNR-based AGFs for interferogram. The third AGF is newly proposed by utilizing more general mathematical forms of SNR and filter parameter that differ from the first two. Specifically, its SNR definition aligns with that widely used in image processing, and its filter parameter is derived as a function of the defined SNR plus an additionally introduced adjustable factor. The three SNR-based AGFs tailored for FRA estimation are tested and evaluated against existing AGF variants and classical image denoising methods using three sets of FP SAR Datasets acquired by the L-band ALOS PALSAR sensor, encompassing an ocean-only scene, a plain land–ocean combined scene, and a more complex land–ocean combined scene. Experimental results demonstrate that all three filters can effectively mitigate noise, with the newly proposed AGF achieving the best performance among all denoising methods included in the comparison. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Techniques for Ionospheric and Thermospheric Observations)

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33 pages, 29670 KB

Open AccessArticle

Slip-Surface Depth Inversion and Influencing Factor Analysis Based on the Integration of InSAR and GeoDetector: A Case Study of Typical Creep Landslide Groups in Li County

by Yue Shen, Xianmin Wang, Xiaoyu Yi, Li Cao and Haixiang Guo

Remote Sens. 2026, 18(2), 377; https://doi.org/10.3390/rs18020377 - 22 Jan 2026

Abstract

Creeping landslides constitute the predominant form of long-term, slow-moving geohazards in high mountain gorge regions. Under the combined influence of gravity and external triggering factors, these landslides undergo persistent deformation, posing continuous threats to major transportation corridors, hydropower infrastructures, and nearby settlements. Li [...] Read more.

Creeping landslides constitute the predominant form of long-term, slow-moving geohazards in high mountain gorge regions. Under the combined influence of gravity and external triggering factors, these landslides undergo persistent deformation, posing continuous threats to major transportation corridors, hydropower infrastructures, and nearby settlements. Li County is located within the active tectonic belt along the eastern margin of the Tibetan Plateau, characterized by highly variable topography, intensely fractured rock masses, and dense development of creeping landslides. The slip surfaces are typically deeply buried and concealed. Consequently, conventional drilling and profile-based investigations, limited by high costs, sparse sampling points, and poor spatial continuity, are insufficient for identifying the deep-seated structures of such landslides. To address this challenge, this study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to obtain ascending and descending deformation rate fields for 2022–2024, revealing pronounced spatial heterogeneity and persistent activity across three types of landslides. Based on the principle of mass conservation, the sliding-surface depths of eight typical landslides were inverted, revealing pronounced heterogeneity. The maximum sliding-surface depths range from 32 to 98 m and show strong agreement with borehole and profile data (R² > 0.92; RMSE ±4.96–±16.56 m), confirming the reliability of the inversion method. The GeoDetector model was used to quantitatively evaluate the dominant factors controlling landslide depth. Elevation was identified as the primary control factor, while slope aspect exhibited significant influence in several landslides. All factor combinations showed either “bi-factor enhancement” or “nonlinear enhancement”, indicating that slip-surface depth is governed by synergistic interactions among multiple factors. Boxplot-based statistical analyses further revealed three typical patterns of slip-surface variation with elevation and slope, based on which the landslides were classified into rotational, push-type translational, and traction-type translational categories. By integrating statistical patterns with mechanical models, the study achieves a transition from “form” to “state”, enabling inference of the internal mechanical conditions and evolutionary stages from the observed surface morphology. The results of this study provide an effective technical approach for deep structural detection, identification of controlling factors, and stability evaluation of creeping landslides in high mountain gorge environments. Full article

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26 pages, 11143 KB

Open AccessArticle

MISA-Net: Multi-Scale Interaction and Supervised Attention Network for Remote-Sensing Image Change Detection

by Haoyu Yin, Junzhe Wang, Shengyan Liu, Yuqi Wang, Yi Liu, Tengyue Guo and Min Xia

Remote Sens. 2026, 18(2), 376; https://doi.org/10.3390/rs18020376 - 22 Jan 2026

Abstract

Change detection in remote sensing imagery plays a vital role in land use analysis, disaster assessment, and ecological monitoring. However, existing remote sensing change detection methods often lack a structured and tightly coupled interaction paradigm to jointly reconcile multi-scale representation, bi-temporal discrimination, and [...] Read more.

Change detection in remote sensing imagery plays a vital role in land use analysis, disaster assessment, and ecological monitoring. However, existing remote sensing change detection methods often lack a structured and tightly coupled interaction paradigm to jointly reconcile multi-scale representation, bi-temporal discrimination, and fine-grained boundary modeling under practical computational constraints. To address this fundamental challenge, we propose a Multi-scale Interaction and Supervised Attention Network (MISANet). To improve the model’s ability to perceive changes at multiple scales, we design a Progressive Multi-Scale Feature Fusion Module (PMFFM), which employs a progressive fusion strategy to effectively integrate multi-granular cross-scale features. To enhance the interaction between bi-temporal features, we introduce a Difference-guided Gated Attention Interaction (DGAI) module. This component leverages difference information between the two time phases and employs a gating mechanism to retain fine-grained details, thereby improving semantic consistency. Furthermore, to guide the model’s focus on change regions, we design a Supervised Attention Decoder Module (SADM). This module utilizes a channel–spatial joint attention mechanism to reweight the feature maps. In addition, a deep supervision strategy is incorporated to direct the model’s attention toward both fine-grained texture differences and high-level semantic changes during training. Experiments conducted on the LEVIR-CD, SYSU-CD, and GZ-CD datasets demonstrate the effectiveness of our method, achieving F1-scores of 91.19%, 82.25%, and 88.35%, respectively. Compared with the state-of-the-art BASNet model, MISANet achieves performance gains of 0.50% F1 and 0.85% IoU on LEVIR-CD, 2.13% F1 and 3.02% IoU on SYSU-CD, and 1.28% F1 and 2.03% IoU on GZ-CD. The proposed method demonstrates strong generalization capabilities and is applicable to various complex change detection scenarios. Full article

(This article belongs to the Special Issue Bridging AI and Remote Sensing: Multimodal Learning for Advanced Semantic Understanding)

22 pages, 11122 KB

Open AccessFeature PaperArticle

Compilation of a Nationwide River Image Dataset for Identifying River Channels and River Rapids via Deep Learning

by Nicholas Brimhall, Kelvyn K. Bladen, Thomas Kerby, Carl J. Legleiter, Cameron Swapp, Hannah Fluckiger, Julie Bahr, Makenna Roberts, Kaden Hart, Christina L. Stegman, Brennan L. Bean and Kevin R. Moon

Remote Sens. 2026, 18(2), 375; https://doi.org/10.3390/rs18020375 - 22 Jan 2026

Abstract

Remote sensing enables large-scale, image-based assessments of river dynamics, offering new opportunities for hydrological monitoring. We present a publicly available dataset consisting of 281,024 satellite and aerial images of U.S. rivers, constructed using an Application Programming Interface (API) and the U.S. Geological Survey’s [...] Read more.

Remote sensing enables large-scale, image-based assessments of river dynamics, offering new opportunities for hydrological monitoring. We present a publicly available dataset consisting of 281,024 satellite and aerial images of U.S. rivers, constructed using an Application Programming Interface (API) and the U.S. Geological Survey’s National Hydrography Dataset. The dataset includes images, primary keys, and ancillary geospatial information. We use a manually labeled subset of the images to train models for detecting rapids, defined as areas where high velocity and turbulence lead to a wavy, rough, or even broken water surface visible in the imagery. To demonstrate the utility of this dataset, we develop an image segmentation model to identify rivers within images. This model achieved a mean test intersection-over-union (

I o U

) of 0.57, with performance rising to an actual

I o U

of 0.89 on the subset of predictions with high confidence (predicted

I o U

> 0.9). Following this initial segmentation of river channels within the images, we trained several convolutional neural network (CNN) architectures to classify the presence or absence of rapids. Our selected model reached an accuracy and F1 score of 0.93, indicating strong performance for the classification of rapids that could support consistent, efficient inventory and monitoring of rapids. These data provide new resources for recreation planning, habitat assessment, and discharge estimation. Overall, the dataset and tools offer a foundation for scalable, automated identification of geomorphic features to support riverine science and resource management. Full article

(This article belongs to the Section Environmental Remote Sensing)

25 pages, 9648 KB

Open AccessArticle

Lake Evolution and Emerging Hazards on the Tibetan Plateau from 2014 to 2023

by Haochen Wang, Peng He, Zhaocheng Guo, Genhou Wang, Jienan Tu and Shangyuan Yu

Remote Sens. 2026, 18(2), 374; https://doi.org/10.3390/rs18020374 - 22 Jan 2026

Abstract

Climate-induced lake expansion on the Tibetan Plateau (TP) has led to two distinct hazard types: outburst floods and passive inundation. However, the divergent driving mechanisms behind these hazards remain insufficiently understood. This study analyzes the spatiotemporal trends of 1352 non-glacial lakes (>1 km [...] Read more.

Climate-induced lake expansion on the Tibetan Plateau (TP) has led to two distinct hazard types: outburst floods and passive inundation. However, the divergent driving mechanisms behind these hazards remain insufficiently understood. This study analyzes the spatiotemporal trends of 1352 non-glacial lakes (>1 km²) on the TP from 2014 to 2023 using high-resolution Gaofen-1 (GF-1) and Gaofen-2 (GF-2) imagery. By integrating geomorphic analysis with hazard mechanisms, we screened and categorized lakes prone to outburst floods and inundation using a classification and assessment framework proposed in this study. The results indicate that the net area of these lakes expanded by 2839.53 km² (6.07%), with the Inner TP Basin contributing the largest absolute area gain (1960.60 km²). We identified 21 potentially hazardous lakes (10 outburst-prone and 11 inundation-prone) and systematically categorized them by risk level. Field investigations of high-risk candidates, such as Rulei Co and Xiao Qaidam Lake, validated the accuracy of the hazard classification and risk assessment methodology. Preliminary attribution analysis further suggests that the two hazard types may be associated with distinct climatic factors. Overall, this study provides a scientific basis for disaster mitigation and lake management on the TP. Full article

19 pages, 7224 KB

Open AccessArticle

Reduced-Dynamic Orbit Determination of Low-Orbit Satellites Taking into Account GNSS Attitude Errors

by Liang Liu, Yuhao Liu, Yibiao Chen and Chuang Qian

Remote Sens. 2026, 18(2), 373; https://doi.org/10.3390/rs18020373 - 22 Jan 2026

Abstract

Satellite attitude is critical for both satellite antenna phase center offset and phase wind-up correction. However, during the eclipse season, the nominal satellite attitude is almost impossible to maintain, and the satellite attitude variability affects the geometric distance correction of GNSS-LEO satellites, which [...] Read more.

Satellite attitude is critical for both satellite antenna phase center offset and phase wind-up correction. However, during the eclipse season, the nominal satellite attitude is almost impossible to maintain, and the satellite attitude variability affects the geometric distance correction of GNSS-LEO satellites, which ultimately affects the orbital accuracy of LEO satellites. To explore the impact of neglecting eclipsing attitude models on LEO satellite orbit determination, this study utilizes the attitude quaternion products provided by CNES to analyze the discrepancies between nominal attitude yaw angles and attitude quaternion-derived yaw angles. It also examines the variations in phase center offset and phase wind-up corrections, caused by neglecting eclipsing attitude models. The model is validated through orbit determination tests using onboard GRACE-FO data from days 90 to 109 of 2023. Based on these analyses, a simplified reduced-dynamic orbit determination model for LEO satellites using attitude quaternion is proposed. It is found that the phase residuals of GRACE-C and GRACE-D under the attitude quaternion strategy are reduced by 3.6% and 3.9%, respectively, and the orbital accuracies of GRACE-C and GRACE-D are improved by 7.3% and 4.5%, respectively, compared with the nominal attitude. Full article

(This article belongs to the Special Issue High-Precision Urban Positioning: GNSS and Multi-Sensor Fusion Technologies)

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