Previous Issue
Volume 17, April-2
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Volume 17
Issue 9
remotesensing-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sens., Volume 17, Issue 9 (May-1 2025) – 103 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
18 pages, 5114 KiB  
Article
Mapping Rice Phenology Using MODIS Products in An Giang Province, Mekong River Delta, Vietnam
by Shou-Hao Chiang and Minh-Binh Ton
Remote Sens. 2025, 17(9), 1583; https://doi.org/10.3390/rs17091583 (registering DOI) - 29 Apr 2025
Abstract
The Moderate Resolution Imaging Spectroradiometer (MODIS) provides consistent long-term satellite observations that are valuable for rice mapping and production estimation through phenology extraction. This study evaluates the effectiveness of three MODIS products, MOD09GQ (1-day), MOD09Q1 (8-day), and MOD13Q1 (16-day), for mapping rice phenology [...] Read more.
The Moderate Resolution Imaging Spectroradiometer (MODIS) provides consistent long-term satellite observations that are valuable for rice mapping and production estimation through phenology extraction. This study evaluates the effectiveness of three MODIS products, MOD09GQ (1-day), MOD09Q1 (8-day), and MOD13Q1 (16-day), for mapping rice phenology in An Giang Province, a key rice-producing region in Vietnam’s climate-sensitive Mekong River Delta (MRD). The analysis focuses on rice cropping seasons from 2019 to 2021, using time series of the Normalized Difference Vegetation Index (NDVI) to capture temporal and spatial variations in rice growth dynamics. To address data gaps due to persistent cloud cover and sensor-related noises, smoothing techniques, including the Double Logistic Function (DLF) and Savitzky–Golay Filtering (SGF), were applied. Thirteen phenological parameters were extracted and used as inputs to an unsupervised K-Means clustering algorithm, enabling the classification of distinct rice growth patterns. The results show that DLF-processed MOD09GQ data most accurately reconstructed NDVI time series and captured short-term phenological transitions, outperforming coarser-resolution products. The resulting phenology maps could be used to correlate the influence of anthropogenic factors, such as the widespread adoption of short-duration rice varieties and shifts in water management practices. This study provides a robust framework for phenology-based rice mapping to support food security, sustainable agricultural planning, and climate resilience in the MRD. Full article
►▼ Show Figures

Figure 1

27 pages, 21915 KiB  
Article
Evolutionary History of the Large-Scale Scarp in Jules Verne Crater, Moon
by Congzhe Wu, Jianzhong Liu, Gregory Michael, Harald Hiesinger, Carolyn H. van der Bogert, Wajiha Iqbal, Kai Zhu and Jingwen Liu
Remote Sens. 2025, 17(9), 1582; https://doi.org/10.3390/rs17091582 - 29 Apr 2025
Abstract
We conducted a detailed study using multi-source data to date the mare activity and lobate scarp formation within the Jules Verne crater on the Moon. In previous studies, the Jules Verne crater has been classified as a pre-Nectarian impact crater. Our analysis indicates [...] Read more.
We conducted a detailed study using multi-source data to date the mare activity and lobate scarp formation within the Jules Verne crater on the Moon. In previous studies, the Jules Verne crater has been classified as a pre-Nectarian impact crater. Our analysis indicates that it has an absolute model age (AMA) of 4.21+0.032 −0.034Ga. After its formation, a magmatic intrusion event created floor fractures, followed by two basaltic eruption events—one at 3.4 Ga and another at 2.6 Ga. Subsequently, around 1.4 billion years ago, lunar seismic activity likely took place in this region, resetting the surface ages of the crater floor fractures and surrounding areas, as evidenced by the scarp. Full article
(This article belongs to the Special Issue Planetary Remote Sensing and Applications to Mars and Chang’E-6/7)
20 pages, 23461 KiB  
Article
Direct and Indirect Effects of Large-Scale Forest Restoration on Water Yield in China’s Large River Basins
by Yaoqi Zhang and Lu Hao
Remote Sens. 2025, 17(9), 1581; https://doi.org/10.3390/rs17091581 - 29 Apr 2025
Abstract
Emerging evidence indicates that large-scale forest restoration exhibits dual hydrological effects: direct reduction of local water availability through elevated evapotranspiration (ET) and indirect augmentation of water resources via enhanced atmospheric moisture recycling. However, the quantitative assessment of these counteracting effects remains challenging due [...] Read more.
Emerging evidence indicates that large-scale forest restoration exhibits dual hydrological effects: direct reduction of local water availability through elevated evapotranspiration (ET) and indirect augmentation of water resources via enhanced atmospheric moisture recycling. However, the quantitative assessment of these counteracting effects remains challenging due to the limited observational constraints on moisture transport. Here, we integrate the Budyko model with the Lagrangian-based UTrack moisture-tracking dataset to disentangle the direct (via ET) and indirect (via precipitation) large-scale hydrological impacts of China’s four-decade forest restoration campaign across eight major river basins. Multisource validation datasets, including gauged runoff records, hydrological reanalysis products, and satellite-derived forest cover maps, were systematically incorporated to verify the Budyko model at the nested spatial scales. Our scenario analyses reveal that during 1980–2015, extensive afforestation individually reduced China’s terrestrial water yield by −28 ± 25 mm yr−1 through dominant ET increases. Crucially, atmospheric moisture recycling mechanisms attenuated this water loss by 12 ± 5 mm yr−1 nationally, with marked spatial heterogeneity across the basins. In some moisture-limited watersheds in the Yellow River Basin, the negative ET effect was compensated for to a certain extent by precipitation recycling, demonstrating net positive hydrological outcomes. We conclude that China’s forest expansion imposes local water stress (direct effect) by elevating ET, while the concomitant strengthening of continental-scale moisture recycling generates compensatory water gains (indirect effect). These findings advance the mechanistic understanding of the vegetation-climate-water nexus, providing quantitative references for optimizing forestation strategies under atmospheric water connectivity constraints. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Hydrology and Water Resources Management)
►▼ Show Figures

Figure 1

22 pages, 6898 KiB  
Article
Long-Term Monitoring of Landslide Activity in a Debris Flow Gully Using SBAS-InSAR: A Case Study of Shawan Gully, China
by Jianming Zhang, Xiaoqing Zuo, Daming Zhu, Yongfa Li and Xu Liu
Remote Sens. 2025, 17(9), 1580; https://doi.org/10.3390/rs17091580 - 29 Apr 2025
Abstract
Shawan Gully historically experienced recurrent debris flow events, resulting in significant losses of life and property. The Nuole and Huajiaoshu landslides are two major high-elevation landslides in Shawan Gully, serving as primary sources of debris flow material. To monitor landslides movements, this study [...] Read more.
Shawan Gully historically experienced recurrent debris flow events, resulting in significant losses of life and property. The Nuole and Huajiaoshu landslides are two major high-elevation landslides in Shawan Gully, serving as primary sources of debris flow material. To monitor landslides movements, this study used interferometric synthetic aperture radar (InSAR) and Sentinel-1 SAR imagery acquired between 2014 and 2023 to analyze surface deformation in Shawan Gully. Prior to InSAR processing, we assessed the InSAR measurement suitability of the involved SAR images in detail based on geometric distortion and monitoring sensitivity. Compared to conventional SBAS-InSAR results without preprocessing, the suitability-refined datasets show improvements in interferometric phase quality (1.55 rad to 1.41 rad) and estimation accuracy (1.45 mm to 1.18 mm). By processing ascending, descending, and cross-track Sentinel-1 SAR images, we obtained multi-directional surface displacements in Shawan Gully. The results reveal significant deformation in the NL1 region of Nuole landslide, while the northern scarp and the foot of the slope exhibited different movement characteristics, indicating spatially variable deformation mechanisms. The study also revealed that the Nuole landslide exhibits a high sensitivity to rainfall-induced instability, with rainfall significantly changing its original movement trend. Full article
(This article belongs to the Special Issue Advanced InSAR Techniques for Geohazard Monitoring and Risk Evaluation)
►▼ Show Figures

Figure 1

36 pages, 23271 KiB  
Article
Comprehensive Evaluation of the Lunar South Pole Landing Sites Using Self-Organizing Maps for Scientific and Engineering Purposes
by Hengxi Liu, Yongzhi Wang, Shibo Wen, Sheng Zhang, Kai Zhu and Jianzhong Liu
Remote Sens. 2025, 17(9), 1579; https://doi.org/10.3390/rs17091579 - 29 Apr 2025
Abstract
The permanently shadowed regions of the lunar South Pole have become a key target for international lunar exploration due to their unique scientific value and engineering challenges. In order to effectively screen suitable landing zones near the lunar South Pole, this research proposes [...] Read more.
The permanently shadowed regions of the lunar South Pole have become a key target for international lunar exploration due to their unique scientific value and engineering challenges. In order to effectively screen suitable landing zones near the lunar South Pole, this research proposes a comprehensive evaluation method based on a self-organizing map (SOM). Using multi-source remote sensing data, the method classifies and analyzes candidate landing zones by combining scientific purposes (such as hydrogen abundance, iron oxide abundance, gravity anomalies, water ice distance analysis, and geological features) and engineering constraints (such as Sun visibility, Earth visibility, slope, and roughness). Through automatic clustering, the SOM model finds the important regions. Subsequently, it integrates with a supervised learning model, a random forest, to determine the feature importance weights in more detail. The results from the research indicate the following: the areas suitable for landing account for 9.05%, 5.95%, and 5.08% in the engineering, scientific, and synthesized perspectives, respectively. In the weighting analysis of the comprehensive data, the weights of Earth visibility, hydrogen abundance, kilometer-scale roughness, and slope data all account for more than 10%, and these are thought to be the four most important factors in the automated site selection process. Furthermore, the kilometer-scale roughness data are more important in the comprehensive weighting, which is in line with the finding that the kilometer-scale roughness data represent both surface roughness from an engineering perspective and bedrock geology from a scientific one. In this study, a local examination of typical impact craters is performed, and it is confirmed that all 10 possible landing sites suggested by earlier authors are within the appropriate landing range. The findings demonstrate that the SOM-model-based analysis approach can successfully assess lunar South Pole landing areas while taking multiple constraints into account, uncovering spatial distribution features of the region, and offering a rationale for choosing desired landing locations. Full article
(This article belongs to the Special Issue Planetary Geologic Mapping and Remote Sensing (Second Edition))
►▼ Show Figures

Figure 1

21 pages, 6525 KiB  
Article
Two-Stage Deep Learning Framework for Individual Tree Crown Detection and Delineation in Mixed-Wood Forests Using High-Resolution Light Detection and Ranging Data
by Qian Li, Baoxin Hu, Jiali Shang and Tarmo K. Remmel
Remote Sens. 2025, 17(9), 1578; https://doi.org/10.3390/rs17091578 - 29 Apr 2025
Abstract
Accurate detection and delineation of individual tree crowns (ITCs) are essential for sustainable forest management and ecosystem monitoring, providing key biophysical attributes at the individual tree level. However, the complex structure of mixed-wood forests, characterized by overlapping canopies of various shapes and sizes, [...] Read more.
Accurate detection and delineation of individual tree crowns (ITCs) are essential for sustainable forest management and ecosystem monitoring, providing key biophysical attributes at the individual tree level. However, the complex structure of mixed-wood forests, characterized by overlapping canopies of various shapes and sizes, presents significant challenges, often compromising accuracy. This study presents a two-stage deep learning framework that integrates Canopy Height Model (CHM)-based treetop detection with three-dimensional (3D) ITC delineation using high-resolution airborne LiDAR point cloud data. In the first stage, Mask R-CNN detects treetops from the CHM, providing precise initial localizations of individual trees. In the second stage, a 3D U-Net architecture clusters LiDAR points to delineate ITC boundaries in 3D space. Evaluated against manually delineated reference data, our approach outperforms established methods, including Mask R-CNN alone and the lidR itcSegment algorithm, achieving mean intersection-over-union (mIoU) scores of 0.82 for coniferous plots, 0.81 for mixed-wood plots, and 0.79 for deciduous plots. This study demonstrates the great potential of the two-stage deep learning approach as a robust solution for 3D ITC delineation in mixed-wood forests. Full article
(This article belongs to the Special Issue Lidar for Forest Parameters Retrieval)
►▼ Show Figures

Figure 1

33 pages, 38944 KiB  
Article
Vegetation Restoration Outpaces Climate Change in Driving Evapotranspiration in the Wuding River Basin
by Geyu Zhang, Zijun Wang, Hanyu Ren, Qiaotian Shen, Tingyi Xue, Zongsen Wang, Xu Chen, Haijing Shi, Peidong Han, Yangyang Liu and Zhongming Wen
Remote Sens. 2025, 17(9), 1577; https://doi.org/10.3390/rs17091577 - 29 Apr 2025
Abstract
For the management of the water cycle, it is essential to comprehend evapotranspiration (ET) and how it changes over time and space, especially in relation to vegetation. Here, using the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, we explored the spatiotemporal variations in ET [...] Read more.
For the management of the water cycle, it is essential to comprehend evapotranspiration (ET) and how it changes over time and space, especially in relation to vegetation. Here, using the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, we explored the spatiotemporal variations in ET across different time scales during 1982–2018 in the Wuding River Basin. We also quantitatively evaluated the driving mechanisms of climate and vegetation changes on ET changes. Results showed that the ET estimate by the PT-JPL model showed good agreement (R2 = 0.71–0.84) with four ET products (PML, MOD16A2, GLASS, FLDAS). Overall, the ET increased significantly at a rate of 3.11 mm/year (p < 0.01). Spatially, ET in the WRB is higher in the southeast and lower in the northwest. Attribution analysis indicated that vegetation restoration (leaf area index) was the dominant driver of ET changes (99.93% basin area, p < 0.05), exhibiting both direct effects and indirect mediation through the Vapor Pressure Deficit. Temperature influences emerged predominantly through vegetation feedbacks rather than direct climatic forcing. These findings establish vegetation restoration as a key driver of regional ET, providing empirical support for optimizing revegetation strategies in semi-arid environments. Full article
(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation (Second Edition))
►▼ Show Figures

Figure 1

28 pages, 42589 KiB  
Article
A Subimage Autofocus Bistatic Ground Cartesian Back-Projection Algorithm for Passive Bistatic SAR Based on GEO Satellites
by Te Zhao, Jun Wang, Zuhan Cheng, Ziqian Huang and Xueming Song
Remote Sens. 2025, 17(9), 1576; https://doi.org/10.3390/rs17091576 - 29 Apr 2025
Abstract
As an evolutionary advancement to conventional synthetic aperture radar (SAR), passive bistatic SAR (PBSAR) utilizing geostationary orbit (GEO) satellite signals demonstrates significant potential for high-resolution imaging. However, PBSAR faces dual challenges in computational efficiency and phase error compensation. Traditional accelerated back-projection (BP) variants [...] Read more.
As an evolutionary advancement to conventional synthetic aperture radar (SAR), passive bistatic SAR (PBSAR) utilizing geostationary orbit (GEO) satellite signals demonstrates significant potential for high-resolution imaging. However, PBSAR faces dual challenges in computational efficiency and phase error compensation. Traditional accelerated back-projection (BP) variants developed from monostatic SAR are incompatible with PBSAR’s geometry, and autofocus BP (AFBP) methods exhibit prohibitive computational costs and inadequate space-variant phase error handling. This study first develops a bistatic ground Cartesian back-projection (BGCBP) algorithm through subimage wavenumber spectrum correction, specifically adapted to GEO-satellite-based PBSAR. Compared to conventional BP, the BGCBP achieves an order-of-magnitude complexity reduction without resolution degradation. Building upon this foundation, we propose a subimage autofocus BGCBP (SIAF-BGCBP) methodology, synergistically integrating autofocus processing with BGCBP’s accelerated framework. SIAF-BGCBP reduces phase estimation’s complexity by 90% through subimage pixel density optimization while maintaining estimation accuracy. Further enhancement of SIAF-BGCBP via geometric inversion would enable the precise compensation of space-variant phase errors while remaining efficient. Simulations and real-environment experiments verify the effectiveness of the proposed methods. Full article
►▼ Show Figures

Figure 1

22 pages, 9005 KiB  
Article
A Target Near-Field Scattering Measurement Technique Utilizing 3D Near-Field Imaging via Cylindrical Scanning
by Zongkai Yang, Jingcheng Zhao, Weikang Si, Changyu Lou, Xin Zhao and Jungang Miao
Remote Sens. 2025, 17(9), 1575; https://doi.org/10.3390/rs17091575 - 29 Apr 2025
Abstract
Radar target near-field scattering characteristics are essential for the identification of target properties and the improvement of target recognition. Nevertheless, the efficiency of current precision three-dimensional (3D) imaging algorithms in near-field scattering measurements is restricted by their substantial computational complexity. To resolve this [...] Read more.
Radar target near-field scattering characteristics are essential for the identification of target properties and the improvement of target recognition. Nevertheless, the efficiency of current precision three-dimensional (3D) imaging algorithms in near-field scattering measurements is restricted by their substantial computational complexity. To resolve this matter, we propose a hybrid 3D imaging algorithm that is optimized for cylindrical sampling and operates in both the wavenumber domain and time domain (WDTD). Wavenumber domain algorithms are initially utilized for the rapid localization of strong scattering sources. Subsequently, morphological image analysis techniques are employed to delineate the regions containing strong scattering sources. Ultimately, accurate calculations are performed utilizing backpropagation (BP) in time domain algorithms. This method significantly reduces the computational burden while maintaining imaging accuracy by integrating rapid scattering source extraction with precise computation for critical regions. The proposed capacity to accomplish efficient and precise 3D imaging is effectively demonstrated by the experimental results, which effectively mitigate the computational challenges associated with traditional algorithms. Furthermore, the method effectively reconstructs near-field echoes of scattering sources, underscoring its potential for decoupling target–background interactions. The versatility of this method is further demonstrated by its ability to be applied to other 3D imaging configurations, which illustrates its potential to advance radar imaging technologies and near-field scattering research. Full article
(This article belongs to the Special Issue SAR-Based Signal Processing and Target Recognition (Second Edition))
►▼ Show Figures

Figure 1

37 pages, 59030 KiB  
Review
Integration of Hyperspectral Imaging and AI Techniques for Crop Type Mapping: Present Status, Trends, and Challenges
by Mohamed Bourriz, Hicham Hajji, Ahmed Laamrani, Nadir Elbouanani, Hamd Ait Abdelali, François Bourzeix, Ali El-Battay, Abdelhakim Amazirh and Abdelghani Chehbouni
Remote Sens. 2025, 17(9), 1574; https://doi.org/10.3390/rs17091574 - 29 Apr 2025
Abstract
Accurate and efficient crop maps are essential for decision-makers to improve agricultural monitoring and management, thereby ensuring food security. The integration of advanced artificial intelligence (AI) models with hyperspectral remote sensing data, which provide richer spectral information than multispectral imaging, has proven highly [...] Read more.
Accurate and efficient crop maps are essential for decision-makers to improve agricultural monitoring and management, thereby ensuring food security. The integration of advanced artificial intelligence (AI) models with hyperspectral remote sensing data, which provide richer spectral information than multispectral imaging, has proven highly effective in the precise discrimination of crop types. This systematic review examines the evolution of hyperspectral platforms, from Unmanned Aerial Vehicle (UAV)-mounted sensors to space-borne satellites (e.g., EnMAP, PRISMA), and explores recent scientific advances in AI methodologies for crop mapping. A review protocol was applied to identify 47 studies from databases of peer-reviewed scientific publications, focusing on hyperspectral sensors, input features, and classification architectures. The analysis highlights the significant contributions of Deep Learning (DL) models, particularly Vision Transformers (ViTs) and hybrid architectures, in improving classification accuracy. However, the review also identifies critical gaps, including the under-utilization of hyperspectral space-borne imaging, the limited integration of multi-sensor data, and the need for advanced modeling approaches such as Graph Neural Networks (GNNs)-based methods and geospatial foundation models (GFMs) for large-scale crop type mapping. Furthermore, the findings highlight the importance of developing scalable, interpretable, and transparent models to maximize the potential of hyperspectral imaging (HSI), particularly in underrepresented regions such as Africa, where research remains limited. This review provides valuable insights to guide future researchers in adopting HSI and advanced AI models for reliable large-scale crop mapping, contributing to sustainable agriculture and global food security. Full article
(This article belongs to the Special Issue Recent Development of Practical AI in Remote Sensing and Geoinformatics)
►▼ Show Figures

Figure 1

22 pages, 16339 KiB  
Article
MFSM-Net: Multimodal Feature Fusion for the Semantic Segmentation of Urban-Scale Textured 3D Meshes
by Xinjie Hao, Jiahui Wang, Wei Leng, Rongting Zhang and Guangyun Zhang
Remote Sens. 2025, 17(9), 1573; https://doi.org/10.3390/rs17091573 - 28 Apr 2025
Abstract
The semantic segmentation of textured 3D meshes is a critical step in constructing city-scale realistic 3D models. Compared to colored point clouds, textured 3D meshes have the advantage of high-resolution texture image patches embedded on each mesh face. However, existing studies predominantly focus [...] Read more.
The semantic segmentation of textured 3D meshes is a critical step in constructing city-scale realistic 3D models. Compared to colored point clouds, textured 3D meshes have the advantage of high-resolution texture image patches embedded on each mesh face. However, existing studies predominantly focus on their geometric structures, with limited utilization of these high-resolution textures. Inspired by the binocular perception of humans, this paper proposes a multimodal feature fusion network based on 3D geometric structures and 2D high-resolution texture images for the semantic segmentation of textured 3D meshes. Methodologically, the 3D feature extraction branch computes the centroid coordinates and face normals of mesh faces as initial 3D features, followed by a multi-scale Transformer network to extract high-level 3D features. The 2D feature extraction branch employs orthographic views of city scenes captured from a top-down perspective and uses a U-Net to extract high-level 2D features. To align features across 2D and 3D modalities, a Bridge view-based alignment algorithm is proposed, which visualizes the 3D mesh indices to establish pixel-level associations with orthographic views, achieving the precise alignment of multimodal features. Experimental results demonstrate that the proposed method achieves competitive performance in city-scale textured 3D mesh semantic segmentation, validating the effectiveness and potential of the cross-modal fusion strategy. Full article
(This article belongs to the Special Issue Urban Planning Supported by Remote Sensing Technology II)
26 pages, 9869 KiB  
Article
CAGFNet: A Cross-Attention Image-Guided Fusion Network for Disparity Estimation of High-Resolution Satellite Stereo Images
by Qian Zhang, Jia Ge, Shufang Tian and Laidian Xi
Remote Sens. 2025, 17(9), 1572; https://doi.org/10.3390/rs17091572 - 28 Apr 2025
Viewed by 25
Abstract
Disparity estimation in high-resolution satellite stereo images is a critical task in remote sensing and photogrammetry. However, significant challenges arise due to the complexity of satellite stereo image scenes and the dynamic variations in disparities. Stereo matching becomes particularly difficult in areas with [...] Read more.
Disparity estimation in high-resolution satellite stereo images is a critical task in remote sensing and photogrammetry. However, significant challenges arise due to the complexity of satellite stereo image scenes and the dynamic variations in disparities. Stereo matching becomes particularly difficult in areas with textureless regions, repetitive patterns, disparity discontinuities, and occlusions. Recent advancements in deep learning have opened new research avenues for disparity estimation. This paper presents a novel end-to-end disparity estimation network designed to address these challenges through three key innovations: (1) a cross-attention mechanism for robust feature extraction, (2) an image-guided module that preserves geometric details, and (3) a 3D feature fusion module for context-aware disparity refinement. Experiments on the US3D dataset demonstrate State-of-the-Art performance, achieving an endpoint error (EPE) of 1.466 pixels (14.71% D1-error) on the Jacksonville subset and 0.996 pixels (10.53% D1-error) on the Omaha subset. The experimental results confirm that the proposed network excels in disparity estimation, exhibiting strong learning capability and robust generalization performance. Full article
►▼ Show Figures

Figure 1

20 pages, 5305 KiB  
Technical Note
A Study on an Anti-Multiple Periodic Frequency Modulation (PFM) Interference Algorithm in Single-Antenna Low-Earth-Orbit Signal-of-Opportunity Positioning Systems
by Lihao Yao, Honglei Qin, Hao Xu, Deyong Xian, Donghan He, Boyun Gu, Hai Sha, Yunchao Zou, Huichao Zhou, Nan Xu, Jiemin Shen, Zhijun Liu, Feiqiang Chen, Chunjiang Ma and Xiaoli Fang
Remote Sens. 2025, 17(9), 1571; https://doi.org/10.3390/rs17091571 - 28 Apr 2025
Viewed by 15
Abstract
Signal-of-Opportunity (SOP) positioning based on Low-Earth-Orbit (LEO) constellations has gradually become a research hotspot. Due to their large quantity, wide spectral coverage, and strong signal power, LEO satellite SOP positioning exhibits robust anti-jamming capabilities. However, no in-depth studies have been conducted on their [...] Read more.
Signal-of-Opportunity (SOP) positioning based on Low-Earth-Orbit (LEO) constellations has gradually become a research hotspot. Due to their large quantity, wide spectral coverage, and strong signal power, LEO satellite SOP positioning exhibits robust anti-jamming capabilities. However, no in-depth studies have been conducted on their anti-jamming performance, particularly regarding the most common type of interference faced by ground receivers—Periodic Frequency Modulation (PFM) interference. Due to the significant differences in signal characteristics between LEO satellite downlink signals and those of Global Navigation Satellite Systems (GNSSs) based on Medium-Earth-Orbit (MEO) or Geostationary-Earth-Orbit (GEO) satellites, traditional interference suppression techniques cannot be directly applied. This paper proposes a Signal Adaptive Iterative Optimization Resampling (SAIOR) algorithm, which leverages the periodicity of PFM jamming signals and the characteristics of LEO constellation signals. The algorithm enhances the concentration of jamming energy by appropriately resampling the data, thereby reducing the overlap between LEO satellite signals and interference. This approach effectively minimizes the damage to the desired signal during anti-jamming processing. Simulation and experimental results demonstrate that, compared to traditional algorithms, this method can effectively eliminates single/multiple-component PFM interference, improve the interference suppression performance under the conditions of narrow bandwidth and high signal power, and holds a high application value in LEO satellite SOP positioning. Full article
(This article belongs to the Special Issue Low Earth Orbit Enhanced GNSS: Opportunities and Challenges)
►▼ Show Figures

Figure 1

21 pages, 7584 KiB  
Article
WTDBNet: A Wavelet Transform-Based Dual-Stream Backbone Network for Fine-Grained Ship Detection
by Wei Cao, Xinyu Zhao, Hongqi Wang and Yuxin Hu
Remote Sens. 2025, 17(9), 1570; https://doi.org/10.3390/rs17091570 - 28 Apr 2025
Viewed by 40
Abstract
Fine-grained ship detection tasks require models to accurately classify fine-grained categories and precisely localize them within complex backgrounds, relying on detailed features. The challenges of this task mainly lie in bird’s-eye viewpoints, scale variations, rotational changes, and environmental factors, which lead to minor [...] Read more.
Fine-grained ship detection tasks require models to accurately classify fine-grained categories and precisely localize them within complex backgrounds, relying on detailed features. The challenges of this task mainly lie in bird’s-eye viewpoints, scale variations, rotational changes, and environmental factors, which lead to minor inter-class differences and significant intra-class variations. This paper presents a novel model, called Wavelet Transform-based Dual-Stream Backbone Network (WTDBNet), which effectively integrates three key strengths: the ability of the Transformer to model long-range dependencies for global context, the capability of convolutional neural networks to extract detailed local features, and the efficiency of wavelet transform in frequency-domain decomposition for enhancing edges and texture details. These components are fused via channel and spatial attention mechanisms, thereby improving the model’s ability to extract discriminative features. The effectiveness of WTDBNet is validated on two widely used benchmarks for fine-grained oriented ship detection, as well as on a self-constructed dataset designed to represent complex scenarios. Experimental results demonstrate the superior performance of the proposed method. Full article
(This article belongs to the Section Remote Sensing Image Processing)
►▼ Show Figures

Figure 1

23 pages, 4371 KiB  
Article
Soil Moisture Inversion Using Multi-Sensor Remote Sensing Data Based on Feature Selection Method and Adaptive Stacking Algorithm
by Liguo Wang and Ya Gao
Remote Sens. 2025, 17(9), 1569; https://doi.org/10.3390/rs17091569 - 28 Apr 2025
Viewed by 25
Abstract
Soil moisture (SM) profoundly influences crop growth, yield, soil temperature regulation, and ecological balance maintenance and plays a pivotal role in water resources management and regulation. The focal objective of this investigation is to identify feature parameters closely associated with soil moisture through [...] Read more.
Soil moisture (SM) profoundly influences crop growth, yield, soil temperature regulation, and ecological balance maintenance and plays a pivotal role in water resources management and regulation. The focal objective of this investigation is to identify feature parameters closely associated with soil moisture through the implementation of feature selection methods on multi-source remote sensing data. Specifically, three feature selection methods, namely SHApley Additive exPlanations (SHAP), information gain (Info-gain), and Info_gain ∩ SHAP were validated in this study. The multi-source remote sensing data collected from Sentinel-1, Landsat-8, and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTGTM DEM) enabled the derivation of 25 characteristic parameters through sound computational approaches. Subsequently, a stacking algorithm integrating multiple machine-learning (ML) algorithms based on adaptive learning was engineered to accomplish soil moisture prediction. The attained prediction outcomes were then juxtaposed against those of single models, including Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). Notably, the adoption of feature factors selected by the Info_gain algorithm in combination with the adaptive stacking (Ada-Stacking) algorithm yielded the most optimal soil moisture prediction results. Specifically, the Mean Absolute Error (MAE) was determined to be 1.86 Vol. %, the Root Mean Square Error (RMSE) amounted to 2.68 Vol. %, and the R-squared (R2) reached 0.95. The multifactor integrated model that harnessed optical remote sensing data, radar backscatter coefficients, and topographic data exhibited remarkable accuracy in soil surface moisture retrieval, thus providing valuable insights for soil moisture inversion studies in the designated study area. Furthermore, the Ada-Stacking algorithm demonstrated its potency in integrating multiple models, thereby elevating retrieval accuracy and overcoming the limitations inherent in a single ML model. Full article
►▼ Show Figures

Figure 1

19 pages, 5290 KiB  
Article
Real-Time Regional Ionospheric Total Electron Content Modeling Using the Extended Kalman Filter
by Jun Tang, Yuhan Gao, Heng Liu, Mingxian Hu, Chaoqian Xu and Liang Zhang
Remote Sens. 2025, 17(9), 1568; https://doi.org/10.3390/rs17091568 - 28 Apr 2025
Viewed by 46
Abstract
Real-time ionospheric products can accelerate the convergence of real-time precise point positioning (PPP) to improve the real-time positioning services of global navigation satellite systems (GNSSs), as well as to achieve continuous monitoring of the ionosphere. This study applied an extended Kalman filter (EKF) [...] Read more.
Real-time ionospheric products can accelerate the convergence of real-time precise point positioning (PPP) to improve the real-time positioning services of global navigation satellite systems (GNSSs), as well as to achieve continuous monitoring of the ionosphere. This study applied an extended Kalman filter (EKF) to total electron content (TEC) modeling, proposing a regional real-time EKF-based ionospheric model (REIM) with a spatial resolution of 1° × 1° and a temporal resolution of 1 h. We examined the performance of REIM through a 7-day period during geomagnetic storms. The post-processing model from the China Earthquake Administration (IOSR), CODG, IGSG, and the BDS geostationary orbit satellite (GEO) observations were utilized as reference. The consistency analysis showed that the mean deviation between REIM and IOSR was 0.97 TECU, with correlation coefficients of 0.936 and 0.938 relative to IOSR and IGSG, respectively. The VTEC mean deviation between REIM and BDS GEO observations was 4.15 TECU, which is lower than those of CODG (4.68 TECU), IGSG (5.67 TECU), and IOSR (6.27 TECU). In the real-time single-frequency PPP (RT-SF-PPP) experiments, REIM-augmented positioning converges within approximately 80 epochs, and IGSG requires 140 epochs. The REIM-augmented east-direction positioning error was 0.086 m, smaller than that of IGSG (0.095 m) and the Klobuchar model (0.098 m). REIM demonstrated high consistencies with post-processing models and showed a higher accuracy at IPPs of BDS GEO satellites. Moreover, the correction results of the REIM model are comparable to post-processing models in RT-SF-PPP while achieving faster convergence. Full article
(This article belongs to the Special Issue Exploring Innovative Ionospheric Applications Using Ground and Spaceborne Observations)
►▼ Show Figures

Figure 1

26 pages, 14574 KiB  
Article
The Spatiotemporal Dynamics and Evolutionary Relationship Between Urbanization and Eco-Environmental Quality: A Case Study in Hangzhou City, China
by Di’en Zhu, Huaqiang Du, Guomo Zhou, Mengchen Hu and Zihao Huang
Remote Sens. 2025, 17(9), 1567; https://doi.org/10.3390/rs17091567 - 28 Apr 2025
Viewed by 42
Abstract
The rapid expansion of urban spaces driven by accelerating urbanization has profoundly impacted the eco-environmental quality. However, the dynamic relationship between urbanization and eco-environmental quality remains insufficiently understood. This study quantifies urbanization intensity and eco-environmental quality using the impervious surface distribution density (ISDD) [...] Read more.
The rapid expansion of urban spaces driven by accelerating urbanization has profoundly impacted the eco-environmental quality. However, the dynamic relationship between urbanization and eco-environmental quality remains insufficiently understood. This study quantifies urbanization intensity and eco-environmental quality using the impervious surface distribution density (ISDD) and Remote Sensing-based Ecological Index (RSEI). By examining the spatiotemporal dynamics and evolutionary relationships of these indicators in Hangzhou from 1985 to 2020, we found that urban expansion drove ecological degradation in expansion areas, whereas ecological quality in the old city significantly improved. The ecological response to urbanization intensity exhibited spatial variation: in low-intensity urbanized expansion areas, ecological quality declined with increasing urbanization, whereas in the high-intensity urbanized old city, ecological quality improved. Additionally, the degree of coupling coordination between urbanization and ecological quality steadily increased over time, underscoring the importance of rational urban planning and ecological management in achieving sustainable development. This study provides a scientific foundation for urban ecological environment management and offers practical insights for fostering green development in rapidly urbanizing regions. Full article
(This article belongs to the Special Issue Remote Sensing and Geospatial Analysis in Urban Environments in the Big Data Era)
►▼ Show Figures

Figure 1

24 pages, 9553 KiB  
Article
A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
by Tengling Luo, Yi Yu, Gang Ma, Weimin Zhang, Luyao Qin, Weilai Shi, Qiudan Dai and Peng Zhang
Remote Sens. 2025, 17(9), 1566; https://doi.org/10.3390/rs17091566 - 28 Apr 2025
Viewed by 96
Abstract
Satellite microwave-sounding radiometer data assimilation under clear-sky conditions typically requires the exclusion of precipitation-affected field-of-view (FOV) regions. However, the traditional scatter index (SI) and cloud liquid water path (CLWP)-based precipitation sounding algorithms from earlier NOAA microwave sounders are built [...] Read more.
Satellite microwave-sounding radiometer data assimilation under clear-sky conditions typically requires the exclusion of precipitation-affected field-of-view (FOV) regions. However, the traditional scatter index (SI) and cloud liquid water path (CLWP)-based precipitation sounding algorithms from earlier NOAA microwave sounders are built on window channels which are not available from FY-3C/D MWTS-II. To address this limitation, this study establishes a nonlinear relationship between multispectral visible/infrared data from the FY-2F geostationary satellite and microwave sounding channels using an artificial intelligence (AI)-driven approach. The methodology involves three key steps: (1) The spatiotemporal integration of FY-2F VISSR-derived products with NOAA-19 AMSU-A microwave brightness temperatures was achieved through the GEO-LEO pixel fusion algorithm. (2) The fused observations were used as a training set and input into a random forest model. (3) The performance of the RF_SI method was evaluated by using individual cases and time series observations. Results demonstrate that the RF_SI method effectively captures the horizontal distribution of microwave scattering signals in deep convective systems. Compared with those of the NOAA-19 AMSU-A traditional SI and CLWP-based precipitation sounding algorithms, the accuracy and sounding rate of the RF_SI method exceed 94% and 92%, respectively, and the error rate is less than 3%. Also, the RF_SI method exhibits consistent performance across diverse temporal and spatial domains, highlighting its robustness for cross-platform precipitation screening in microwave data assimilation. Full article
(This article belongs to the Special Issue Advanced Machine Learning Models for Remote Sensing Applications and Data Analysis—Recent Developments)
►▼ Show Figures

Figure 1

23 pages, 4070 KiB  
Article
Automated Plasma Region Classification and Boundary Layer Identification Using Machine Learning
by Jiye Wang, Xuan Liu, Fanzhuo Dai, Rui Zheng, Yuanlin Han, Yang Wang, Andi Liu, Xinhua Wei, Lingqian Zhang, Hui Li, Chi Wang, Tieyan Wang, James L. Burch and Wolfgang Baumjohann
Remote Sens. 2025, 17(9), 1565; https://doi.org/10.3390/rs17091565 - 28 Apr 2025
Viewed by 26
Abstract
The accurate classification of plasma regions is a critical challenge in space science, with identifying dynamic boundary layers (BLs) being particularly complex. This study introduces a novel wavelet-decision tree classifier (WDTC) designed to automate BL detection. Unlike conventional machine learning methods that rely [...] Read more.
The accurate classification of plasma regions is a critical challenge in space science, with identifying dynamic boundary layers (BLs) being particularly complex. This study introduces a novel wavelet-decision tree classifier (WDTC) designed to automate BL detection. Unlike conventional machine learning methods that rely on raw satellite measurements, the WDTC utilizes processed parameters derived from wavelet analysis as inputs to the decision tree algorithm. For each in situ measurement, including magnetic field strength (B), plasma density (n), velocity (V), and temperature (T), the wavelet analysis generates two features: wavelet energy and wavelet entropy. This results in a total of eight input parameters (two for each of the four in situ measurements) for the decision tree. By incorporating these distinctive wavelet-derived features, the WDTC enhances its ability to accurately and efficiently identify BLs within complex plasma environments. The model was applied to data from the Magnetospheric Multiscale (MMS) mission, focusing on the dayside region, and successfully differentiated between the solar wind, bow shock, magnetosheath, magnetopause, and magnetosphere. From September 15 to December 31, 2015, the WDTC identified 711 BL crossings, including 295 bow shock events and 416 magnetopause crossings. Beyond its scientific applications, the WDTC provides high-quality training datasets and a reliable data labeling tool, contributing to neural network training efforts. Full article
(This article belongs to the Special Issue State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications (Second Edition))
►▼ Show Figures

Figure 1

19 pages, 4762 KiB  
Article
Parametric Representation of Tropical Cyclone Outer Radical Wind Profile Using Microwave Radiometer Data
by Yuan Gao, Weili Wang, Jian Sun and Yunhua Wang
Remote Sens. 2025, 17(9), 1564; https://doi.org/10.3390/rs17091564 - 28 Apr 2025
Viewed by 101
Abstract
The Soil Moisture Active Passive (SMAP) satellite can measure sea surface winds under tropical cyclone (TC) conditions with its L-band microwave radiometer, without being affected by rainfall or signal saturation. Through the statistical analysis of SMAP data, this study aims to develop radial [...] Read more.
The Soil Moisture Active Passive (SMAP) satellite can measure sea surface winds under tropical cyclone (TC) conditions with its L-band microwave radiometer, without being affected by rainfall or signal saturation. Through the statistical analysis of SMAP data, this study aims to develop radial wind profile models for the TC outer area whose distance from TC center is larger than the radius of maximum wind (Rm). A total of 196 TC cases observed by SMAP were collected between 2015 and 2020, and their intensities range from tropical storm to category 5. Based on the wind and radius data, the key model parameters α and β were fitted through the Rankine vortex model and the tangential wind profile (TWP) Gaussian model, respectively. α and β control the rate of change of the tangential wind speed with radius. Subsequently, for the parametric representation of α and β, we extracted some TC wind filed parameters, such as maximum wind speed (Um), Rm, the average wind speed at Rm (Uma), and the average radius of 17 m/s (R17) and examined the relationship between Uma and Um, the relationship between Rm and R17, the relationship between α, Um and Rm, and the relationship between β, Um and Rm. According to the results, the new radial wind profile models were proposed, i.e., SMAP Rankine Model-4 (SRM-4), SMAP Rankine Model-5 (SRM-5), and SMAP Gaussian Model-1 (SGM-1). A significant advantage of these models is that they can simulate average wind distribution through the conversion from Um to Uma. Finally, comparisons were made between the new models and existing SRM-1, SRM-2, and SRM-3, according to the Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements of 126 TC cases. The results demonstrate that the SRM-4 simulated the radial wind profile best overall, with the lowest root mean-square error (RMSE) of 5.57 m/s, due to replacing the parameter Um with Uma, using Rankine vortex for α parameterization and modeling with adequate data. Moreover, the models outperform in the Atlantic Ocean, with a RMSE of 5.37 m/s. The new models have the potential to make a contribution to the study of ocean surface dynamics and be used for forcing numerical models under TC conditions. Full article
(This article belongs to the Special Issue Observations of Atmospheric and Oceanic Processes by Remote Sensing)
►▼ Show Figures

Figure 1

18 pages, 6131 KiB  
Article
Lake Evolution and Its Response to Urban Expansion in Wuhan City in the Last Hundred Years Based on Historical Maps and Remote Sensing Images
by Guoqing Li, Yufen Zhang and Chang’an Li
Remote Sens. 2025, 17(9), 1563; https://doi.org/10.3390/rs17091563 - 28 Apr 2025
Viewed by 122
Abstract
Wuhan is dotted with lakes, is known as the “City with Hundreds of Lakes”, and the development of the city is inseparable from the river and lake waters, with the evolution of the lakes affecting the construction and layout of the city. Since [...] Read more.
Wuhan is dotted with lakes, is known as the “City with Hundreds of Lakes”, and the development of the city is inseparable from the river and lake waters, with the evolution of the lakes affecting the construction and layout of the city. Since the 20th century, the lake evolution in the main urban area of Wuhan has been the most intense and the urban development has also been the most rapid. Therefore, on the basis of the study of the origin of different types of lakes, based on the precious high-precision historical maps of Wuhan in the early- and mid-20th century, combined with the information about lakes in Wuhan obtained from satellite remote sensing images, the evolution characteristics of the lakes in Wuhan in the past 100 years (1920~2019) were investigated through the theory of landscape fractal, and the response mechanism of lake evolution to urban expansion was further explored by being combined with the trajectory of urban expansion. The results show that the area of lakes in Wuhan declined from 2133.5 km2 in 1920 to 550.8 km2 in 2019, with a total decrease of 1582.7 km2, an area shrinkage rate of 74.18%, and a strong amplitude of area change. The changes in the fractal dimension and the shoreline development coefficient of lakes in Wuhan city show synchronization as a whole, with occasional fluctuations, but on the whole, the fractal dimension and shoreline development coefficient of lakes are becoming smaller over a century. Specifically, the evolution of lakes in the Hankou area is mainly affected by the construction of dykes and lake filling, and most of the lakes are resolved and fragmented under the influence of urban expansion, whereas the evolution of lakes in Wuchang and Hanyang is mainly caused by the urban construction around the lakes, and many lake branches have been cut for various urban constructions, and the shape of the lake tends to be simple and regular under the influence of urban expansion. This study is of great significance for filling in the history of lake evolution in Wuhan before the popularization of remote sensing, and for guiding the rational development of lakes in Wuhan and the sustainable and healthy development of Wuhan. Full article
►▼ Show Figures

Figure 1

20 pages, 6790 KiB  
Article
LD-Det: Lightweight Ship Target Detection Method in SAR Images via Dual Domain Feature Fusion
by Hang Yu, Bingzong Liu, Lei Wang and Teng Li
Remote Sens. 2025, 17(9), 1562; https://doi.org/10.3390/rs17091562 - 28 Apr 2025
Viewed by 66
Abstract
Ship detection technology represents a significant research focus within the application domain of synthetic aperture radar. Among all the detection methods, the deep learning method stands out for its high accuracy and high efficiency. However, large-scale deep learning algorithm training requires huge computing [...] Read more.
Ship detection technology represents a significant research focus within the application domain of synthetic aperture radar. Among all the detection methods, the deep learning method stands out for its high accuracy and high efficiency. However, large-scale deep learning algorithm training requires huge computing power support and large equipment to process, which is not suitable for real-time detection on edge platforms. Therefore, to achieve fast data transmission and little computation complexity, the design of lightweight computing models becomes a research hot point. In order to conquer the difficulties of the high complexity of the existing deep learning model and the balance between efficiency and high accuracy, this paper proposes a lightweight dual-domain feature fusion detection model (LD-Det) for ship target detection. This model designs three effective modules, including the following: (1) a wavelet transform method for image compression and the frequency domain feature extraction; (2) a lightweight partial convolutional module for channel feature extraction; and (3) an improved multidimensional attention module to realize the weight assignment of different dimensional features. Additionally, we propose a hybrid IoU loss function specifically designed to enhance the detection of small objects, improving localization accuracy and robustness. Then, we introduce these modules into the Yolov8 detection algorithm for implementation. The experiments are designed to verify LD-Det’s effectiveness. Compared with other algorithm models, LD-Det can not only achieve lighter weight but also take into account the precision of ship target detection. The experimental results from the SSDD dataset demonstrate that the proposed LD-Det model improves precision (P) by 1.4 percentage points while reducing the number of model parameters by 20% compared to the baseline. LD-Det effectively balances lightweight efficiency and detection accuracy, making it highly advantageous for deployment on edge platforms compared to other models. Full article
►▼ Show Figures

Figure 1

25 pages, 18349 KiB  
Article
Surface-Dependent Meteorological Responses to a Taklimakan Dust Event During Summer near the Northern Slope of the Tibetan Plateau
by Binrui Wang, Hongyu Ji, Zhida Zhang, Jiening Liang, Lei Zhang, Mengqi Li, Rui Qiu, Hongjing Luo, Weiming An, Pengfei Tian and Mansur O. Amonov
Remote Sens. 2025, 17(9), 1561; https://doi.org/10.3390/rs17091561 - 28 Apr 2025
Viewed by 170
Abstract
The northern slope of the Tibetan Plateau (TP) is the crucial affected area for dust originating from the Taklimakan Desert (TD). However, few studies have focused on the meteorological element responses to TD dust over different surface types near the TP. Satellite data [...] Read more.
The northern slope of the Tibetan Plateau (TP) is the crucial affected area for dust originating from the Taklimakan Desert (TD). However, few studies have focused on the meteorological element responses to TD dust over different surface types near the TP. Satellite data and the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to analyze the dust being transported from the TD to the TP and its effect from 30 July to 2 August 2016. In the TD, the middle-upper dust layer weakened the solar radiation reaching the lower dust layer, which reduced the temperature within the planetary boundary layer (PBL) during daytime. At night, the dust’s thermal preservation effect increased temperatures within the PBL and decreased temperatures at approximately 0.5 to 2.5 km above PBL. In the TP without snow cover, dust concentration was one-fifth of the TD, while the cooling layer intensity was comparable to the TD. However, within the PBL, the lower concentration and thickness of dust allowed dust to heat atmospheric continuously throughout the day. In the TP with snow cover, dust diminished planetary albedo, elevating temperatures above 6 km, hastening snow melting, which absorbed latent heat and increased the atmospheric water vapor content, consequently decreasing temperatures below 6 km. Surface meteorological element responses to dust varied significantly across different surface types. In the TD, 2 m temperature (T2) decreased by 0.4 °C during daytime, with the opposite nighttime variation. In the TP without snow cover, T2 was predominantly warming. In the snow-covered TP, T2 decreased throughout the day, with a maximum cooling of 1.12 °C and decreased PBL height by up to 258 m. Additionally, a supplementary simulation of a dust event from 17 June to 19 June 2016 further validated our findings. The meteorological elements response to dust is significantly affected by the dust concentration, thickness, and surface type, with significant day–night differences, suggesting that surface types and dust distribution should be considered in dust effect studies to improve the accuracy of climate predictions. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
►▼ Show Figures

Figure 1

19 pages, 6469 KiB  
Article
Long-Term Impact of Extreme Weather Events on Grassland Growing Season Length on the Mongolian Plateau
by Wanyi Zhang, Qun Guo, Genan Wu, Kiril Manevski and Shenggong Li
Remote Sens. 2025, 17(9), 1560; https://doi.org/10.3390/rs17091560 - 28 Apr 2025
Viewed by 188
Abstract
Quantifying extreme weather events (EWEs) and understanding their impacts on vegetation phenology is crucial for assessing ecosystem stability under climate change. This study systematically investigated the ecosystem growing season length (GL) response to four types of EWEs—extreme heat, extreme cold, extreme wetness (surplus [...] Read more.
Quantifying extreme weather events (EWEs) and understanding their impacts on vegetation phenology is crucial for assessing ecosystem stability under climate change. This study systematically investigated the ecosystem growing season length (GL) response to four types of EWEs—extreme heat, extreme cold, extreme wetness (surplus precipitation), and extreme drought (lack of precipitation). The EWE extremity thresholds were found statistically using detrended long time series (2000–2022) ERA5 meteorological data through z-score transformation. The analysis was based on a grassland ecosystem in the Mongolian Plateau (MP) from 2000 to 2022. Using solar-induced chlorophyll fluorescence data and event coincidence analysis, we evaluated the probability of GL anomalies coinciding with EWEs and assessed the vegetation sensitivity to climate variability. The analysis showed that 83.7% of negative and 87.4% of positive GL anomalies were associated with one or more EWEs, with extreme wetness (27.0%) and extreme heat (25.4%) contributing the most. These findings highlight the dominant role of EWEs in shaping phenological shifts. Negative GL anomalies were more strongly linked to EWEs, particularly in arid and cold regions where extreme drought and cold shortened the growing season. Conversely, extreme heat and wetness had a greater influence in warmer and wetter areas, driving both the lengthening and shortening of GL. Furthermore, background hydrothermal conditions modulated the vegetation sensitivity, with warmer regions being more susceptible to heat stress and drier regions more vulnerable to drought. These findings emphasize the importance of regional weather variability and climate characteristics in shaping vegetation phenology and provide new insights into how weather extremes impact ecosystem stability in semi-arid and arid regions. Future research should explore extreme weather events and the role of human activities to enhance predictions of vegetation–climate interactions in grassland ecosystems of the MP. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Data for Water Resource Management in Agriculture)
►▼ Show Figures

Figure 1

23 pages, 46352 KiB  
Article
Unveiling the Spatial Variation in Ecosystem Services Interactions and Their Drivers Within the National Key Ecological Function Zones, China
by Tingjing Zhang, Quanqin Shao and Haibo Huang
Remote Sens. 2025, 17(9), 1559; https://doi.org/10.3390/rs17091559 - 27 Apr 2025
Viewed by 174
Abstract
Understanding the spatial differentiation of ecosystem service (ES) interactions and their underlying driving mechanisms is crucial for effective ecosystem management and enhancing regional landscape sustainability. However, comprehensive analyses of the effects of key influencing factors on ES interactions remains limited, especially regarding the [...] Read more.
Understanding the spatial differentiation of ecosystem service (ES) interactions and their underlying driving mechanisms is crucial for effective ecosystem management and enhancing regional landscape sustainability. However, comprehensive analyses of the effects of key influencing factors on ES interactions remains limited, especially regarding the nonlinear driving mechanisms of factors and their regional heterogeneity. We assessed and validated five key ES in the National Key Ecological Function Zones (NKEFZs) of China—net primary productivity (NPP), soil conservation (SC), sandstorm prevention (SP), water retention (WR), and biodiversity maintenance (BM). By integrating the optimal parameter geographical detector with constraint line methods, we further explored the complex responses of ES interactions to driving factors across different functional zones. The results showed that most ES exhibited significant spatial synergistic clustering. In contrast, widespread spatial trade-off clustering was detected in ES pairs related to WR, mainly distributed in the Tibetan Plateau, northeast China, and the Southern Hills region. Due to the improvement in ES, the overall synergies of ES enhanced from 2000 to 2020. The dominant factors in different functional zones influenced ES interactions in a non-stationary manner, with the same factors potentially showing diverse effect types in different sub-regions. Additionally, we detected the dominant role of landscape configuration factors in sub-regions for specific interaction types (e.g., WR-NPP interaction in the SP zones), suggesting the potential for achieving multi-ES synergies through landscape planning without altering landscape composition. This research provides valuable insights into understanding ES interactions and offers a scientific foundation for the implementation of ecological protection and restoration plans. Full article
(This article belongs to the Special Issue Remote Sensing Based Monitoring of Terrestrial Ecosystem Service Bundles, Trade-Offs and Synergies (Second Edition))
►▼ Show Figures

Graphical abstract

29 pages, 29845 KiB  
Article
Post-Processing Optimization of the Global 30 m Land Cover Dynamic Monitoring Product
by Zhehua Li, Xiao Zhang, Wendi Liu, Tingting Zhao, Weitao Ai, Jinqing Wang and Liangyun Liu
Remote Sens. 2025, 17(9), 1558; https://doi.org/10.3390/rs17091558 - 27 Apr 2025
Viewed by 114
Abstract
Post-processing optimization refers to the refinement of land cover products by applying specific rules or algorithms to minimize erroneous changes in land cover types caused by classification uncertainty or interannual phenological variations. Global land cover (GLC) mapping has gained significant attention over the [...] Read more.
Post-processing optimization refers to the refinement of land cover products by applying specific rules or algorithms to minimize erroneous changes in land cover types caused by classification uncertainty or interannual phenological variations. Global land cover (GLC) mapping has gained significant attention over the past decade, but current GLC time-series products suffer from considerable inconsistencies in mapping results between different epochs, leading to severe erroneous changes. Here, we aimed to design a novel post-processing approach by combining multi-source data to optimize the GLC_FCS30D product, which represents a groundbreaking improvement in GLC dynamic mapping at a resolution of 30 m. First, spatiotemporal filtering with a window size of 3 × 3 × 3 was applied to reduce the “salt-and-pepper” effect. Second, a temporal consistency optimization algorithm based on LandTrendr was used to identify land cover changes across the entire time series and eliminate excessively frequent erroneous changes. Third, certain land cover transitions between easily misclassified types were optimized using logical rules and multi-source data. Specifically, the illogical wetland-related transitions (wetland–water and wetland–forest) were corrected using a simple replacement rule. To address the noticeable erroneous changes in arid and semi-arid regions, the erroneous land cover transitions involving bare areas, sparse vegetation, grassland, and shrubland were corrected by combining NDVI and precipitation data. Finally, the performance of our post-processing optimization approach was evaluated and quantified. The proposed approach successfully reduced the cumulative change area from 7537.00 million hectares (Mha) in the GLC_FCS30D product without optimization to 1981.00 Mha in the GLC_FCS30D product with optimization, eliminating 5556.00 Mha of erroneous changes across 26 epochs. Furthermore, the overall accuracy of the mapping was also improved from 73.04% to 74.24% for the Land Cover Classification System (LCCS) level-1 validation system. Erroneous changes in GLC_FCS30D were considerably mitigated with the post-processing optimization method, providing more reliable insights into GLC changes from 1985 to 2022 at a 30 m resolution. Full article
►▼ Show Figures

Figure 1

15 pages, 2685 KiB  
Technical Note
Enhancing Multi-Flight Unmanned-Aerial-Vehicle-Based Detection of Wheat Canopy Chlorophyll Content Using Relative Radiometric Correction
by Jiale Jiang, Qianyi Zhang and Shuai Gao
Remote Sens. 2025, 17(9), 1557; https://doi.org/10.3390/rs17091557 - 27 Apr 2025
Viewed by 164
Abstract
Unmanned aerial vehicle (UAV) remote sensing has emerged as a powerful tool for precision agriculture, offering high-resolution crop monitoring capabilities. However, multi-flight UAV missions introduce radiometric inconsistencies that hinder the accuracy of vegetation indices and physiological trait estimation. This study investigates the efficacy [...] Read more.
Unmanned aerial vehicle (UAV) remote sensing has emerged as a powerful tool for precision agriculture, offering high-resolution crop monitoring capabilities. However, multi-flight UAV missions introduce radiometric inconsistencies that hinder the accuracy of vegetation indices and physiological trait estimation. This study investigates the efficacy of relative radiometric correction in enhancing canopy chlorophyll content (CCC) estimation for winter wheat. Dual UAV sensor configurations captured multi-flight imagery across three experimental sites and key wheat phenological stages (the green-up, heading, and grain filling stages). Sentinel-2 data served as an external radiometric reference. The results indicate that relative radiometric correction significantly improved spectral consistency, reducing RMSE values (in spectral bands by >86% and in vegetation indices by 38–96%) and enhancing correlations with Sentinel-2 reflectance. The predictive accuracy of CCC models improved after the relative radiometric correction, with validation errors decreasing by 17.1–45.6% across different growth stages and with full-season integration yielding a 44.3% reduction. These findings confirm the critical role of relative radiometric correction in optimizing multi-flight UAV-based chlorophyll estimation, reinforcing its applicability for dynamic agricultural monitoring. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
►▼ Show Figures

Figure 1

19 pages, 4512 KiB  
Article
AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes
by Zhenteng Li, Sheng Lian, Dengfeng Pan, Youlin Wang and Wei Liu
Remote Sens. 2025, 17(9), 1556; https://doi.org/10.3390/rs17091556 - 27 Apr 2025
Viewed by 129
Abstract
Object detection in unmanned aerial vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, [...] Read more.
Object detection in unmanned aerial vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, this paper proposes AD-Det, a novel framework employing a coherent coarse-to-fine strategy that seamlessly integrates two pivotal components: adaptive small object enhancement (ASOE) and dynamic class-balanced copy–paste (DCC). ASOE utilizes a high-resolution feature map to identify and cluster regions containing small objects. These regions are subsequently enlarged and processed by a fine-grained detector. On the other hand, DCC conducts object-level resampling by dynamically pasting tail classes around the cluster centers obtained by ASOE, maintaining a dynamic memory bank for each tail class. This approach enables AD-Det to not only extract regions with small objects for precise detection but also dynamically perform reasonable resampling for tail-class objects. Consequently, AD-Det enhances the overall detection performance by addressing the challenges of scale variations and class imbalance in UAV images through a synergistic and adaptive framework. We extensively evaluate our approach on two public datasets, i.e., VisDrone and UAVDT, and demonstrate that AD-Det significantly outperforms existing competitive alternatives. Notably, AD-Det achieves a 37.5% average precision (AP) on the VisDrone dataset, surpassing its counterparts by at least 3.1%. Full article
(This article belongs to the Special Issue Applications of Remote Sensing Imagery for Urban Areas (Second Edition))
►▼ Show Figures

Figure 1

22 pages, 5095 KiB  
Article
Modeling and Correction Methods for Positioning Errors in Loran System at Sea
by Jingling Li and Huabing Wu
Remote Sens. 2025, 17(9), 1555; https://doi.org/10.3390/rs17091555 - 27 Apr 2025
Viewed by 67
Abstract
Loran is a crucial maritime navigation system and is also considered a key backup for satellite navigation systems. To enhance positioning and timing services, improving the accuracy of the Loran system is essential. This paper discusses the factors affecting Loran’s positioning and timing [...] Read more.
Loran is a crucial maritime navigation system and is also considered a key backup for satellite navigation systems. To enhance positioning and timing services, improving the accuracy of the Loran system is essential. This paper discusses the factors affecting Loran’s positioning and timing performance, with a focus on ASF (additional secondary factor) measurement techniques and filtering methods. This study specifically addresses challenges in maritime navigation and employs a first-order Gauss–Markov process to simulate ASF+SF values. This approach eliminates the need for precise geodetic distances or real-time GNSS corrections. The research included experimental tests conducted along the eastern coast of China, evaluating environmental conditions and the positioning station’s location data. Positioning calculations were performed under maritime navigation conditions. The experimental results demonstrate that when satellite navigation systems are unavailable, the proposed model significantly enhances navigation accuracy. The accuracy, previously at the level of several hundred meters, was improved to approximately 40 m, making Loran a more reliable alternative for maritime applications. Full article
►▼ Show Figures

Figure 1

23 pages, 10361 KiB  
Article
OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPR
by Congde Lu, Senguo Cao, Xiao Wang, Guanglai Jin, Siqi Wang and Wenlong Cai
Remote Sens. 2025, 17(9), 1554; https://doi.org/10.3390/rs17091554 - 27 Apr 2025
Viewed by 77
Abstract
Accurate detection of interlayer distress based on ground-penetrating radar has been widely adopted for in-service asphalt pavement condition assessment to improve maintenance efficiency and reduce costs. However, accurate interlayer distress locating is challenging with limited adaptability to their large-scale variations, which significantly weakens [...] Read more.
Accurate detection of interlayer distress based on ground-penetrating radar has been widely adopted for in-service asphalt pavement condition assessment to improve maintenance efficiency and reduce costs. However, accurate interlayer distress locating is challenging with limited adaptability to their large-scale variations, which significantly weakens the detection performance. This study proposed a novel automatic detection network based on YOLOv5s to detect interlayer distresses in asphalt pavement named OEM-HWNet. Firstly, an object enhancement module based on prior knowledge was designed to locate the regions of interlayer distress and enhance their characteristics. Then, wavelet convolution was added to increase the receptive field of the network and enhance the ability to capture low-frequency information. Finally, an additional detection head was added to improve the detection capability of interlayer distress with different sizes. Experiments demonstrated that the proposed network achieves a mean average precision (mAP) of 89.6%, outperforming other advanced models, such as YOLOv5s, YOLOv8s, YOLOv11s, and Faster R-CNN. Incorporating prior knowledge into deep learning networks could provide an effective solution to detect interlayer distress of asphalt pavement. Full article
(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-103
Previous Issue
Back to TopTop