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Remote Sens., Volume 16, Issue 21 (November-1 2024) – 204 articles

Cover Story (view full-size image): Over the last two decades, several new approaches based on the processing of point clouds to obtain individual trees and the automatic estimation of tree-level attributes (tree height (H), crown diameter (CD), etc.) with high accuracy have emerged. However, these approaches use many parameters that need to be tuned (i.e., local tuning). This parameter tuning is time-consuming and requires learning and understanding the meaning and role of each parameter. In this study, three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual trees and extract their H and CD using two types of point: (1) Low-Density Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on UAV Imagery. A methodology requiring minimal user interactions has been developed for application in large areas of Mediterranean forests. View this paper
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22 pages, 16745 KiB  
Article
Unsupervised PolSAR Image Classification Based on Superpixel Pseudo-Labels and a Similarity-Matching Network
by Lei Wang, Lingmu Peng, Rong Gui, Hanyu Hong and Shenghui Zhu
Remote Sens. 2024, 16(21), 4119; https://doi.org/10.3390/rs16214119 - 4 Nov 2024
Viewed by 938
Abstract
Supervised polarimetric synthetic aperture radar (PolSAR) image classification demands a large amount of precisely labeled data. However, such data are difficult to obtain. Therefore, many unsupervised methods have been proposed for unsupervised PolSAR image classification. The classification maps of unsupervised methods contain many [...] Read more.
Supervised polarimetric synthetic aperture radar (PolSAR) image classification demands a large amount of precisely labeled data. However, such data are difficult to obtain. Therefore, many unsupervised methods have been proposed for unsupervised PolSAR image classification. The classification maps of unsupervised methods contain many high-confidence samples. These samples, which are often ignored, can be used as supervisory information to improve classification performance on PolSAR images. This study proposes a new unsupervised PolSAR image classification framework. The framework combines high-confidence superpixel pseudo-labeled samples and semi-supervised classification methods. The experiments indicated that this framework could achieve higher-level effectiveness in unsupervised PolSAR image classification. First, superpixel segmentation was performed on PolSAR images, and the geometric centers of the superpixels were generated. Second, the classification maps of rotation-domain deep mutual information (RDDMI), an unsupervised PolSAR image classification method, were used as the pseudo-labels of the central points of the superpixels. Finally, the unlabeled samples and the high-confidence pseudo-labeled samples were used to train an excellent semi-supervised method, similarity matching (SimMatch). Experiments on three real PolSAR datasets illustrated that, compared with the excellent RDDMI, the accuracy of the proposed method was increased by 1.70%, 0.99%, and 0.8%. The proposed framework provides significant performance improvements and is an efficient method for improving unsupervised PolSAR image classification. Full article
(This article belongs to the Special Issue SAR in Big Data Era III)
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16 pages, 8722 KiB  
Article
Evaluation of the Predictive Capability of CMA Climate Prediction System Model for Summer Surface Heat Source on the Tibetan Plateau
by Xinyu Chen, Minhong Song, Yaqi Wang and Tongwen Wu
Remote Sens. 2024, 16(21), 4118; https://doi.org/10.3390/rs16214118 - 4 Nov 2024
Viewed by 551
Abstract
Surface heat source (SHS) is a crucial factor affecting local weather systems. Particularly SHS on the Tibetan Plateau (TP) significantly influences East Asian atmospheric circulation and global climate. Accurate prediction of summer SHS on the TP is of urgent demand for economic development [...] Read more.
Surface heat source (SHS) is a crucial factor affecting local weather systems. Particularly SHS on the Tibetan Plateau (TP) significantly influences East Asian atmospheric circulation and global climate. Accurate prediction of summer SHS on the TP is of urgent demand for economic development and local climate change. To evaluate the performance of SHS on the TP, the observed SHS data from the eleven sites on the TP verified against CRA40-land (CRA) is evidenced significantly better than ERA5-land (ERA5), another widely used reanalysis. The predictive capability of the CMA Climate Prediction System Model (CMA-CPS) for SHS on the TP was assessed using multiple scoring methods, including the anomaly correlation coefficient and temporal correlation coefficient, among others. Furthermore, relative variability and trend analysis were conducted. Finally, based on these assessments, the causes of the biases were preliminarily discussed. The CMA-CPS demonstrates a reasonable ability to predict the spatial distribution patterns of SHS, sensible heat (SH), and latent heat (LH) on the TP in summer. Specifically, the prediction results of SHS and LH exhibit an “east-high and west-low” distribution, while the distribution of the predicted SH is opposite. Nevertheless, the predicted values are generally lower than CRA, particularly in interannual variations and trends. Among the predictions, LH exhibits the highest temporal correlation coefficients, consistently above 0.6, followed by SHS, while SH predictions are less accurate. The spatial distribution and skill scores indicate that LH on the TP contributes more significantly to SHS than SH in summer. Furthermore, discrepancies in the predictions of surface temperature gradients, ground wind speed, and humidity on the TP may partly explain the biases in SHS and their components. Full article
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16 pages, 39531 KiB  
Technical Note
A Geophysical Investigation in Which 3D Electrical Resistivity Tomography and Ground-Penetrating Radar Are Used to Determine Singularities in the Foundations of the Protected Historic Tower of Murcia Cathedral (Spain)
by María C. García-Nieto, Marcos A. Martínez-Segura, Manuel Navarro, Ignacio Valverde-Palacios and Pedro Martínez-Pagán
Remote Sens. 2024, 16(21), 4117; https://doi.org/10.3390/rs16214117 - 4 Nov 2024
Viewed by 741
Abstract
This study presents a procedure in which 3D electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to determine singularities in the foundations of protected historic towers, where space is limited due to their characteristics and location in highly populated areas. This [...] Read more.
This study presents a procedure in which 3D electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to determine singularities in the foundations of protected historic towers, where space is limited due to their characteristics and location in highly populated areas. This study was carried out on the Tower of the Cathedral “Santa Iglesia Catedral de Santa María” in Murcia, Spain. The novel distribution of a continuous nonlinear profile along the outer and inner perimeters of the Tower allowed us to obtain a 3D ERT model of the subsoil, even under its load-bearing walls. This nonlinear configuration of the electrodes allowed us to reach adequate investigation depths in buildings with limited interior and exterior space for data collection without disturbing the historic structure. The ERT results were compared with GPR measurements and with information from archaeological excavations conducted in 1999 and 2009. The geometry and distribution of the cavities in the entire foundation slab of the Tower were determined, verifying the proposed procedure. This methodology allows the acquisition of a detailed understanding of the singularities of the foundations of protected historic towers in urban areas with limited space, reducing time and costs and avoiding the use of destructive techniques, with the aim of implementing a more efficient and effective strategy for the protection of other tower foundations. Full article
(This article belongs to the Special Issue 3D Virtual Reconstruction for Cultural Heritage (Second Edition))
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19 pages, 10774 KiB  
Article
Using Resistivity Structure to Study the Seismogenic Mechanism of the 2021 Luxian Ms6.0 Earthquakes
by Xuehua Liu, Yan Zhan, Lingqiang Zhao, Xiangyu Sun and Xiaoyu Lou
Remote Sens. 2024, 16(21), 4116; https://doi.org/10.3390/rs16214116 - 4 Nov 2024
Viewed by 659
Abstract
Over the past few years, there has been a noticeable change in the occurrence of seismic disasters in Sichuan, China. The focus has shifted from Western Sichuan to the previously more stable Southeastern Sichuan. The recent Ms6.0 earthquake in Luxian, Southeastern Sichuan, [...] Read more.
Over the past few years, there has been a noticeable change in the occurrence of seismic disasters in Sichuan, China. The focus has shifted from Western Sichuan to the previously more stable Southeastern Sichuan. The recent Ms6.0 earthquake in Luxian, Southeastern Sichuan, on 16 September 2021, has once again captured the interest of scholars, who are closely examining the seismogenic environment and potential seismic hazards in the region. We conducted a magnetotelluric (MT) array survey in the Luxian earthquake area to explore the deep seismogenic environment of the 2021 Luxian Ms6.0 earthquake zone and understand the potential effects of industrial extraction on seismic activities. Here are the insights we obtained: Underneath the anticline in the Luxian Ms6.0 earthquake area, there is a structure that mainly exhibits high resistance. On the other hand, beneath the syncline, a structure with medium to low resistance is observed. The epicenter of the mainshock was identified near the intersection of high- and low-resistance media within the Fuji syncline area. Smaller aftershocks that followed the mainshock were mainly concentrated in the low-resistance layers at depths of 3–5 km in the Fuji syncline area. MT survey results have confirmed the existence of a detachment zone in the shallow crust near the epicenter of the Luxian Ms6.0 earthquake. It is believed that this detachment layer played a significant role in the seismogenic process of the Luxian Ms6.0 earthquake. During different stress conditions, this layer became active and caused the compression and faulting of a hidden fault below, resulting in the Luxian Ms6.0 earthquake. After the main earthquake, a series of smaller aftershocks with varying focal mechanisms occurred as the stress fields continued to release. It is important to note that the Luxian Ms6.0 earthquake highlights the ongoing high stress levels in the southern region of the Sichuan Basin. This emphasizes the need for continued monitoring and consideration of potential seismic hazards in the southern Sichuan area. Full article
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22 pages, 5092 KiB  
Article
Shifting from Trade-Offs to Synergies in Ecosystem Services Through Effective Ecosystem Management in Arid Areas
by Yan Xu, Xiaoyun Song, Mingjiang Deng, Tao Bai and Wanghai Tao
Remote Sens. 2024, 16(21), 4115; https://doi.org/10.3390/rs16214115 - 4 Nov 2024
Viewed by 694
Abstract
Human activities continuously alter the delivery of ecosystem services (ESs), which play a crucial role in human well-being. There is a pressing need for effective ecological management strategies that consider the spatial heterogeneity of ESs to support the transition from trade-offs to synergies. [...] Read more.
Human activities continuously alter the delivery of ecosystem services (ESs), which play a crucial role in human well-being. There is a pressing need for effective ecological management strategies that consider the spatial heterogeneity of ESs to support the transition from trade-offs to synergies. This study focuses on the Haba River Basin and examines characteristics of land-use change and the shift from trade-offs to synergies. The results indicate that from 1990 to 2000, the initial phase of land development, 10.65% of the land experienced change. Subsequently, during the intensive period of land development from 2000 to 2010, 30.29% of the land underwent significant transformation, with approximately 78% of grassland, sparse grassland, forested land, and desert converted into arable land. However, between 2010 and 2020, as the focus shifted towards the establishment of native vegetation. The intensity of land development decreased, and only a small percentage (3.65%) of the total area underwent changes. Based on an in-depth analysis of spatial heterogeneity from 1990 to 2020, it is believed there has been a shift from trade-offs to co-benefits between 2000–2010 and 2010–2020. The years 2010 and 2020 were pivotal time nodes for the transition from trade-offs to synergies and for reducing trade-offs, with NPP identified as a critical driving factor for comprehensive ES (CES) functions. By considering the trade-off–synergy relationship and hotspots of ecological service functions, combined with unified water resource management policies, comprehensive ecological management measures tailored to different regions are proposed. These measures have facilitated the implementation of robust ecological protection policies to shift ES development from trade-offs to synergies in arid areas, thereby enhancing overall ecosystem service functions in the Haba River Basin. The research findings offer crucial scientific support and guidance for ecosystem management in arid areas, particularly within Central Asia. Full article
(This article belongs to the Section Remote Sensing for Geospatial Science)
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15 pages, 14372 KiB  
Article
Calibration of Dual-Polarised Antennas for Air-Coupled Ground Penetrating Radar Applications
by Samuel J. I. Forster, Anthony J. Peyton and Frank J. W. Podd
Remote Sens. 2024, 16(21), 4114; https://doi.org/10.3390/rs16214114 - 4 Nov 2024
Cited by 1 | Viewed by 900
Abstract
Radar polarimetry is a technique that can be used to enhance target detection, identification and classification; however, the quality of these measurements can be significantly influenced by the characteristics of the radar antenna. For an accurate and reliable system, the calibration of the [...] Read more.
Radar polarimetry is a technique that can be used to enhance target detection, identification and classification; however, the quality of these measurements can be significantly influenced by the characteristics of the radar antenna. For an accurate and reliable system, the calibration of the antenna is vitally important to mitigate these effects. This study presents a methodology to calibrate Ultra-Wideband (UWB) dual-polarised antennas in the near-field using a thin elongated metallic cylinder as the calibration object. The calibration process involves measuring the scattering matrix of the metallic cylinder as it is rotated, in this case producing 100 distinct scattering matrices from which the calibration parameters are derived, facilitating a robust and stable solution. The calibration procedure was tested and validated using a Vector Network Analyser (VNA) and two quad-ridged antennas, which presented different performance levels. The calibration methodology demonstrated notable improvements, aligning the performance of both functioning and under-performing antennas to equivalent specifications. Mid-band validation measurements indicated minimal co-polar channel imbalance (<0.3 dB), low phase error (<0.8°) and improved cross-polar isolation (≈48 dB). Full article
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45 pages, 7187 KiB  
Review
A Review of Deep Learning-Based Remote Sensing Image Caption: Methods, Models, Comparisons and Future Directions
by Ke Zhang, Peijie Li and Jianqiang Wang
Remote Sens. 2024, 16(21), 4113; https://doi.org/10.3390/rs16214113 - 4 Nov 2024
Viewed by 1509
Abstract
Remote sensing images contain a wealth of Earth-observation information. Efficient extraction and application of hidden knowledge from these images will greatly promote the development of resource and environment monitoring, urban planning and other related fields. Remote sensing image caption (RSIC) involves obtaining textual [...] Read more.
Remote sensing images contain a wealth of Earth-observation information. Efficient extraction and application of hidden knowledge from these images will greatly promote the development of resource and environment monitoring, urban planning and other related fields. Remote sensing image caption (RSIC) involves obtaining textual descriptions from remote sensing images through accurately capturing and describing the semantic-level relationships between objects and attributes in the images. However, there is currently no comprehensive review summarizing the progress in RSIC based on deep learning. After defining the scope of the papers to be discussed and summarizing them all, the paper begins by providing a comprehensive review of the recent advancements in RSIC, covering six key aspects: encoder–decoder framework, attention mechanism, reinforcement learning, learning with auxiliary task, large visual language models and few-shot learning. Subsequently a brief explanation on the datasets and evaluation metrics for RSIC is given. Furthermore, we compare and analyze the results of the latest models and the pros and cons of different deep learning methods. Lastly, future directions of RSIC are suggested. The primary objective of this review is to offer researchers a more profound understanding of RSIC. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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23 pages, 5110 KiB  
Article
FireNet: A Lightweight and Efficient Multi-Scenario Fire Object Detector
by Yonghuan He, Age Sahma, Xu He, Rong Wu and Rui Zhang
Remote Sens. 2024, 16(21), 4112; https://doi.org/10.3390/rs16214112 - 4 Nov 2024
Viewed by 854
Abstract
Fire and smoke detection technologies face challenges in complex and dynamic environments. Traditional detectors are vulnerable to background noise, lighting changes, and similar objects (e.g., clouds, steam, dust), leading to high false alarm rates. Additionally, they struggle with detecting small objects, limiting their [...] Read more.
Fire and smoke detection technologies face challenges in complex and dynamic environments. Traditional detectors are vulnerable to background noise, lighting changes, and similar objects (e.g., clouds, steam, dust), leading to high false alarm rates. Additionally, they struggle with detecting small objects, limiting their effectiveness in early fire warnings and rapid responses. As real-time monitoring demands grow, traditional methods often fall short in smart city and drone applications. To address these issues, we propose FireNet, integrating a simplified Vision Transformer (RepViT) to enhance global feature learning while reducing computational overhead. Dynamic snake convolution (DSConv) captures fine boundary details of flames and smoke, especially in complex curved edges. A lightweight decoupled detection head optimizes classification and localization, ideal for high inter-class similarity and small targets. FireNet outperforms YOLOv8 on the Fire Scene dataset (FSD) with a [email protected] of 80.2%, recall of 78.4%, and precision of 82.6%, with an inference time of 26.7 ms. It also excels on the FSD dataset, addressing current fire detection challenges. Full article
(This article belongs to the Section AI Remote Sensing)
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20 pages, 22822 KiB  
Article
Monitoring Aeolian Erosion from Surface Coal Mines in the Mongolian Gobi Using InSAR Time Series Analysis
by Jungrack Kim, Bayasgalan Amgalan and Amanjol Bulkhbai
Remote Sens. 2024, 16(21), 4111; https://doi.org/10.3390/rs16214111 - 3 Nov 2024
Viewed by 1116
Abstract
Surface mining in the southeastern Gobi Desert has significant environmental impacts, primarily due to the creation of large coal piles that are highly susceptible to aeolian processes. Using spaceborne remote sensing and numerical simulations, we investigated erosional processes and their environmental impacts. Our [...] Read more.
Surface mining in the southeastern Gobi Desert has significant environmental impacts, primarily due to the creation of large coal piles that are highly susceptible to aeolian processes. Using spaceborne remote sensing and numerical simulations, we investigated erosional processes and their environmental impacts. Our primary tool was Interferometric Synthetic Aperture Radar (InSAR) data from Sentinel-1 imagery collected between 2017 and 2022. We analyzed these data using phase angle information from the Small Baseline InSAR time series framework. The time series analyses revealed intensive aeolian erosion in the coal piles, represented as thin deformation patterns along the potential pathways of aerodynamic transportation. Further analysis of multispectral data, combined with correlations between wind patterns and trajectory simulations, highlighted the detrimental impact of coal dust on the surrounding environment and the mechanism of aeolian erosion. The lack of mitigation measures, such as water spray, appeared to exacerbate erosion and dust generation. This study demonstrates the feasibility of using publicly available remote sensing data to monitor coal mining activities and their environmental hazards. Our findings contribute to a better understanding of coal dust generation processes in surface mining operations as well as the aeolian erosion mechanism in desert environments. Full article
(This article belongs to the Special Issue Remote Sensing and Geophysics Methods for Geomorphology Research)
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21 pages, 5891 KiB  
Article
Detection of Ship Wakes in Dynamic Sea Surface Video Sequences: A Data-Driven Approach
by Chengcheng Yu, Yanmei Zhang, Meifang Xiao and Zhibo Zhang
Remote Sens. 2024, 16(21), 4110; https://doi.org/10.3390/rs16214110 - 3 Nov 2024
Viewed by 594
Abstract
In order to enhance the detection of maritime vessel targets, considering the causal relationship between the motion of vessels and their wakes, as well as the characteristics of ship wakes such as large diffusion range and distinctive features, this paper proposes a data-driven [...] Read more.
In order to enhance the detection of maritime vessel targets, considering the causal relationship between the motion of vessels and their wakes, as well as the characteristics of ship wakes such as large diffusion range and distinctive features, this paper proposes a data-driven method based on Dynamic Mode Decomposition (DMD) for detecting and analyzing ship wakes in sea surface videos. The method, named Multi-dimensional Dynamic Mode Decomposition (MDDMD), segments the video sequence into smaller blocks and analyzes them at various resolution levels, effectively addressing the data analysis issues of large and complex systems. The MDDMD algorithm not only extracts key dynamic features but also reveals the intrinsic structure of the system at different scales, providing new perspectives for the in-depth understanding of nonlinear systems. Comparative experimental results with existing DMD and PCA algorithms demonstrate that the MDDMD algorithm has higher accuracy and robustness in ship wake detection. This study offers valuable insights for ship wake detection under complex maritime conditions and holds potential for practical application in the field of maritime surveillance. Full article
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26 pages, 5895 KiB  
Article
SeFi-CD: A Semantic First Change Detection Paradigm That Can Detect Any Change You Want
by Ling Zhao, Zhenyang Huang, Yipeng Wang, Chengli Peng, Jun Gan, Haifeng Li and Chao Hu
Remote Sens. 2024, 16(21), 4109; https://doi.org/10.3390/rs16214109 - 3 Nov 2024
Viewed by 995
Abstract
The existing change detection (CD) methods can be summarized as the visual-first change detection (ViFi-CD) paradigm, which first extracts change features from visual differences and then assigns them specific semantic information. However, CD is essentially dependent on change regions of interest (CRoIs), meaning [...] Read more.
The existing change detection (CD) methods can be summarized as the visual-first change detection (ViFi-CD) paradigm, which first extracts change features from visual differences and then assigns them specific semantic information. However, CD is essentially dependent on change regions of interest (CRoIs), meaning that the CD results are directly determined by the semantics changes in interest, making its primary image factor semantic of interest rather than visual. The ViFi-CD paradigm can only assign specific semantics of interest to specific change features extracted from visual differences, leading to the inevitable omission of potential CRoIs and the inability to adapt to different CRoI CD tasks. In other words, changes in other CRoIs cannot be detected by the ViFi-CD method without retraining the model or significantly modifying the method. This paper introduces a new CD paradigm, the semantic-first CD (SeFi-CD) paradigm. The core idea of SeFi-CD is to first perceive the dynamic semantics of interest and then visually search for change features related to the semantics. Based on the SeFi-CD paradigm, we designed Anything You Want Change Detection (AUWCD). Experiments on public datasets demonstrate that the AUWCD outperforms the current state-of-the-art CD methods, achieving an average F1 score 5.01% higher than that of these advanced supervised baselines on the SECOND dataset, with a maximum increase of 13.17%. The proposed SeFi-CD offers a novel CD perspective and approach. Full article
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17 pages, 1066 KiB  
Article
Efficient Phase Step Determination Approach for Four-Quadrant Wind Imaging Interferometer
by Tingyu Yan, William Ward, Chunmin Zhang and Shiping Guo
Remote Sens. 2024, 16(21), 4108; https://doi.org/10.3390/rs16214108 - 3 Nov 2024
Viewed by 607
Abstract
A four-quadrant wind imaging interferometer is a new generation of wind imaging interferometer with the valuable features of being monolithic, compact, light, and insensitive to temporal variations in the source. Its applications include remote sensing of the wind field of the upper atmosphere [...] Read more.
A four-quadrant wind imaging interferometer is a new generation of wind imaging interferometer with the valuable features of being monolithic, compact, light, and insensitive to temporal variations in the source. Its applications include remote sensing of the wind field of the upper atmosphere and observing important dynamical processes in the mesosphere and lower thermosphere. In this paper, we describe a new phase step determination approach based on the Lissajous figure, which provides an efficient, accurate, and visual method for the characterization and calibration of this type of instrument. Using the data from wavelength or thermal fringe scanning, the phase steps, relative intensities, and instrument visibilities of four quadrants can be retrieved simultaneously. A general model for the four-quadrant wind imaging interferometer is described and the noise sensitivity of this method is analyzed. This approach was successfully implemented with four-quadrant wind imaging interferometer prototypes, and its feasibility was experimentally verified. Full article
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15 pages, 14538 KiB  
Article
Weighted Fusion Method of Marine Gravity Field Model Based on Water Depth Segmentation
by Zhaoyu Chen, Qiankun Liu, Ke Xu and Xiaoyang Liu
Remote Sens. 2024, 16(21), 4107; https://doi.org/10.3390/rs16214107 - 3 Nov 2024
Viewed by 701
Abstract
Among the marine gravity field models derived from satellite altimetry, the Scripps Institution of Oceanography (SIO) series and Denmark Technical University (DTU) series models are the most representative and are often used to integrate global gravity field models, which were inverted by the [...] Read more.
Among the marine gravity field models derived from satellite altimetry, the Scripps Institution of Oceanography (SIO) series and Denmark Technical University (DTU) series models are the most representative and are often used to integrate global gravity field models, which were inverted by the deflection of vertical method and sea surface height method, respectively. The fusion method based on the offshore distance used in the EGM2008 model is just model stitching, which cannot realize the true fusion of the two types of marine gravity field models. In the paper, a new fusion method based on water depth segmentation is proposed, which established the Precision–Depth relationship of each model in each water depth segment in the investigated area, then constructed the FUSION model by weighted fusion based on the precision predicted from the Precision–Depth relationship at each grid in the whole region. The application in the South China Sea shows that the FUSION model built by the new fusion method has better accuracy than SIO28 and DTU17, especially in shallow water and offshore areas. Within 20 km offshore, the RMS of the FUSION model is 5.10 mGal, which is 8% and 4% better than original models, respectively. Within 100 m of shallow water, the accuracy of the FUSION model is 4.01 mGal, which is 14% and 12% higher than the original models, respectively. A further analysis shows that the fusion model is in better agreement with the seabed topography than original models. The new fusion method can blend the effective information of original models to provide a higher-precision marine gravity field. Full article
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16 pages, 13229 KiB  
Article
A Study on the Classification of Shrubs and Grasses on the Tibetan Plateau Based on Unmanned Aerial Vehicle Multispectral Imagery
by Xiaoqiang Chen, Hui Deng, Wenjiang Zhang and Houxi Zhang
Remote Sens. 2024, 16(21), 4106; https://doi.org/10.3390/rs16214106 - 2 Nov 2024
Viewed by 979
Abstract
The ecosystem of the Qinghai–Tibet Plateau is highly fragile due to its unique geographical conditions, with vegetation playing a crucial role in maintaining ecological balance. Thus, accurately monitoring the distribution of vegetation in the plateau region is of paramount importance. This study employs [...] Read more.
The ecosystem of the Qinghai–Tibet Plateau is highly fragile due to its unique geographical conditions, with vegetation playing a crucial role in maintaining ecological balance. Thus, accurately monitoring the distribution of vegetation in the plateau region is of paramount importance. This study employs UAV multispectral imagery in combination with four machine-learning models—Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Random Forest (RF)—to investigate the impact of different features and their combinations on the fine classification of shrubs and grasses on the Qinghai–Tibet Plateau, including Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea. The results indicate that near-infrared spectral information can improve classification accuracy, with improvements of 5.21%, 1.65%, 6.64%, and 5.03% for Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea, respectively. Feature selection effectively reduces redundant information and enhances model classification accuracy, with all four machine-learning models achieving the best performance on the optimized feature set. Furthermore, the RF model performs best on the optimized feature set, achieving an overall accuracy (OA) of 95.32% and a kappa coefficient of 0.94. This study provides important scientific support for the fine classification and ecological monitoring of plateau vegetation. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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18 pages, 10136 KiB  
Article
The Combination Application of FY-4 Satellite Products on Typhoon Saola Forecast on the Sea
by Chun Yang, Bingying Shi and Jinzhong Min
Remote Sens. 2024, 16(21), 4105; https://doi.org/10.3390/rs16214105 - 2 Nov 2024
Viewed by 769
Abstract
Satellite data play an irreplaceable role in global observation data systems. Effective comprehensive application of satellite products will inevitably improve numerical weather prediction. FengYun-4 (FY-4) series satellites can provide not only radiance data but also retrieval data with high temporal and spatial resolutions. [...] Read more.
Satellite data play an irreplaceable role in global observation data systems. Effective comprehensive application of satellite products will inevitably improve numerical weather prediction. FengYun-4 (FY-4) series satellites can provide not only radiance data but also retrieval data with high temporal and spatial resolutions. To evaluate the potential benefits of the combination application of FY-4 Advanced Geostationary Radiance Imager (AGRI) products on Typhoon Saola analysis and forecast, two group of experiments are set up with the Weather Research and Forecasting model (WRF). Compared with the benchmark experiment, whose sea surface temperature (SST) is from the National Centers for Environmental Prediction (NCEP) reanalysis data, the SST replacement experiments with FY-4 A/B SST products significantly improve the track and precipitation forecast, especially with the FY-4B SST product. Based on the above results, AGRI clear-sky and all-sky assimilations with FY-4B SST are implemented with a self-constructed AGRI assimilation module. The results show that the AGRI all-sky assimilation experiment can obtain better analyses and forecasts. Furthermore, it is proven that the combination application of AGRI radiance and SST products is beneficial for typhoon prediction. Full article
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10 pages, 2934 KiB  
Article
A Multivariable Study of a Traveling Ionosphere Disturbance Using the Arecibo Incoherent Scatter Radar
by Qihou Zhou, Yanlin Li and Yun Gong
Remote Sens. 2024, 16(21), 4104; https://doi.org/10.3390/rs16214104 - 2 Nov 2024
Cited by 1 | Viewed by 711
Abstract
We present the first simultaneous observations of a traveling ionosphere wave (TID) event, measuring electron concentration (Ne), vertical plasma drift (Vz), and ion and electron temperatures (Ti, Te) using the Arecibo incoherent [...] Read more.
We present the first simultaneous observations of a traveling ionosphere wave (TID) event, measuring electron concentration (Ne), vertical plasma drift (Vz), and ion and electron temperatures (Ti, Te) using the Arecibo incoherent scatter radar. A TID with a period of 135 min was evident in all four state variables in the thermosphere. The amplitudes of Vz and relative Ti fluctuations show only small height variations from 200 to 500 km and their vertical wavelengths increase with altitude. The Te fluctuation shows different characteristics from EISCAT in both phase and amplitude. When the geomagnetic dip angle is 45°, half of the driving gravity wave’s (GW’s) equatorward velocity is mapped to Vz. This meridional-to-vertical velocity coupling amplifies GW’s effect in Ne through vertical transport. The amplifying and anisotropic effects of the geomagnetic field explain the ubiquitous presence of TIDs and their preferred equatorward propagation direction in the geomagnetic mid-latitudes, as well as the midnight collapse phenomenon observed at Arecibo. Full article
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22 pages, 4067 KiB  
Article
AIFormer: Adaptive Interaction Transformer for 3D Point Cloud Understanding
by Xutao Chu, Shengjie Zhao and Hongwei Dai
Remote Sens. 2024, 16(21), 4103; https://doi.org/10.3390/rs16214103 - 2 Nov 2024
Viewed by 648
Abstract
Recently, significant advancements have been made in 3D point cloud analysis by leveraging transformer architecture in 3D space. However, it remains challenging to effectively implement local and global learning within irregular and sparse structures of 3D point clouds. This paper presents the Adaptive [...] Read more.
Recently, significant advancements have been made in 3D point cloud analysis by leveraging transformer architecture in 3D space. However, it remains challenging to effectively implement local and global learning within irregular and sparse structures of 3D point clouds. This paper presents the Adaptive Interaction Transformer (AIFormer), a novel hierarchical transformer architecture designed to enhance 3D point cloud analysis by fusing local and global features through the adaptive interaction of features. Specifically, AIFormer mainly consists of several stacked AIFormer Blocks. Each AIFormer module employs the Local Relation Aggregation Module and the Global Context Aggregation Module, respectively, to extract local details of relationships within the reference point and long-range dependencies between reference points. Then, the local and global features are fused using the Adaptive Interaction Module for adaptive interaction to optimize the point representation. Additionally, the AIFormer Block further designs geometric relation functions and contextual relative semantic encoding to enhance local and global feature extraction capabilities, respectively. Extensive experiments on three popular 3D point cloud datasets verify that AIFormer achieves state-of-the-art or comparable performances. Our comprehensive ablation study further validates the effectiveness and soundness of the AIFormer design. Full article
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28 pages, 14472 KiB  
Article
Characteristics of R2019 Processing of MODIS Sea Surface Temperature at High Latitudes
by Chong Jia, Peter J. Minnett and Malgorzata Szczodrak
Remote Sens. 2024, 16(21), 4102; https://doi.org/10.3390/rs16214102 - 2 Nov 2024
Viewed by 511
Abstract
Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due [...] Read more.
Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change in the Arctic. Full article
(This article belongs to the Section Ocean Remote Sensing)
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32 pages, 28061 KiB  
Article
Linking Vegetation Phenology to Net Ecosystem Productivity: Climate Change Impacts in the Northern Hemisphere Using Satellite Data
by Hanmin Yin, Xiaofei Ma, Xiaohan Liao, Huping Ye, Wentao Yu, Yue Li, Junbo Wei, Jincheng Yuan and Qiang Liu
Remote Sens. 2024, 16(21), 4101; https://doi.org/10.3390/rs16214101 - 2 Nov 2024
Viewed by 1025
Abstract
With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and [...] Read more.
With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and high latitudes of the Northern Hemisphere. This study comprehensively analyzed vegetation phenological changes and their climate drivers using satellite data. It also investigated the spatial distribution and climate drivers of NEP and further analyzed the sensitivity of NEP to vegetation phenology. The results indicated that the average land surface phenology (LSP) was dominated by a monotonic trend in the study area. LSP derived from different satellite products and retrieval methods exhibited relatively consistent responses to climate. The average SOS and POS for different retrieval methods showed a higher negative correlation with nighttime temperatures compared to daytime temperatures. The average EOS exhibited a higher negative correlation with daytime temperatures than a positive correlation. The correlations between VPD and the average SOS, POS, and EOS showed that the proportion of negative correlations was higher than that of positive correlations. The average annual NEP ranged from 0 to 1000 gC·m−2. The cumulative trends of NEP were mainly monotonically increasing, accounting for 61.04%, followed by monotonically decreasing trends, which accounted for 17.95%. In high-latitude regions, the proportion of positive correlation between VPD and NEP was predominant, while the proportion of negative correlation was predominant in middle-latitude regions. The positive and negative correlations between soil moisture and NEP (48.08% vs. 51.92%) were basically consistent in the study area. The correlation between SOS and POS with NEP was predominantly negative. The correlation between EOS and NEP was overall characterized by a greater proportion of negative correlations than positive correlations. The correlation between LOS and NEP exhibited a positive relationship in most areas. The sensitivity of NEP to vegetation phenological parameters (SOS, POS, and EOS) was negative, while the sensitivity of NEP to LOS was positive (0.75 gC·m−2/d for EVI vs. 0.63 gC·m−2/d for LAI vs. 0.30 gC·m−2/d for SIF). This study provides new insights and a theoretical basis for exploring the relationship between vegetation phenology and NEP under global climate change. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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22 pages, 6887 KiB  
Article
Detecting Water Stress in Winter Wheat Based on Multifeature Fusion from UAV Remote Sensing and Stacking Ensemble Learning Method
by He Zhao, Jingjing Wang, Jiali Guo, Xin Hui, Yunling Wang, Dongyu Cai and Haijun Yan
Remote Sens. 2024, 16(21), 4100; https://doi.org/10.3390/rs16214100 - 2 Nov 2024
Viewed by 788
Abstract
The integration of remote sensing technology and machine learning algorithms represents a new research direction for the rapid and large-scale detection of water stress in modern agricultural crops. However, in solving practical agricultural problems, single machine learning algorithms cannot fully explore the potential [...] Read more.
The integration of remote sensing technology and machine learning algorithms represents a new research direction for the rapid and large-scale detection of water stress in modern agricultural crops. However, in solving practical agricultural problems, single machine learning algorithms cannot fully explore the potential information within the data, lacking stability and accuracy. Stacking ensemble learning (SEL) can combine the advantages of multiple single machine learning algorithms to construct more stable predictive models. In this study, threshold values of stomatal conductance (gs) under different soil water stress indices (SWSIs) were proposed to assist managers in irrigation scheduling. In the present study, six irrigation treatments were established for winter wheat to simulate various soil moisture supply conditions. During the critical growth stages, gs was measured and the SWSI was calculated. A spectral camera mounted on an unmanned aerial vehicle (UAV) captured reflectance images in five bands, from which vegetation indices and texture information were extracted. The results indicated that gs at different growth stages of winter wheat was sensitive to soil moisture supply conditions. The correlation between the gs value and SWSI value was high (R2 > 0.79). Therefore, the gs value threshold can reflect the current soil water stress level. Compared with individual machine learning models, the SEL model exhibited higher prediction accuracy, with R2 increasing by 6.67–17.14%. Using a reserved test set, the SEL model demonstrated excellent performance in various evaluation metrics across different growth stages (R2: 0.69–0.87, RMSE: 0.04–0.08 mol m−2 s−1; NRMSE: 12.3–23.6%, MAE: 0.03–0.06 mol m−2 s−1) and exhibited excellent stability and accuracy. This research can play a significant role in achieving large-scale monitoring of crop growth status through UAV, enabling the real-time capture of winter wheat water deficit changes, and providing technical support for precision irrigation. Full article
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25 pages, 13007 KiB  
Article
Crop Classification from Drone Imagery Based on Lightweight Semantic Segmentation Methods
by Zuojun Zheng, Jianghao Yuan, Wei Yao, Hongxun Yao, Qingzhi Liu and Leifeng Guo
Remote Sens. 2024, 16(21), 4099; https://doi.org/10.3390/rs16214099 - 2 Nov 2024
Viewed by 1153
Abstract
Technological advances have dramatically improved precision agriculture, and accurate crop classification is a key aspect of precision agriculture (PA). The flexibility and real-time nature of UAVs have led them to become an important tool for acquiring agricultural data and enabling precise crop classification. [...] Read more.
Technological advances have dramatically improved precision agriculture, and accurate crop classification is a key aspect of precision agriculture (PA). The flexibility and real-time nature of UAVs have led them to become an important tool for acquiring agricultural data and enabling precise crop classification. Currently, crop identification relies heavily on complex high-precision models that often struggle to provide real-time performance. Research on lightweight models specifically for crop classification is also limited. In this paper, we propose a crop classification method based on UAV visible-light images based on PP-LiteSeg, a lightweight model proposed by Baidu. To improve the accuracy, a pyramid pooling module is designed in this paper, which integrates adaptive mean pooling and CSPC (Convolutional Spatial Pyramid Pooling) techniques to handle high-resolution features. In addition, a sparse self-attention mechanism is employed to help the model pay more attention to locally important semantic regions in the image. The combination of adaptive average pooling and the sparse self-attention mechanism can better handle different levels of contextual information. To train the model, a new dataset based on UAV visible-light images including nine categories such as rice, soybean, red bean, wheat, corn, poplar, etc., with a time span of two years was created for accurate crop classification. The experimental results show that the improved model outperforms other models in terms of accuracy and prediction performance, with a MIoU (mean intersection ratio joint) of 94.79%, which is 2.79% better than the original model. Based on the UAV RGB images demonstrated in this paper, the improved model achieves a better balance between real-time performance and accuracy. In conclusion, the method effectively utilizes UAV RGB data and lightweight deep semantic segmentation models to provide valuable insights for crop classification and UAV field monitoring. Full article
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26 pages, 5012 KiB  
Article
A Likelihood-Based Triangulation Method for Uncertainties in Through-Water Depth Mapping
by Mohamed Ali Ghannami, Sylvie Daniel, Guillaume Sicot and Isabelle Quidu
Remote Sens. 2024, 16(21), 4098; https://doi.org/10.3390/rs16214098 - 2 Nov 2024
Viewed by 695
Abstract
Coastal environments, which are crucial for economic and strategic reasons, heavily rely on accurate bathymetry for safe navigation and resource monitoring. Recent advancements in through-water photogrammetry have shown promise in mapping shallow waters efficiently. However, robust uncertainty modeling methods for these techniques, especially [...] Read more.
Coastal environments, which are crucial for economic and strategic reasons, heavily rely on accurate bathymetry for safe navigation and resource monitoring. Recent advancements in through-water photogrammetry have shown promise in mapping shallow waters efficiently. However, robust uncertainty modeling methods for these techniques, especially in challenging coastal environments, are lacking. This study introduces a novel likelihood-based approach for through-water photogrammetry, focusing on uncertainties associated with camera pose—a key factor affecting depth mapping accuracy. Our methodology incorporates probabilistic modeling and stereo-photogrammetric triangulation to provide realistic estimates of uncertainty in Water Column Depth (WCD) and Water–Air Interface (WAI) height. Using simulated scenarios for both drone and airborne surveys, we demonstrate that viewing geometry and camera pose quality significantly influence resulting uncertainties, often overshadowing the impact of depth itself. Our results reveal the superior performance of the likelihood ratio statistic in scenarios involving high attitude noise, high flight altitude, and complex viewing geometries. Notably, drone-based applications show particular promise, achieving decimeter-level WCD precision and WAI height estimations comparable to high-quality GNSS measurements when using large samples. These findings highlight the potential of drone-based surveys in producing more accurate bathymetric charts for shallow coastal waters. This research contributes to the refinement of uncertainty quantification in bathymetric charting and sets a foundation for future advancements in through-water surveying methodologies. Full article
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21 pages, 9598 KiB  
Article
Euler Kernel Mapping for Hyperspectral Image Clustering via Self-Paced Learning
by Fenggan Zhang, Hao Yan, Jianwei Zhao and Haojie Hu
Remote Sens. 2024, 16(21), 4097; https://doi.org/10.3390/rs16214097 - 2 Nov 2024
Viewed by 531
Abstract
Clustering, as a classical unsupervised artificial intelligence technology, is commonly employed for hyperspectral image clustering tasks. However, most existing clustering methods designed for remote sensing tasks aim to solve a non-convex objective function, which can be optimized iteratively, beginning with random initializations. Consequently, [...] Read more.
Clustering, as a classical unsupervised artificial intelligence technology, is commonly employed for hyperspectral image clustering tasks. However, most existing clustering methods designed for remote sensing tasks aim to solve a non-convex objective function, which can be optimized iteratively, beginning with random initializations. Consequently, during the learning phase of the clustering model, it may easily fall into bad local optimal solutions and finally hurt the clustering performance. Additionally, prevailing approaches often exhibit limitations in capturing the intricate structures inherent in hyperspectral images and are very sensitive to noise and outliers that widely exist in remote sensing data. To address these issues, we proposed a novel Euler kernel mapping for hyperspectral image clustering via self-paced learning (EKM-SPL). EKM-SPL first employs self-paced learning to learn the clustering model in a meaningful order by progressing samples from easy to complex, which can help to remove bad local optimal solutions. Secondly, a probabilistic soft weighting scheme is employed to measure complexity across the data sample, which makes the optimization process more reasonable. Thirdly, in order to more accurately characterize the intricate structure of hyperspectral images, Euler kernel mapping is used to convert the original data into a reproduced kernel Hilbert space, where the nonlinearly inseparable clusters may become linearly separable. Moreover, we innovatively integrate the coordinate descent technique into the optimization algorithm to circumvent the computational inefficiencies and information loss typically associated with conventional kernel methods. Extensive experiments conducted on classic benchmark hyperspectral image datasets illustrate the effectiveness and superiority of our proposed model. Full article
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29 pages, 50680 KiB  
Article
Relative Radiometric Correction Method Based on Temperature Normalization for Jilin1-KF02
by Shuai Huang, Song Yang, Yang Bai, Yingshan Sun, Bo Zou, Hongyu Wu, Lei Zhang, Jiangpeng Li and Xiaojie Yang
Remote Sens. 2024, 16(21), 4096; https://doi.org/10.3390/rs16214096 - 2 Nov 2024
Viewed by 699
Abstract
The optical remote sensors carried by the Jilin-1 KF02 series satellites have an imaging resolution better than 0.5 m and a width of 150 km. There are radiometric problems, such as stripe noise, vignetting, and inter-slice chromatic aberration, in their raw images. In [...] Read more.
The optical remote sensors carried by the Jilin-1 KF02 series satellites have an imaging resolution better than 0.5 m and a width of 150 km. There are radiometric problems, such as stripe noise, vignetting, and inter-slice chromatic aberration, in their raw images. In this paper, a relative radiometric correction method based on temperature normalization is proposed for the response characteristics of sensors and the structural characteristics of optical splicing of Jilin-1 KF02 series satellites cameras. Firstly, a model of temperature effect on sensor output is established to correct the variation of sensor response output digital number (DN) caused by temperature variation during imaging process, and the image is normalized to a uniform temperature reference. Then, the horizontal stripe noise of the image is eliminated by using the sensor scan line and dark pixel information, and the vertical stripe noise of the image is eliminated by using the method of on-orbit histogram statistics. Finally, the method of superposition compensation is used to correct the vignetting area at the edge of the image due to the lack of energy information received by the sensor so as to ensure the consistency of the image in color and image quality. The proposed method is verified by Jilin-1 KF02A on-orbit images. Experimental results show that the image response is uniform, the color is consistent, the average Streak Metrics (SM) is better than 0.1%, Root-Mean-Square Deviation of the Mean Line (RA) and Generalized Noise (GN) are better than 2%, Relative Average Spectral Error (RASE) and Relative Average Spectral Error (ERGAS) are greatly improved, which are better than 5% and 13, respectively, and the relative radiation quality is obviously improved after relative radiation correction. Full article
(This article belongs to the Special Issue Optical Remote Sensing Payloads, from Design to Flight Test)
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20 pages, 5632 KiB  
Article
A Frequency Selecting Method for High-Frequency Communication Based on Ionospheric Oblique Backscatter Sounding
by Chuqi Cai, Guobin Yang, Tongxin Liu and Chunhua Jiang
Remote Sens. 2024, 16(21), 4095; https://doi.org/10.3390/rs16214095 - 2 Nov 2024
Viewed by 813
Abstract
Ionospheric oblique backscatter sounding is an effective means of monitoring the ionosphere which can be used as a frequency selection system to serve HF communication and ensure its quality and stability. But how to obtain effective information from the oblique backscatter ionogram is [...] Read more.
Ionospheric oblique backscatter sounding is an effective means of monitoring the ionosphere which can be used as a frequency selection system to serve HF communication and ensure its quality and stability. But how to obtain effective information from the oblique backscatter ionogram is still a hot issue. Due to this situation, a frequency selecting method for HF communication based on ionospheric oblique backscatter sounding is proposed in this study. After obtaining the ionograms, pattern recognition is used to separate the vertical echoes and the oblique backscatter echoes. Next, the leading edge of the oblique backscatter echoes are extracted, and then a two-dimensional electron density profile can be reconstructed. Then, with the help of ray tracing, the usable frequency range can be estimated. Finally, according to the signal-to-noise ratio reflected by the ionograms, several optimal communication frequencies can be selected. In order to verify this method, oblique ionograms are obtained through oblique sounding experiments to evaluate its accuracy. The result indicates that the usable frequency range and the selected frequencies are in accordance with the echo of the oblique ionogram, so the practicability and accuracy of the method are validated. Eventually, the maximum usable frequencies (MUFs) obtained from oblique backscatter sounding are compared with the MUFs from the oblique sounding ionogram; its Mean Absolute Percentage Error (MAPE) is 7.8% and its root mean squared error (RMSE) is 1.34 MHz. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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15 pages, 4045 KiB  
Article
Impact of Site Conditions on Quercus robur and Quercus petraea Growth and Distribution Under Global Climate Change
by Monika Konatowska, Adam Młynarczyk, Paweł Rutkowski and Krzysztof Kujawa
Remote Sens. 2024, 16(21), 4094; https://doi.org/10.3390/rs16214094 - 2 Nov 2024
Viewed by 778
Abstract
Climate change has significant natural and economic implications, but its extent is particularly challenging to assess in forest management, a field which combines both of the previous aspects and requires the evaluation of the impact of climate change on tree species over a [...] Read more.
Climate change has significant natural and economic implications, but its extent is particularly challenging to assess in forest management, a field which combines both of the previous aspects and requires the evaluation of the impact of climate change on tree species over a 100-year timeframe. Oaks are among the tree species of significant natural and economic value in Europe. Therefore, the aim of this study was to analyze all oak stands in Poland and verify the hypothesis regarding differences between Quercus robur and Quercus petraea stands in terms of soil type, annual total precipitation, average annual air temperature, and the length of the growing season. Additionally, this study aimed to analyze the impact of these differences on the growth rates of both oak species and test whether climate change may affect oak stands. A database containing 195,241 tree stands, including different oak species with varying shares in the stand (from 10% to 100%), was analyzed. A particular emphasis was placed on Q. robur and Q. petraea. The results show that, although both oak species have a wide common range of occurrence, there are clear differences in their habitat preferences. Based on the ordinal regression analysis of selected oak stands, it was concluded that an increase in air temperature of 1 °C could impair the growth of Q. robur and slightly improve the growth of Q. petraea. This may indicate the possibility of expanding the geographic range of sessile oaks towards the east and northeast under warming climatic conditions, provided that appropriate moisture conditions are maintained. Full article
(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)
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16 pages, 4929 KiB  
Article
A Comparative Crash-Test of Manual and Semi-Automated Methods for Detecting Complex Submarine Morphologies
by Vasiliki Lioupa, Panagiotis Karsiotis, Riccardo Arosio, Thomas Hasiotis and Andrew J. Wheeler
Remote Sens. 2024, 16(21), 4093; https://doi.org/10.3390/rs16214093 - 2 Nov 2024
Viewed by 699
Abstract
Multibeam echosounders provide ideal data for the semi-automated seabed feature extraction and accurate morphometric measurements. In this study, bathymetric and raw backscatter data were initially used to manually delimit the reef morphologies found in an insular semi-enclosed gulf in the northern Aegean Sea [...] Read more.
Multibeam echosounders provide ideal data for the semi-automated seabed feature extraction and accurate morphometric measurements. In this study, bathymetric and raw backscatter data were initially used to manually delimit the reef morphologies found in an insular semi-enclosed gulf in the northern Aegean Sea (Gera Gulf, Lesvos Island, Greece). The complexity of this environment makes it an ideal area to “crash test” (test to the limit) and compare the results of the delineation methods. A large number of (more than 7000) small but prominent reefs were detected, which made manual mapping extremely time-consuming. Three semi-automated tools were also employed to map the reefs: the Benthic Terrain Modeler (BTM), Confined Morphologies Mapping (CoMMa), and eCognition Multiresolution Segmentation. BTM did not function properly with irregular reef footprints, but by modifying both the bathymetry and slope, the outcome was improved, producing accurate results that appeared to exceed the accuracy of manual mapping. CoMMa, a new GIS morphometric toolbox, was a “one-stop shop” that, besides generating satisfactory reef delineation results (i.e., detecting the same total reef area as the manual method), was also used to extract the morphometric characteristics of the polygons resulting from all the methods. Lastly, the Multiresolution Segmentation also gave satisfactory results with the highest precision. To compare the final maps with the distribution of the reefs, mapcurves were created to estimate the goodness-of-fit (GOF) with the Precision, Recall, and F1 Scores producing values higher than 0.78, suggesting a good detection accuracy for the semi-automated methods. The analysis reveals that the semi-automated methods provided more efficient results in comparison with the time-consuming manual mapping. Overall, for this case study, the modification of the bathymetry and slope enabled the results’ accuracy to be further enhanced. This study asserts that the use of semi-automated mapping is an effective method for delineating the geomorphometry of intricate relief and serves as a powerful tool for habitat mapping and decision-making. Full article
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17 pages, 4975 KiB  
Article
Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation
by Shui Yu, Jing Peng, Ming Ma, Hang Gong, Zongnan Li and Shaojie Ni
Remote Sens. 2024, 16(21), 4092; https://doi.org/10.3390/rs16214092 - 2 Nov 2024
Viewed by 1064
Abstract
The time of a satellite navigation system is primarily generated by the main control station of the ground system. Consequently, when ground stations fail, there is a risk to the continuous provision of time services to the equipment and users. Furthermore, the anticipated [...] Read more.
The time of a satellite navigation system is primarily generated by the main control station of the ground system. Consequently, when ground stations fail, there is a risk to the continuous provision of time services to the equipment and users. Furthermore, the anticipated launch of additional satellites will further strain the satellite–ground link. Next-generation satellite navigation systems will rely on time deviation measurements from inter-satellite links to independently establish and maintain a space-based time reference, enhancing the system’s reliability and robustness. The increasing number of low-Earth-orbit satellite navigation constellations provides ample resources for establishing a space-based time reference. However, this also introduces challenges, including extensive time scale computations, increased link noise, and low clock resource utilization. To address these issues, this paper proposes a Distributed Kalman Plus Weight (D-KPW) algorithm, which combines the benefits of Kalman filtering and the weighted average algorithm, balancing the performance with computational resources. Furthermore, an adaptive clock control algorithm, D-KPW (Control), is developed to account for both the short-term and long-term frequency stability of the time reference. The experimental results demonstrate that the frequency stability of the time reference established by the D-KPW (Control) algorithm reaches 7.40×1015 and 2.30×1015 for sampling intervals of 1000 s and 1,000,000 s, respectively, outperforming traditional algorithms such as ALGOS. The 20-day prediction error of the time reference is 1.55 ns. Compared to traditional algorithms such as AT1, ALGOS, Kalman, and D-KPW, the accuracy improves by 65%, 65%, 66%, and 67%, respectively. Full article
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22 pages, 6436 KiB  
Article
Spatiotemporal Evolution Analysis of Surface Deformation on the Beihei Highway Based on Multi-Source Remote Sensing Data
by Wei Shan, Guangchao Xu, Peijie Hou, Helong Du, Yating Du and Ying Guo
Remote Sens. 2024, 16(21), 4091; https://doi.org/10.3390/rs16214091 - 1 Nov 2024
Viewed by 538
Abstract
Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this [...] Read more.
Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this paper, the spatial and temporal evolution of surface deformations along the Beihei Highway was investigated by combining the SBAS-InSAR technique and the surface frost number model after considering the vegetation factor with multi-source remote sensing observation data. After comprehensively considering factors such as climate change, permafrost degradation, anthropogenic disturbance, and vegetation disturbance, the surface uneven settlement and landslide processes were analyzed in conjunction with site surveys and ground data. The results show that the average deformation rate is approximately −16 mm/a over the 22 km section of the study area. The rate of surface deformation on the pavement is related to topography, and the rate of surface subsidence on the pavement is more pronounced in areas with high topographic relief and a sunny aspect. Permafrost along the roads in the study area showed an insignificant degradation trend, and at landslides with large surface deformation, permafrost showed a significant degradation trend. Meteorological monitoring data indicate that the annual minimum mean temperature in the study area is increasing rapidly at a rate of 1.266 °C/10a during the last 40 years. The occurrence of landslides is associated with precipitation and freeze–thaw cycles. There are interactions between permafrost degradation, landslides, and vegetation degradation, and permafrost and vegetation are important influences on uneven surface settlement. Focusing on the spatial and temporal evolution process of surface deformation in the permafrost zone can help to deeply understand the mechanism of climate change impact on road hazards in the permafrost zone. Full article
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16 pages, 4971 KiB  
Technical Note
Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy
by Changda Zhu, Fubin Zhu, Cheng Li, Wenhao Lu, Zihan Fang, Zhaofu Li and Jianjun Pan
Remote Sens. 2024, 16(21), 4090; https://doi.org/10.3390/rs16214090 - 1 Nov 2024
Viewed by 582
Abstract
Topography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on [...] Read more.
Topography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on soil mapping uses landscape types generated by existing methods directly as environmental covariates, and there are few landscape classification methods specifically oriented toward soil surveys. There is rarely any research on landform classification using relative slope position (RSP) and elevation. Therefore, we designed a landform classification method based on RSP and elevation, Terrainforms (TF), and combined the landform type with land use type to construct soil–landscape units for soil type and attribute spatial prediction. In this study, two commonly used landform classification methods, Geomorphons and Landforms, were also used to compare with this design method. It was found that the constructed soil–landscape units had a high consistency with the soil spatial distribution. The landform types based on RSP and elevation obtained the second-highest prediction accuracy in both soil type and soil organic carbon (SOC), and the constructed soil–landscape types obtained the highest prediction accuracy. The results show that the landform classification method based on RSP and elevation is not easily limited by the analysis scale, and is an efficient and accurate landform classification method. The TF landform type and its constructed soil–landscape types can be used as an important environmental variable in soil prediction and sampling, which can provide some guidance and reference for landform classification and digital soil mapping. Full article
(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling)
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