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Remote Sens., Volume 16, Issue 22 (November-2 2024) – 223 articles

Cover Story (view full-size image): The use of near-Earth space has dramatically increased in recent decades, leading to thousands of active and inactive satellites and significant space debris. To monitor this environment, sensitive high-precision observation systems are essential for detecting, tracking, and imaging space objects. TIRA, with its 34 m antenna, is one of the largest space observation radars, contributing to space domain awareness. A new fully polarimetric imaging radar with enhanced geometrical and radiometric resolution is being developed. Additionally, the tracking radar is being upgraded with a novel modular structure and open design, enabling flexible radar modes and precise tracking for improved space domain awareness. The upgraded TIRA system will start a new era for radar space observation in Europe. View this paper
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22 pages, 7129 KiB  
Article
Urban Multi-Scenario Land Use Optimization Simulation Considering Local Climate Zones
by Jie Chen, Zikun Dong, Ruijie Shi, Geng Sun, Ya Guo, Zhuopeng Peng, Min Deng and Kaiqi Chen
Remote Sens. 2024, 16(22), 4342; https://doi.org/10.3390/rs16224342 - 20 Nov 2024
Viewed by 438
Abstract
The urban heat island (UHI) effect, a significant environmental challenge within the global urbanization process, poses severe threats to human health, ecological security, and life safety while also impacting the achievement of the United Nations Sustainable Development Goals. This study proposes a multi-scenario [...] Read more.
The urban heat island (UHI) effect, a significant environmental challenge within the global urbanization process, poses severe threats to human health, ecological security, and life safety while also impacting the achievement of the United Nations Sustainable Development Goals. This study proposes a multi-scenario optimization method for urban thermal environments based on local climate zones (LCZs) in Changsha City. The research employs a genetic algorithm to optimize the LCZ quantity structure in order to improve the urban temperature environment. Subsequently, the optimized quantity structure is integrated with the future land use simulation (FLUS) model under multi-scenario constraints to achieve optimal spatial distribution of LCZs, providing scientific guidance for urban planning decision-makers. Results demonstrate that the LCZ-based optimization method can effectively regulate the urban thermal environment and maintain a suitable urban temperature range, offering both theoretical foundation and practical guidance for mitigating UHI effects. Full article
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28 pages, 12380 KiB  
Article
Characterization of CYGNSS Ocean Surface Wind Speed Products
by Christopher Ruf, Mohammad Al-Khaldi, Shakeel Asharaf, Rajeswari Balasubramaniam, Darren McKague, Daniel Pascual, Anthony Russel, Dorina Twigg and April Warnock
Remote Sens. 2024, 16(22), 4341; https://doi.org/10.3390/rs16224341 - 20 Nov 2024
Viewed by 401
Abstract
Since its launch in 2016, a number of wind speed retrieval algorithms have been developed for the NASA CYGNSS satellite observations. We assess their accuracy and precision and characterize the dependence of their performance on environmental factors. The dependence of retrieval uncertainty on [...] Read more.
Since its launch in 2016, a number of wind speed retrieval algorithms have been developed for the NASA CYGNSS satellite observations. We assess their accuracy and precision and characterize the dependence of their performance on environmental factors. The dependence of retrieval uncertainty on the wind speed itself is considered. The triple colocation method of validation is used to correct for the quality of the reference wind speed products with which CYGNSS is compared. The dependence of retrieval performance on sea state is also considered, with particular attention being paid to the long wave portion of the surface roughness spectrum that is less closely coupled to the instantaneous local wind speed than the capillary wave portion of the spectrum. The dependence is found to be significant, and the efficacy of the approaches taken to account for it is examined. The dependence of retrieval accuracy on wind speed persistence (the change in wind speed prior to a measurement) is also characterized and is found to be significant when winds have increased markedly in the ~2 h preceding an observation. Full article
(This article belongs to the Section Ocean Remote Sensing)
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21 pages, 2229 KiB  
Article
LH-YOLO: A Lightweight and High-Precision SAR Ship Detection Model Based on the Improved YOLOv8n
by Qi Cao, Hang Chen, Shang Wang, Yongqiang Wang, Haisheng Fu, Zhenjiao Chen and Feng Liang
Remote Sens. 2024, 16(22), 4340; https://doi.org/10.3390/rs16224340 - 20 Nov 2024
Viewed by 552
Abstract
Synthetic aperture radar is widely applied to ship detection due to generating high-resolution images under diverse weather conditions and its penetration capabilities, making SAR images a valuable data source. However, detecting multi-scale ship targets in complex backgrounds leads to issues of false positives [...] Read more.
Synthetic aperture radar is widely applied to ship detection due to generating high-resolution images under diverse weather conditions and its penetration capabilities, making SAR images a valuable data source. However, detecting multi-scale ship targets in complex backgrounds leads to issues of false positives and missed detections, posing challenges for lightweight and high-precision algorithms. There is an urgent need to improve accuracy of algorithms and their deployability. This paper introduces LH-YOLO, a YOLOv8n-based, lightweight, and high-precision SAR ship detection model. We propose a lightweight backbone network, StarNet-nano, and employ element-wise multiplication to construct a lightweight feature extraction module, LFE-C2f, for the neck of LH-YOLO. Additionally, a reused and shared convolutional detection (RSCD) head is designed using a weight sharing mechanism. These enhancements significantly reduce model size and computational demands while maintaining high precision. LH-YOLO features only 1.862 M parameters, representing a 38.1% reduction compared to YOLOv8n. It exhibits a 23.8% reduction in computational load while achieving a mAP50 of 96.6% on the HRSID dataset, which is 1.4% higher than YOLOv8n. Furthermore, it demonstrates strong generalization on the SAR-Ship-Dataset with a mAP50 of 93.8%, surpassing YOLOv8n by 0.7%. LH-YOLO is well-suited for environments with limited resources, such as embedded systems and edge computing platforms. Full article
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24 pages, 3462 KiB  
Article
Underutilized Feature Extraction Methods for Burn Severity Mapping: A Comprehensive Evaluation
by Linh Nguyen Van and Giha Lee
Remote Sens. 2024, 16(22), 4339; https://doi.org/10.3390/rs16224339 - 20 Nov 2024
Viewed by 477
Abstract
Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing [...] Read more.
Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing classification accuracy. While principal component analysis (PCA) is commonly used to address this issue, its effectiveness relative to other feature extraction (FE) methods in BSM remains underexplored. This study aims to enhance ML classifier accuracy in BSM by evaluating various FE techniques that mitigate multicollinearity among vegetation indices. Using composite burn index (CBI) data from the 2014 Carlton Complex fire in the United States as a case study, we extracted 118 vegetation indices from seven Landsat-8 spectral bands. We applied and compared 13 different FE techniques—including linear and nonlinear methods such as PCA, t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), Isomap, uniform manifold approximation and projection (UMAP), factor analysis (FA), independent component analysis (ICA), multidimensional scaling (MDS), truncated singular value decomposition (TSVD), non-negative matrix factorization (NMF), locally linear embedding (LLE), spectral embedding (SE), and neighborhood components analysis (NCA). The performance of these techniques was benchmarked against six ML classifiers to determine their effectiveness in improving BSM accuracy. Our results show that alternative FE techniques can outperform PCA, improving classification accuracy and computational efficiency. Techniques like LDA and NCA effectively capture nonlinear relationships critical for accurate BSM. The study contributes to the existing literature by providing a comprehensive comparison of FE methods, highlighting the potential benefits of underutilized techniques in BSM. Full article
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37 pages, 19323 KiB  
Article
Impacts of Storm “Zyprian” on Middle and Upper Atmosphere Observed from Central European Stations
by Petra Koucká Knížová, Kateřina Potužníková, Kateřina Podolská, Tereza Šindelářová, Tamás Bozóki, Martin Setvák, Marcell Pásztor, Csilla Szárnya, Zbyšek Mošna, Daniel Kouba, Jaroslav Chum, Petr Zacharov, Attila Buzás, Hana Hanzlíková, Michal Kozubek, Dalia Burešová, István Bozsó, Kitti A. Berényi and Veronika Barta
Remote Sens. 2024, 16(22), 4338; https://doi.org/10.3390/rs16224338 - 20 Nov 2024
Viewed by 386
Abstract
Mesoscale convective systems are effective sources of atmospheric disturbances that can reach ionospheric heights and significantly alter atmospheric and ionospheric conditions. Convective systems can affect the Earth’s atmosphere on a continental scale and up to F-layer heights. Extratropical cyclone “Zyprian” occurred at the [...] Read more.
Mesoscale convective systems are effective sources of atmospheric disturbances that can reach ionospheric heights and significantly alter atmospheric and ionospheric conditions. Convective systems can affect the Earth’s atmosphere on a continental scale and up to F-layer heights. Extratropical cyclone “Zyprian” occurred at the beginning of July, 2021 and dominated weather over the whole of Europe. An extensive cold front associated with “Zyprian” moved from the western part to the eastern part of Europe, followed by ground-level convergence and the formation of organized convective thunderstorm systems. Torrential rains in the Czech Republic have caused a great deal of damage and casualties. Storm-related signatures were developed in ground microbarograph measurements of infrasound and gravity waves. Within the stratosphere, a shift of the polar jet stream and increase in specific humidity related to the storm system were observed. At the ionospheric heights, irregular stratification and radio wave reflection plane undulation were observed. An increase in wave-like activity was detected based on ionograms and narrowband very-low-frequency (VLF) data. On directograms and SKYmaps (both products of digisonde measurements), strong and rapid changes in the horizontal plasma motion were recorded. However, no prevailing plasma motion direction was identified within the F-layer. Increased variability within the ionosphere is attributed mainly to the “Zyprian” cyclone as it developed during low geomagnetic activity and stable solar forcing. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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21 pages, 10992 KiB  
Article
Radiometric Cross-Calibration of HJ-2A/CCD3 Using the Random Forest Algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSI
by Xiang Zhou, Yidan Chen, Yong Xie, Jie Han and Wen Shao
Remote Sens. 2024, 16(22), 4337; https://doi.org/10.3390/rs16224337 - 20 Nov 2024
Viewed by 460
Abstract
In the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross–Li model in current research. However, the Ross–Li BRDF [...] Read more.
In the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross–Li model in current research. However, the Ross–Li BRDF model is based on the linear relationship between the kernel models and is unable to take into account the nonlinear relationship between them. Furthermore, when using SBAF to account for spectral difference, a radiative transfer model is often used, but it requires many parameters to be set, which may introduce more errors and reduce the calibration accuracy. To address these issues, the random forest algorithm and a spectral interpolation convolution method using the Sentinel-2/multispectral instrument (MSI) are proposed in this study, in which the HuanJing-2A (HJ-2A)/charge-coupled device (CCD3) sensor is taken as an example, and the Dunhuang radiometric calibration site (DRCS) is used as a radiometric delivery platform. Firstly, a BRDF model by using the random forest algorithm of the DRCS is constructed using time-series MODIS images, which corrects the viewing geometry difference. Secondly, the BRDF correction coefficients, MSI reflectance, and relative spectral responses (RSRs) of CCD3 are used to correct the spectral differences. Finally, with the validation results, the maximum relative error between the calibration results of the proposed method and the official calibration coefficients (OCCs) published by the China Centre for Resources Satellite Data and Application (CRESDA) is 3.38%. When tested using the Baotou sandy site, the proposed method is better than the OCCs of the average relative errors calculated for all the bands except for the near-infrared (NIR) band, which has a larger error. Additionally, the effects of the light-matching method and the radiative transfer method, different approaches to constructing the BRDF model, using SBAF to account for spectral differences, different BRDF sources, as well as the imprecise viewing geometrical parameters, spectral interpolation method, and geometric positioning error, on the calibration results are analyzed. Results indicate that the cross-calibration coefficients obtained using the random forest algorithm and the proposed spectral interpolation method are more applicable to the CCD3; thus, they also account for the nonlinear relationships between the kernel models and reduce the error due to the radiative transfer model. The total uncertainty of the proposed method in all bands is less than 5.16%. Full article
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24 pages, 33437 KiB  
Article
Global Assessment of Mesoscale Eddies with TOEddies: Comparison Between Multiple Datasets and Colocation with In Situ Measurements
by Artemis Ioannou, Lionel Guez, Rémi Laxenaire and Sabrina Speich
Remote Sens. 2024, 16(22), 4336; https://doi.org/10.3390/rs16224336 - 20 Nov 2024
Viewed by 411
Abstract
The present study introduces a comprehensive, open-access atlas of mesoscale eddies in the global ocean, as identified and tracked by the TOEddies algorithm implemented on a global scale. Unlike existing atlases, TOEddies detects eddies directly from absolute dynamic topography (ADT) without spatial filtering, [...] Read more.
The present study introduces a comprehensive, open-access atlas of mesoscale eddies in the global ocean, as identified and tracked by the TOEddies algorithm implemented on a global scale. Unlike existing atlases, TOEddies detects eddies directly from absolute dynamic topography (ADT) without spatial filtering, preserving the natural spatial variability and enabling precise, high-resolution tracking of eddy dynamics. This dataset provides daily information on eddy characteristics, such as size, intensity, and polarity, over a 30-year period (1993–2023), capturing complex eddy interactions, including splitting and merging events that often produce networks of interconnected eddies. This unique approach challenges the traditional single-trajectory perspective, offering a nuanced view of eddy life cycles as dynamically linked trajectories. In addition to traditional metrics, TOEddies identifies both the eddy core (characterized by maximum azimuthal velocity) and the outer boundary, offering a detailed representation of eddy structure and enabling precise comparisons with in situ data. To demonstrate its value, we present a statistical overview of eddy characteristics and spatial distributions, including generation, disappearance, and merging/splitting events, alongside a comparative analysis with existing global eddy datasets. Among the multi-year observations, TOEddies captures coherent, long-lived eddies with lifetimes exceeding 1.5 years, while highlighting significant differences in the dynamic properties and spatial patterns across datasets. Furthermore, this study integrates TOEddies with 23 years of colocalized Argo profile data (2000–2023), allowing for a novel examination of eddy-induced subsurface variability and the role of mesoscale eddies in the transport of global ocean heat and biogeochemical properties. This atlas aims to be a valuable resource for the oceanographic community, providing an open dataset that can support diverse applications in ocean dynamics, climate research, and marine resource management. Full article
(This article belongs to the Special Issue Recent Advances on Oceanic Mesoscale Eddies II)
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2 pages, 534 KiB  
Correction
Correction: Wolswijk et al. Can Mangrove Silviculture Be Carbon Neutral? Remote Sens. 2022, 14, 2920
by Giovanna Wolswijk, Africa Barrios Trullols, Jean Hugé, Viviana Otero, Behara Satyanarayana, Richard Lucas and Farid Dahdouh-Guebas
Remote Sens. 2024, 16(22), 4335; https://doi.org/10.3390/rs16224335 - 20 Nov 2024
Viewed by 255
Abstract
In the original publication [...] Full article
(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)
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20 pages, 3319 KiB  
Article
The Performance of GPM IMERG Product Validated on Hourly Observations over Land Areas of Northern Hemisphere
by Pengfei Lv and Guocan Wu
Remote Sens. 2024, 16(22), 4334; https://doi.org/10.3390/rs16224334 - 20 Nov 2024
Viewed by 456
Abstract
The integrated multi-satellite retrievals for the global precipitation measurement (IMERG) data, which is the latest generation of multi-satellite fusion inversion precipitation product provided by the Global Precipitation Measurement (GPM) mission, has been widely applied in hydrological research and applications. However, the quality of [...] Read more.
The integrated multi-satellite retrievals for the global precipitation measurement (IMERG) data, which is the latest generation of multi-satellite fusion inversion precipitation product provided by the Global Precipitation Measurement (GPM) mission, has been widely applied in hydrological research and applications. However, the quality of IMERG data needs to be validated, as this technology is essentially an indirect way to obtain precipitation information. This study evaluated the performance of IMERG final run (version 6.0) products from 2001 to 2020, using three sets of gauge-derived precipitation data obtained from the Integrated Surface Database, China Meteorological Administration, and U.S. Climate Reference Network. The results showed a basic consistency in the spatial pattern of annual precipitation total between IMERG data and gauge observations. The highest and lowest correlations between IMERG data and gauge observations were obtained in North Asia (0.373, p < 0.05) and Europe (0.308, p < 0.05), respectively. IMERG data could capture the bimodal structure of diurnal precipitation in South Asia but overestimates a small variation in North Asia. The disparity was attributed to the frequency overestimation but intensity underestimation in satellite inversion, since small raindrops may evaporate before arriving at the ground but can be identified by remote sensors. IMERG data also showed similar patterns of interannual precipitation variability to gauge observation, while overestimating the proportion of annual precipitation hours by 2.5% in North America, and 2.0% in North Asia. These findings deepen our understanding of the capabilities of the IMERG product to estimate precipitation at the hourly scale, and can be further applied to improve satellite precipitation retrieval. Full article
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25 pages, 5692 KiB  
Article
Initial Design for Next-Generation BeiDou Integrity Subsystem: Space–Ground Integrated Integrity Monitoring
by Weiguang Gao, Lei Chen, Feiren Lv, Xingqun Zhan, Lin Chen, Yuqi Liu, Yongshan Dai and Yundi Jin
Remote Sens. 2024, 16(22), 4333; https://doi.org/10.3390/rs16224333 - 20 Nov 2024
Viewed by 475
Abstract
It is essential to provide high-integrity navigation information for safety-critical applications. Global navigation satellite systems (GNSSs) play an important role in these applications because they can provide global, high-accuracy, all-weather navigation services. Therefore, it has been a hot topic to improve GNSS integrity [...] Read more.
It is essential to provide high-integrity navigation information for safety-critical applications. Global navigation satellite systems (GNSSs) play an important role in these applications because they can provide global, high-accuracy, all-weather navigation services. Therefore, it has been a hot topic to improve GNSS integrity performance. This paper focuses on an initial proposal of the next-generation BeiDou Navigation Satellite System (BDS) integrity subsystem, with the aim of providing high-quality and global integrity services for the BDS. This paper first reviews the current status of the third-generation BDS integrity service. Following this, this paper proposes a space–ground integrated integrity monitoring design for the BDS that integrates the traditional ground-based integrity monitoring method, the advanced satellite autonomous integrity monitoring (A-SAIM) method, and the augmentation from low-earth-orbit (LEO) satellites. Specifically, this work offers an initial design of the A-SAIM method, which considers both single-satellite autonomous integrity monitoring and multi-satellite joint integrity monitoring. In addition, this work describes two different ways to augment BDS integrity with LEO satellites, i.e., (a) LEO satellites act as space monitoring stations and (b) LEO satellites act as navigation satellites. Simulations are carried out to validate the proposed design using CAT-I operation in civil aviation as an example. Simulation results indicate the effectiveness of the proposed design. In addition, simulation results suggest that if the fault probability of LEO satellites is worse than 1 × 10−4, LEO satellites can contribute more to BDS integrity performance improvement by acting as space monitoring stations; otherwise, it would be better to employ LEO satellites to broadcast navigation signals. The results also suggest that after taking LEO satellites into account, the global coverage of CAT-I can be potentially improved from 67% to 99%. This work is beneficial to the design of the next-generation BDS integrity subsystem. Full article
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24 pages, 21738 KiB  
Article
New Method to Correct Vegetation Bias in a Copernicus Digital Elevation Model to Improve Flow Path Delineation
by Gabriel Thomé Brochado and Camilo Daleles Rennó
Remote Sens. 2024, 16(22), 4332; https://doi.org/10.3390/rs16224332 - 20 Nov 2024
Viewed by 439
Abstract
Digital elevation models (DEM) are widely used in many hydrologic applications, providing key information about the topography, which is a major driver of water flow in a landscape. Several open access DEMs with near-global coverage are currently available, however, they represent the elevation [...] Read more.
Digital elevation models (DEM) are widely used in many hydrologic applications, providing key information about the topography, which is a major driver of water flow in a landscape. Several open access DEMs with near-global coverage are currently available, however, they represent the elevation of the earth’s surface including all its elements, such as vegetation cover and buildings. These features introduce a positive elevation bias that can skew the water flow paths, impacting the extraction of hydrological features and the accuracy of hydrodynamic models. Many attempts have been made to reduce the effects of this bias over the years, leading to the generation of improved datasets based on the original global DEMs, such as MERIT DEM and, more recently, FABDEM. However, even after these corrections, the remaining bias still affects flow path delineation in a significant way. Aiming to improve on this aspect, a new vegetation bias correction method is proposed in this work. The method consists of subtracting from the Copernicus DEM elevations their respective forest height but adjusted by correction factors to compensate for the partial penetration of the SAR pulses into the vegetation cover during the Copernicus DEM acquisition process. These factors were calculated by a new approach where the slope around the pixels at the borders of each vegetation patch were analyzed. The forest height was obtained from a global dataset developed for the year 2019. Moreover, to avoid temporal vegetation cover mismatch between the DEM and the forest height dataset, we introduced a process where the latter is automatically adjusted to best match the Copernicus acquisition year. The correction method was applied for regions with different forest cover percentages and topographic characteristics, and the result was compared to the original Copernicus DEM and FABDEM, which was used as a benchmark for vegetation bias correction. The comparison method was hydrology-based, using drainage networks obtained from topographic maps as reference. The new corrected DEM showed significant improvements over both the Copernicus DEM and FABDEM in all tested scenarios. Moreover, a qualitative comparison of these DEMs was also performed through exhaustive visual analysis, corroborating these findings. These results suggest that the use of this new vegetation bias correction method has the potential to improve DEM-based hydrological applications worldwide. Full article
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17 pages, 4491 KiB  
Article
Height Measurement for Meter-Wave MIMO Radar Based on Sparse Array Under Multipath Interference
by Cong Qin, Qin Zhang, Guimei Zheng, Gangsheng Zhang and Shiqiang Wang
Remote Sens. 2024, 16(22), 4331; https://doi.org/10.3390/rs16224331 - 20 Nov 2024
Viewed by 396
Abstract
For meter-wave multiple-input multiple-output (MIMO) radar, the multipath of target echoes may cause severe errors in height measurement, especially in the case of complex terrain where terrain fluctuation, ground inclination, and multiple reflection points exist. Inspired by a sparse array with greater degrees [...] Read more.
For meter-wave multiple-input multiple-output (MIMO) radar, the multipath of target echoes may cause severe errors in height measurement, especially in the case of complex terrain where terrain fluctuation, ground inclination, and multiple reflection points exist. Inspired by a sparse array with greater degrees of freedom and low mutual coupling, a height measurement method based on a sparse array is proposed. First, a practical signal model of MIMO radar based on a sparse array is established. Then, the modified multiple signal classification (MUSIC) and maximum likelihood (ML) estimation algorithms based on two classical sparse arrays (coprime array and nested array) are proposed. To reduce the complexity of the algorithm, a real-valued processing algorithm for generalized MUSIC (GMUSIC) and maximum likelihood is proposed, and a reduced dimension matrix is introduced into the real-valued processing algorithm to further reduce computation complexity. Finally, sufficient simulation results are provided to illustrate the effectiveness and superiority of the proposed technique. The simulation results show that the height measurement accuracy can be efficiently improved by using our proposed technique for both simple and complex terrain. Full article
(This article belongs to the Special Issue Array and Signal Processing for Radar)
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27 pages, 10743 KiB  
Article
Comparative Validation and Misclassification Diagnosis of 30-Meter Land Cover Datasets in China
by Xiaolin Xu, Dan Li, Hongxi Liu, Guang Zhao, Baoshan Cui, Yujun Yi, Wei Yang and Jizeng Du
Remote Sens. 2024, 16(22), 4330; https://doi.org/10.3390/rs16224330 - 20 Nov 2024
Viewed by 525
Abstract
Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land cover and their effects on carbon storage, land–atmosphere energy [...] Read more.
Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land cover and their effects on carbon storage, land–atmosphere energy balance, and water cycle processes. However, current data products use different classification methods, resulting in significant classification inconsistency and triggering serious disagreements among related studies. Here, we compared four mainstream land cover products in China, namely GLC_FCS30, CLCD, Globeland30, and CNLUCC. The result shows that only 50.34% of the classification results were consistent across the four datasets. The differences between pairs of datasets ranged from 21.10% to 37.53%. Importantly, most inconsistency occurs in transitional zones among land cover types sensitive to climate change and human activities. Based on the accuracy evaluation, CLCD is the most accurate land cover product, with an overall accuracy reaching 86.98 ± 0.76%, followed by CNLUCC (81.38 ± 0.87%) and GLC_FCS30 (77.83 ± 0.80%). Globeland30 had the lowest accuracy (75.24 ± 0.91%), primarily due to misclassification between croplands and forests. Misclassification diagnoses revealed that vegetation-related spectral confusion among land cover types contributed significantly to misclassifications, followed by slope, cloud cover, and landscape fragmentation, which affected satellite observation angles, data availability, and mixed pixels. Automated classification methods using the random forest algorithm can perform better than those that depend on traditional human–machine interactive interpretation or object-based approaches. However, their classification accuracy depends more on selecting training samples and feature variables. Full article
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25 pages, 12054 KiB  
Article
Towards 3D Reconstruction of Multi-Shaped Tunnels Utilizing Mobile Laser Scanning Data
by Xuan Ding, Shen Chen, Mu Duan, Jinchang Shan, Chao Liu and Chuli Hu
Remote Sens. 2024, 16(22), 4329; https://doi.org/10.3390/rs16224329 - 20 Nov 2024
Viewed by 414
Abstract
Using digital twin models of tunnels has become critical to their efficient maintenance and management. A high-precision 3D tunnel model is the prerequisite for a successful digital twin model of tunnel applications. However, constructing high-precision 3D tunnel models with high-quality textures and structural [...] Read more.
Using digital twin models of tunnels has become critical to their efficient maintenance and management. A high-precision 3D tunnel model is the prerequisite for a successful digital twin model of tunnel applications. However, constructing high-precision 3D tunnel models with high-quality textures and structural integrity based on mobile laser scanning data remains a challenge, particularly for tunnels of different shapes. This study addresses this problem by developing a novel method for the 3D reconstruction of multi-shaped tunnels based on mobile laser scanning data. This method does not require any predefined mathematical models or projection parameters to convert point clouds into 2D intensity images that conform to the geometric features of tunnel linings. This method also improves the accuracy of 3D tunnel mesh models by applying an adaptive threshold approach that reduces the number of pseudo-surfaces generated during the Poisson surface reconstruction of tunnels. This method was experimentally verified by conducting 3D reconstruction tasks involving tunnel point clouds of four different shapes. The superiority of this method was further confirmed through qualitative and quantitative comparisons with related approaches. By automatically and efficiently constructing a high-precision 3D tunnel model, the proposed method offers an important model foundation for digital twin engineering and a valuable reference for future tunnel model construction projects. Full article
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17 pages, 4466 KiB  
Article
Flooded Infrastructure Change Detection in Deeply Supervised Networks Based on Multi-Attention-Constrained Multi-Scale Feature Fusion
by Gang Qin, Shixin Wang, Futao Wang, Suju Li, Zhenqing Wang, Jinfeng Zhu, Ming Liu, Changjun Gu and Qing Zhao
Remote Sens. 2024, 16(22), 4328; https://doi.org/10.3390/rs16224328 - 20 Nov 2024
Viewed by 311
Abstract
Flood disasters are frequent, sudden, and have significant chain effects, seriously damaging infrastructure. Remote sensing images provide a means for timely flood emergency monitoring. When floods occur, emergency management agencies need to respond quickly and assess the damage. However, manual evaluation takes a [...] Read more.
Flood disasters are frequent, sudden, and have significant chain effects, seriously damaging infrastructure. Remote sensing images provide a means for timely flood emergency monitoring. When floods occur, emergency management agencies need to respond quickly and assess the damage. However, manual evaluation takes a significant amount of time; in current, commercial applications, the post-disaster flood vector range is used to directly overlay land cover data. On the one hand, land cover data are not updated in time, resulting in the misjudgment of disaster losses; on the other hand, since buildings block floods, the above methods cannot detect flooded buildings. Automated change-detection methods can effectively alleviate the above problems. However, the ability of change-detection structures and deep learning models for flooding to characterize flooded buildings and roads is unclear. This study specifically evaluated the performance of different change-detection structures and different deep learning models for the change detection of flooded buildings and roads in very-high-resolution remote sensing images. At the same time, a plug-and-play, multi-attention-constrained, deeply supervised high-dimensional and low-dimensional multi-scale feature fusion (MSFF) module is proposed. The MSFF module was extended to different deep learning models. Experimental results showed that the embedded MSFF performs better than the baseline model, demonstrating that MSFF can be used as a general multi-scale feature fusion component. After FloodedCDNet introduced MSFF, the detection accuracy of flooded buildings and roads changed after the data augmentation reached a maximum of 69.1% MIoU. This demonstrates its effectiveness and robustness in identifying change regions and categories from very-high-resolution remote sensing images. Full article
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19 pages, 3711 KiB  
Article
Secchi Depth Retrieval in Oligotrophic to Eutrophic Chilean Lakes Using Open Access Satellite-Derived Products
by Daniela Rivera-Ruiz, José Luis Arumí, Mario Lillo-Saavedra, Carlos Esse, Patricia Arancibia-Ávila, Roberto Urrutia, Marcelo Portuguez-Maurtua and Igor Ogashawara
Remote Sens. 2024, 16(22), 4327; https://doi.org/10.3390/rs16224327 - 20 Nov 2024
Viewed by 945
Abstract
The application of the Multispectral Instrument (MSI) aboard Sentinel-2A/B constellation for assessing water quality in Chilean lakes represents an emerging area of research, particularly for the environmental monitoring of optically complex water bodies. Similarly, atmospheric correction processors applied to aquatic environments, such as [...] Read more.
The application of the Multispectral Instrument (MSI) aboard Sentinel-2A/B constellation for assessing water quality in Chilean lakes represents an emerging area of research, particularly for the environmental monitoring of optically complex water bodies. Similarly, atmospheric correction processors applied to aquatic environments, such as the Case 2 Networks (C2RCC-Nets), are notably underrepresented. This study evaluates the capability of C2RCC-Nets using different neural networks—Case-2 Regional/Coast Color (C2RCC), C2X-Extreme (C2X), and C2X-Complex (C2XC)—to estimate Secchi depth in Lake Lanalhue (eutrophic), Lake Villarrica (oligo-mesotrophic), and Lake Panguipulli (oligotrophic). The evaluation used different statistical methods such as Spearman’s correlation and normalized error metrics (nRMSE, nMAE, and nbias) to assess the agreement between satellite-derived data and in situ measurements. C2XC demonstrated the best fit for Lake Lanalhue, with an nRMSE = 33.13%, nMAE = 23.51%, and nbias = 8.57%, in relation to the median ground truth values. In Lake Villarrica, the C2XC neural network displayed a moderate correlation (rs = 0.618) and error metrics, with an nRMSE of 24.67% and nMAE of 20.67%, with an nbias of 4.21%. In the oligotrophic Lake Panguipulli, no relationship was observed between estimated and measured values, which could be related to the fact that the selected neural networks were developed for very case 2 waters. These findings highlight the need for methodological advancements in processing satellite-derived water quality products for Chile’s optical water types, particularly for very clear waters. Nonetheless, this study underscores the need for model-specific calibration of C2RCC-Nets, as lakes with different optical water types and trophic states may require tailored training ranges for inherent optical properties. Full article
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23 pages, 8730 KiB  
Article
Three-Dimensional Surface Motion Displacement Estimation of the Muz Taw Glacier, Sawir Mountains
by Yanqiang Wang, Jun Zhao, Zhongqin Li, Yanjie Yang and Jialiang Liu
Remote Sens. 2024, 16(22), 4326; https://doi.org/10.3390/rs16224326 - 20 Nov 2024
Viewed by 505
Abstract
Research on glacier movement is helpful for comprehensively understanding the laws behind this movement and can also provide a scientific basis for glacier change and analyses of the dynamic mechanisms driving atmospheric circulation and glacier evolution. Sentinel-1 series data were used in this [...] Read more.
Research on glacier movement is helpful for comprehensively understanding the laws behind this movement and can also provide a scientific basis for glacier change and analyses of the dynamic mechanisms driving atmospheric circulation and glacier evolution. Sentinel-1 series data were used in this study to retrieve the three-dimensional (3D) surface motion displacement of the Muz Taw glacier from 22 August 2017, to 17 August 2018. The inversion method of the 3D surface motion displacement of glaciers has been verified by the field measurement data from Urumqi Glacier No. 1. The effects of topographic factors, glacier thickness, and climate factors on the 3D surface displacement of the Muz Taw glacier are discussed in this paper. The results show that, during the study period, the total 3D displacement of the Muz Taw glacier was between 0.52 and 13.19 m, the eastward displacement was 4.27 m, the northward displacement was 4.07 m, and the horizontal displacement was 5.90 m. Areas of high displacement were mainly distributed in the main glacier at altitudes of 3300–3350 and 3450–3600 m. There were significant differences in the total 3D displacement of the Muz Taw glacier in each season. The displacement was larger in summer, followed by spring, and it was similar in autumn and winter. The total 3D displacement during the whole study period and in spring, summer, and autumn fluctuated greatly along the glacier centerline, while the change in winter was relatively gentle. Various factors such as topography, glacier thickness, and climate had different influences on the surface motion displacement of the Muz Taw glacier. Full article
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20 pages, 5213 KiB  
Article
Radar Moving Target Detection Based on Small-Sample Transfer Learning and Attention Mechanism
by Jiang Zhu, Cai Wen, Chongdi Duan, Weiwei Wang and Xiaochao Yang
Remote Sens. 2024, 16(22), 4325; https://doi.org/10.3390/rs16224325 - 20 Nov 2024
Viewed by 426
Abstract
Moving target detection is one of the most important tasks of radar systems. The clutter echo received by radar is usually strong and heterogeneous when the radar works in a complex terrain environment, resulting in performance degradation in moving target detection. Utilizing prior [...] Read more.
Moving target detection is one of the most important tasks of radar systems. The clutter echo received by radar is usually strong and heterogeneous when the radar works in a complex terrain environment, resulting in performance degradation in moving target detection. Utilizing prior knowledge of the clutter distribution in the space–time domain, this paper proposes a novel moving target detection network based on small-sample transfer learning and attention mechanism. The proposed network first utilizes offline data to train the feature extraction network and reduce the online training time. Meanwhile, the attention mechanism used for feature extraction is applied in the beam-Doppler domain to improve classification accuracy of targets. Then, a small amount of real-time data are applied to a small-sample transfer network to fine-tune the feature extraction network. Finally, the target detection can be realized by the fine-tuned network. Simulation experiments show that the proposed network can eliminate the influence of heterogeneous clutter on moving target detection, and the attention mechanism can improve clutter suppression under a low signal-to-noise ratio regime. The proposed network has a lower computational load compared to conventional neural networks, enabling its use in real-time applications on space-borne/airborne radars. Full article
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23 pages, 11324 KiB  
Article
Optimal Feature-Guided Position-Shape Dual Optimization for Building Point Cloud Facade Detail Enhancement
by Shiming Li, Fengtao Yan, Kaifeng Ma, Qingfeng Hu, Feng Wang and Wenkai Liu
Remote Sens. 2024, 16(22), 4324; https://doi.org/10.3390/rs16224324 - 20 Nov 2024
Viewed by 549
Abstract
Dense three-dimensional point clouds are the cornerstone of modern architectural 3D reconstruction, containing a wealth of semantic structural information about building facades. However, current methods struggle to automatically and accurately extract the complex detailed structures of building facades from unstructured point clouds, with [...] Read more.
Dense three-dimensional point clouds are the cornerstone of modern architectural 3D reconstruction, containing a wealth of semantic structural information about building facades. However, current methods struggle to automatically and accurately extract the complex detailed structures of building facades from unstructured point clouds, with detailed facade modeling often relying heavily on manual interaction. This study introduces an efficient method for semantic structural detail enhancement of building facade point clouds, achieved through feature-guided dual-layer optimization of position and shape. The proposed framework addresses three key challenges: (1) robust extraction of facade semantic feature point clouds to effectively perceive the underlying geometric features of facade structures; (2) improved grouping of similarly structured objects using Hausdorff distance discrimination, overcoming the impact of point cloud omissions and granularity differences; (3) position-shape double optimization for facade enhancement, achieving detailed structural optimization. Validated on three typical datasets, the proposed method not only achieved 98.5% accuracy but also effectively supplemented incomplete scan results. It effectively optimizes semantic structures that widely exist and have the characteristic of repeated appearance on building facades, providing robust support for smart city construction and analytical applications. Full article
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23 pages, 8203 KiB  
Article
Optimal Hyperspectral Characteristic Parameters Construction and Concentration Retrieval for Inland Water Chlorophyll-a Under Different Motion States
by Jie Yu, Zhonghan Zhang, Yi Lin, Yuguan Zhang, Qin Ye, Xuefei Zhou, Hongtao Wang, Mingzhi Qu and Wenwei Ren
Remote Sens. 2024, 16(22), 4323; https://doi.org/10.3390/rs16224323 - 20 Nov 2024
Viewed by 631
Abstract
In recent decades, the rapid expansion of phytoplankton blooms caused by lake eutrophication has led to severe ecological destruction and impeded the sustainable economic development of local regions. Chlorophyll-a (Chl-a) is commonly used as a biological indicator to detect phytoplankton blooms due to [...] Read more.
In recent decades, the rapid expansion of phytoplankton blooms caused by lake eutrophication has led to severe ecological destruction and impeded the sustainable economic development of local regions. Chlorophyll-a (Chl-a) is commonly used as a biological indicator to detect phytoplankton blooms due to its ease of detection. To improve the accuracy of Chl-a estimation in aquatic systems, an accurate understanding of its true spectral characteristics is imperative. In this study, a comprehensive and realistic experimental scheme was designed from the perspective of real algal strains and real water states. Both in situ and laboratory-based hyperspectral data were collected and analyzed. The results show that there are huge spectral differences not only between laboratory-cultured and real algae strains, but also between static and disturbed water surface conditions. A total of ten different categories of spectral characteristics were selected in both disturbed and static states. Then, six parameters with the best models to the Chl-a concentration were identified. Finally, two linear models of the Chl-a concentration at peaks of 810 nm and 700 nm were identified as the best estimation models for the static and disturbed states, respectively. The results provide a scientific reference for the large-scale retrieval of the Chl-a concentration using satellite remote sensing data. This advancement benefits inland water monitoring and management efforts. Full article
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27 pages, 33223 KiB  
Article
Synergistic Coupling of Multi-Source Remote Sensing Data for Sandy Land Detection and Multi-Indicator Integrated Evaluation
by Junjun Wu, Yi Li, Bo Zhong, Yan Zhang, Qinhuo Liu, Xiaoliang Shi, Changyuan Ji, Shanlong Wu, Bin Sun, Changlong Li and Aixia Yang
Remote Sens. 2024, 16(22), 4322; https://doi.org/10.3390/rs16224322 - 19 Nov 2024
Viewed by 386
Abstract
Accurate and timely extraction and evaluation of sandy land are essential for ecological environmental protection; it is urgent to do the research to support the sustainable development goals (SDGs) of Land Degradation Neutrality. This study used Sentinel-1 Synthetic Aperture Radar (SAR) data and [...] Read more.
Accurate and timely extraction and evaluation of sandy land are essential for ecological environmental protection; it is urgent to do the research to support the sustainable development goals (SDGs) of Land Degradation Neutrality. This study used Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 8 OLI multispectral data as the main data sources. Combining the rich spectral information from optical data and the penetrating advantages of radar data, a feature-level fusion method was employed to unveil the intrinsic nature of vegetative cover and accurately identify sandy land. Simultaneously, leveraging the results obtained from training with measured data, a comprehensive desertification assessment model was proposed, which combines multiple indicators to achieve a thorough evaluation of sandy land. The results showed that the method based on feature-level fusion achieved an overall accuracy of 86.31% in sandy land detection in Gansu Province, China. The integrated multi-indicator model C22_C/FVC is the ratio of correlation texture features of VH to vegetation cover based on which sandy land can be classified into three categories. When C22_C/FVC is less than 2.2, the pixel is classified as fixed sandy land. Pixels of semi-fixed sandy land have an indicator value between 2.2 and 5.2. Shifting sandy land has values greater than 5.2. Results showed that shifting sandy land and semi-fixed sandy land are the predominant types in Gansu Province, with 85,100 square kilometers and 87,100 square kilometers, respectively. The acreage of fixed sandy land was the least, 51,800 square kilometers. The method presented in this paper is robust for the detection and evaluation of sandy land from satellite imageries, which can potentially be applied for conducting high-resolution and large-scale detection and evaluation of sandy land. Full article
(This article belongs to the Section Ecological Remote Sensing)
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26 pages, 10461 KiB  
Article
Accuracy and Precision of Shallow-Water Photogrammetry from the Sea Surface
by Elisa Casella, Giovanni Scicchitano and Alessio Rovere
Remote Sens. 2024, 16(22), 4321; https://doi.org/10.3390/rs16224321 - 19 Nov 2024
Viewed by 700
Abstract
Mapping shallow-water bathymetry and morphology represents a technical challenge. In fact, acoustic surveys are limited by water depths reachable by boat, and airborne surveys have high costs. Photogrammetric approaches (either via drone or from the sea surface) have opened up the possibility to [...] Read more.
Mapping shallow-water bathymetry and morphology represents a technical challenge. In fact, acoustic surveys are limited by water depths reachable by boat, and airborne surveys have high costs. Photogrammetric approaches (either via drone or from the sea surface) have opened up the possibility to perform shallow-water surveys easily and at accessible costs. This work presents a simple, low-cost, and highly portable platform that allows gathering sequential photos and echosounder depth values of shallow-water sites (up to 5 m depth). The photos are then analysed in conjunction with photogrammetric techniques to obtain digital bathymetric models and orthomosaics of the seafloor. The workflow was tested on four repeated surveys of the same area in the Western Mediterranean and allowed obtaining digital bathymetric models with centimetric average accuracy and precision and root mean square errors within a few decimetres. The platform presented in this work can be employed to obtain first-order bathymetric products, enabling the contextual establishment of the depth accuracy of the final products. Full article
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18 pages, 7633 KiB  
Article
Coastal Reclamation Embankment Deformation: Dynamic Monitoring and Future Trend Prediction Using Multi-Temporal InSAR Technology in Funing Bay, China
by Jinhua Huang, Baohang Wang, Xiaohe Cai, Bojie Yan, Guangrong Li, Wenhong Li, Chaoying Zhao, Liye Yang, Shouzhu Zheng and Linjie Cui
Remote Sens. 2024, 16(22), 4320; https://doi.org/10.3390/rs16224320 - 19 Nov 2024
Viewed by 405
Abstract
Reclamation is an effective strategy for alleviating land scarcity in coastal areas, thereby providing additional arable land and opportunities for marine ranching. Monitoring the safety of artificial reclamation embankments is crucial for protecting these reclaimed areas. This study employed synthetic aperture radar interferometry [...] Read more.
Reclamation is an effective strategy for alleviating land scarcity in coastal areas, thereby providing additional arable land and opportunities for marine ranching. Monitoring the safety of artificial reclamation embankments is crucial for protecting these reclaimed areas. This study employed synthetic aperture radar interferometry (InSAR) using 224 Sentinel-1A data, spanning from 9 January 2016 to 8 April 2024, to investigate the deformation characteristics of the coastal reclamation embankment in Funing Bay, China. We optimized the phase-unwrapping network by employing ambiguity-detection and redundant-observation methods to facilitate the multitemporal InSAR phase-unwrapping process. The deformation results indicated that the maximum observed land subsidence rate exceeded 50 mm per year. The Funing Bay embankment exhibited a higher level of internal deformation than areas closer to the sea. Time-series analysis revealed a gradual deceleration in the deformation rate. Furthermore, a geotechnical model was utilized to predict future deformation trends. Understanding the spatial dynamics of deformation characteristics in the Funing Bay reclamation embankment will be beneficial for ensuring the safe operation of future coastal reclamation projects. Full article
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15 pages, 7825 KiB  
Technical Note
D-InSAR-Based Analysis of Slip Distribution and Coulomb Stress Implications from the 2024 Mw 7.01 Wushi Earthquake
by Yurong Ding, Xin Liu, Xiaofeng Dai, Gaoying Yin, Yang Yang and Jinyun Guo
Remote Sens. 2024, 16(22), 4319; https://doi.org/10.3390/rs16224319 - 19 Nov 2024
Viewed by 310
Abstract
On 23 January 2024, an Mw 7.01 earthquake struck the Wushi County, Xinjiang Uygur Autonomous Region, China. The occurrence of this earthquake provides an opportunity to gain a deeper understanding of the rupture behavior and tectonic activity of the fault system in [...] Read more.
On 23 January 2024, an Mw 7.01 earthquake struck the Wushi County, Xinjiang Uygur Autonomous Region, China. The occurrence of this earthquake provides an opportunity to gain a deeper understanding of the rupture behavior and tectonic activity of the fault system in the Tianshan seismic belt. The coseismic deformation field of the Wushi earthquake was derived from Sentinel-1A ascending and descending track data using Differential Interferometric Synthetic Aperture Radar (D-InSAR) technology. The findings reveal a maximum line-of-sight (LOS) displacement of 81.1 cm in the uplift direction and 16 cm in subsidence. Source parameters were determined using an elastic half-space dislocation model. The slip distribution on the fault plane for the Mw 7.01 Wushi earthquake was further refined through a coseismic slip model, and Coulomb stress changes on nearby faults were calculated to evaluate seismic hazards in surrounding areas. Results indicate that the coseismic rupture in the Mw 7.01 Wushi earthquake sequence was mainly characterized by left-lateral strike-slip motion. The peak fault slip was 3.2 m, with a strike of 228.34° and a dip of 61.80°, concentrated primarily at depths between 5 and 25 km. The focal depth is 13 km. This is consistent with findings reported by organizations like the United States Geological Survey (USGS). The fault rupture extended to the surface, consistent with field investigations by the Xinjiang Uygur Autonomous Region Earthquake Bureau. Coulomb stress results suggest that several fault zones, including the Kuokesale, Dashixia, Piqiang North, Karaitike, southeastern sections of the Wensu, northwestern sections of the Tuoergan, and the Maidan-Sayram Fault Zone, are within regions of stress loading. These areas show an increased risk of future seismic activity and warrant close monitoring. Full article
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21 pages, 7222 KiB  
Article
Spatiotemporal Variations and Driving Factors of Water Availability in the Arid and Semiarid Regions of Northern China
by Xiaoyu Han, Yaning Chen, Gonghuan Fang, Zhi Li, Yupeng Li and Yanfeng Di
Remote Sens. 2024, 16(22), 4318; https://doi.org/10.3390/rs16224318 - 19 Nov 2024
Viewed by 484
Abstract
It is anticipated that global warming will modify precipitation and evapotranspiration patterns, consequently affecting water availability. Changes in water availability pose challenges to freshwater supply, food security, and ecosystem sustainability. However, the variations and driving mechanisms of water availability in the arid and [...] Read more.
It is anticipated that global warming will modify precipitation and evapotranspiration patterns, consequently affecting water availability. Changes in water availability pose challenges to freshwater supply, food security, and ecosystem sustainability. However, the variations and driving mechanisms of water availability in the arid and semiarid regions of Northern China remain unclear. This study evaluates the accuracy of three evapotranspiration products and analyzes the changes in water availability in the arid and semiarid regions of Northern China over the past 39 years (1982–2020) along with their driving factors. The results indicate that during this period, precipitation increased at a rate of 7.5 mm/decade, while evapotranspiration rose at a higher rate of 13 mm/decade, resulting in a decline in water availability of 5.5 mm/decade. Spatially, approximately 30.17% of the area exhibited a significant downward trend in water availability, while 65.65% remained relatively stable. Evapotranspiration is the dominant factor leading to the decrease in water availability, with a contribution rate of 63.41%. The increase in evapotranspiration was primarily driven by temperature (32.53% contribution) and the saturation vapor pressure deficit (24.72% contribution). The decline in water availability may further exacerbate drought risks in arid and semiarid regions. The research results can provide a scientific basis for developing water resource management strategies and ecological restoration strategies under environmental change. Full article
(This article belongs to the Special Issue Remote Sensing for Terrestrial Hydrologic Variables)
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29 pages, 30487 KiB  
Article
Joint Classification of Hyperspectral and LiDAR Data via Multiprobability Decision Fusion Method
by Tao Chen, Sizuo Chen, Luying Chen, Huayue Chen, Bochuan Zheng and Wu Deng
Remote Sens. 2024, 16(22), 4317; https://doi.org/10.3390/rs16224317 - 19 Nov 2024
Viewed by 568
Abstract
With the development of sensor technology, the sources of remotely sensed image data for the same region are becoming increasingly diverse. Unlike single-source remote sensing image data, multisource remote sensing image data can provide complementary information for the same feature, promoting its recognition. [...] Read more.
With the development of sensor technology, the sources of remotely sensed image data for the same region are becoming increasingly diverse. Unlike single-source remote sensing image data, multisource remote sensing image data can provide complementary information for the same feature, promoting its recognition. The effective utilization of remote sensing image data from various sources can enhance the extraction of image features and improve the accuracy of feature recognition. Hyperspectral remote sensing (HSI) data and light detection and ranging (LiDAR) data can provide complementary information from different perspectives and are frequently combined in feature identification tasks. However, the process of joint use suffers from data redundancy, low classification accuracy and high time complexity. To address the aforementioned issues and improve feature recognition in classification tasks, this paper introduces a multiprobability decision fusion (PRDRMF) method for the combined classification of HSI and LiDAR data. First, the original HSI data and LiDAR data are downscaled via the principal component–relative total variation (PRTV) method to remove redundant information. In the multifeature extraction module, the local texture features and spatial features of the image are extracted to consider the local texture and spatial structure of the image data. This is achieved by utilizing the local binary pattern (LBP) and extended multiattribute profile (EMAP) for the two types of data after dimensionality reduction. The four extracted features are subsequently input into the corresponding kernel–extreme learning machine (KELM), which has a simple structure and good classification performance, to obtain four classification probability matrices (CPMs). Finally, the four CPMs are fused via a multiprobability decision fusion method to obtain the optimal classification results. Comparison experiments on four classical HSI and LiDAR datasets demonstrate that the method proposed in this paper achieves high classification performance while reducing the overall time complexity of the method. Full article
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19 pages, 7749 KiB  
Article
Generative Simplex Mapping: Non-Linear Endmember Extraction and Spectral Unmixing for Hyperspectral Imagery
by John Waczak and David J. Lary
Remote Sens. 2024, 16(22), 4316; https://doi.org/10.3390/rs16224316 - 19 Nov 2024
Viewed by 581
Abstract
We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM). The model represents endmember mixing using a latent space of points sampled within a (n1)-simplex corresponding to n [...] Read more.
We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM). The model represents endmember mixing using a latent space of points sampled within a (n1)-simplex corresponding to n unique sources. Barycentric coordinates within this simplex are naturally interpreted as relative endmember abundances satisfying both the abundance sum-to-one and abundance non-negativity constraints. Points in this latent space are mapped to reflectance spectra via a flexible function combining linear and non-linear mixing. Due to the probabilistic formulation of the GSM, spectral variability is also estimated by a precision parameter describing the distribution of observed spectra. Model parameters are determined using a generalized expectation-maximization algorithm, which guarantees non-negativity for extracted endmembers. We first compare the GSM against three varieties of non-negative matrix factorization (NMF) on a synthetic data set of linearly mixed spectra from the USGS spectral database. Here, the GSM performed favorably for both endmember accuracy and abundance estimation with all non-linear contributions driven to zero by the fitting procedure. In a second experiment, we apply the GTM to model non-linear mixing in real hyperspectral imagery captured over a pond in North Texas. The model accurately identified spectral signatures corresponding to near-shore algae, water, and rhodamine tracer dye introduced into the pond to simulate water contamination by a localized source. Abundance maps generated using the GSM accurately track the evolution of the dye plume as it mixes into the surrounding water. Full article
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18 pages, 6209 KiB  
Article
Impact of Latency and Continuity of GNSS Products on Filter-Based Real-Time LEO Satellite Clock Determination
by Meifang Wu, Kan Wang, Jinqian Wang, Wei Xie, Jiawei Liu, Beixi Chen, Yulong Ge, Ahmed El-Mowafy and Xuhai Yang
Remote Sens. 2024, 16(22), 4315; https://doi.org/10.3390/rs16224315 - 19 Nov 2024
Viewed by 439
Abstract
High-precision Low Earth Orbit (LEO) satellite clocks are essential for LEO-augmented Positioning, Navigation, and Timing (PNT) services. Nowadays, high-precision LEO satellite clocks can be determined in real-time using a Kalman filter either onboard or on the ground, as long as the GNSS observations [...] Read more.
High-precision Low Earth Orbit (LEO) satellite clocks are essential for LEO-augmented Positioning, Navigation, and Timing (PNT) services. Nowadays, high-precision LEO satellite clocks can be determined in real-time using a Kalman filter either onboard or on the ground, as long as the GNSS observations collected onboard LEO satellites can be transmitted to the ground in real-time. While various real-time and high-precision GNSS products are available nowadays in the latter case, their continuity and latencies in engineering reality are not as perfect as expected and will lead to unignorable impacts on the precision of the real-time LEO satellite clocks. In this study, based on real observations of Sentinel-3B, the impacts of different latencies and continuity of the real-time GNSS products on LEO real-time clocks are determined and discussed for two scenarios, namely the “epoch estimation” and “arc estimation” scenarios. The former case refers to the traditional filter-based processing epoch-by-epoch, and the latter case connects LEO satellite clocks from different rounds of filter-based processing under a certain arc length. The two scenarios lead to the “end-loss” and “mid-gap” situations. Latencies of the real-time GNSS products are discussed for the cases of orbit-only latency, clock-only latency, and combined forms, and different handling methods for the missing GNSS satellite clocks are discussed and compared. Results show that the real-time LEO satellite clock precision is very sensitive to the precision of real-time GNSS satellite clocks, and prediction of the latter becomes essential in case of their latencies. For the “end-loss” situation, with a latency of 30 to 120 s for the GNSS real-time clocks, the LEO satellite clock precision is reduced from about 0.2 to 0.28–0.57 ns. Waiting for the GNSS products in case of their short latencies and predicting the LEO satellite clocks instead could be a better option. For “arc-estimation”, when the gap of GNSS real-time products increases from 5 to 60 min, the real-time LEO clock precision decreases from 0.26 to 0.32 ns. Full article
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17 pages, 5205 KiB  
Article
Temporal Associations Between Polarimetric Updraft Proxies and Signatures of Inflow and Hail in Supercells
by Matthew S. Van Den Broeke and Erik R. Green
Remote Sens. 2024, 16(22), 4314; https://doi.org/10.3390/rs16224314 - 19 Nov 2024
Viewed by 358
Abstract
Recurring polarimetric radar signatures in supercells include deep and persistent differential reflectivity (ZDR) columns, hail inferred in low-level scans, and the ZDR arc signature. Prior investigations of supercell polarimetric signatures reveal positive correlations between the ZDR column depth [...] Read more.
Recurring polarimetric radar signatures in supercells include deep and persistent differential reflectivity (ZDR) columns, hail inferred in low-level scans, and the ZDR arc signature. Prior investigations of supercell polarimetric signatures reveal positive correlations between the ZDR column depth and cross-sectional area and quantitative characteristics of the radar reflectivity field. This study expands upon prior work by examining temporal associations between supercell polarimetric radar signatures, incorporating a dataset of relatively discrete, right-moving supercells from the continental United States observed by the Weather Surveillance Radar 1988-Doppler (WSR-88D) network. Cross-correlation coefficients were calculated between the ZDR column area and depth and the base-scan hail area, ZDR arc area, and mean ZDR arc value. These correlation values were computed with a positive and negative lag time of up to 45 min. Results of the lag correlation analysis are consistent with prior observations indicative of storm cycling, including temporal associations between ZDR columns and inferred hail signatures/ZDR arcs in both tornadic and nontornadic supercells, but were most pronounced in tornadic storms. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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17 pages, 10034 KiB  
Article
Vertical Distribution, Diurnal Evolution, and Source Region of Formaldehyde During the Warm Season Under Ozone-Polluted and Non-Polluted Conditions in Nanjing, China
by Keqiang Cheng, Mingjie Xie, Yuhang Wang and Yahan Lu
Remote Sens. 2024, 16(22), 4313; https://doi.org/10.3390/rs16224313 - 19 Nov 2024
Viewed by 376
Abstract
Formaldehyde (HCHO), a key volatile organic compound (VOC) in the atmosphere, plays a crucial role in driving photochemical processes. Satellite-based observations of column concentrations of HCHO and other gaseous pollutants (e.g., NO2) have generally been used in previous studies to elucidate [...] Read more.
Formaldehyde (HCHO), a key volatile organic compound (VOC) in the atmosphere, plays a crucial role in driving photochemical processes. Satellite-based observations of column concentrations of HCHO and other gaseous pollutants (e.g., NO2) have generally been used in previous studies to elucidate the mechanisms behind secondary organic aerosol (SOA) and ozone (O3) formation. This study aimed to investigate the characteristics of HCHO by retrieving its vertical profile over Nanjing during the warm season (May–June 2022) and analyzing the diurnal variation in vertical distribution and potential source regions on non-polluted (MDA8 O3 < 160 μg m−3, NO3P) and O3-polluted (MDA8 O3 ≥ 160 μg m−3, O3P) days. Under both conditions, HCHO was primarily concentrated below 1.5 km altitude, with average vertical profiles displaying similar Boltzmann-like distributions. However, HCHO concentrations on O3P days were 1.2–1.6 times higher than those on non-polluted days at the same altitude below 1.5 km. Maximum HCHO concentrations occurred in the afternoon, while the peak value in the 0.1–0.4 km layers was reached around noon (~11:00 a.m.). The variation rates (VR) of HCHO in the 0.3–1.2 km altitudes had a maximum on O3P days (approximately 0.33 ppbv h−1), and were significantly higher (p < 0.01) than the VR observed on NO3P days (0.14–0.20 ppbv h−1). The analysis of footprints showed that HCHO concentrations were jointly influenced by the upstream region and the surroundings of the study site. The study results improve the understanding of the vertical distribution and potential source regions of HCHO. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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