Journal Description
Remote Sensing
Remote Sensing
is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Geomatics
Impact Factor:
5.0 (2022);
5-Year Impact Factor:
5.6 (2022)
Latest Articles
Investigating Dual-Source Satellite Image Data and ALS Data for Estimating Aboveground Biomass
Remote Sens. 2024, 16(10), 1804; https://doi.org/10.3390/rs16101804 (registering DOI) - 19 May 2024
Abstract
Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained
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Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions.
Full article
(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)
Open AccessArticle
Examining the Impacts of Pre-Fire Forest Conditions on Burn Severity Using Multiple Remote Sensing Platforms
by
Kangsan Lee, Willem J. D. van Leeuwen, Jeffrey K. Gillan and Donald A. Falk
Remote Sens. 2024, 16(10), 1803; https://doi.org/10.3390/rs16101803 (registering DOI) - 19 May 2024
Abstract
Pre-fire environmental conditions play a critical role in wildfire severity. This study investigated the impact of pre-fire forest conditions on burn severity as a result of the 2020 Bighorn Fire in the Santa Catalina Mountains in Arizona. Using a stepwise regression model and
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Pre-fire environmental conditions play a critical role in wildfire severity. This study investigated the impact of pre-fire forest conditions on burn severity as a result of the 2020 Bighorn Fire in the Santa Catalina Mountains in Arizona. Using a stepwise regression model and remotely sensed data from Landsat 8 and LiDAR, we analyzed the effects of structural and functional vegetation traits and environmental factors on burn severity. This analysis revealed that the difference normalized burn ratio (dNBR) was a more reliable indicator of burn severity compared to the relative dNBR (RdNBR). Stepwise regression identified pre-fire normalized difference vegetation index (NDVI), canopy cover, and tree density as significant variables across all land cover types that explained burn severity, suggesting that denser areas with higher vegetation greenness experienced more severe burns. Interestingly, residuals between the actual and estimated dNBR were lower in herbaceous zones compared to denser forested areas at similar elevations, suggesting potentially more predictable burn severity in open areas. Spatial analysis using Geary’s C statistics further revealed a strong negative autocorrelation: areas with high burn severity tended to be clustered, with lower severity areas interspersed. Overall, this study demonstrates the potential of readily available remote sensing data to predict potential burn severity values before a fire event, providing valuable information for forest managers to develop strategies for mitigating future wildfire damage.
Full article
(This article belongs to the Special Issue Vegetation Mapping through Multiscale Remote Sensing)
Open AccessArticle
Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing
by
Haitao Dong, Jian Suo, Zhigang Zhu, Haiyan Wang and Hongbing Ji
Remote Sens. 2024, 16(10), 1802; https://doi.org/10.3390/rs16101802 (registering DOI) - 18 May 2024
Abstract
Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is
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Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is conducted on UAVS signal preprocessing subjected to gain-phase uncertainties for average acoustic intensity measurement (AAIM) and complex acoustic intensity measurement (CAIM)-based vector DOA estimation, aiming to explain the theoretical restrictions of intensity-based vector acoustic preprocessing approaches. On this basis, a generalized vector acoustic preprocessing optimization model is established in which the principle can be described as “maximizing the denoising performance under the constraints of an equivalent amplitude-gain response and phase-bias response”. A novel vector acoustic preprocessing method named linear matched stochastic resonance (LMSR) is proposed within the framework of matched stochastic resonance theory, which can naturally guarantee the linear gain-phase restrictions, as well achieving effective denoising performance. Numerical analyses demonstrate the superior vector DOA estimation performance of our proposed LMSR-AAIM and LMSR-CAIM methods in comparison to classical intensity-based AAIM and CAIM methods, especially under low-SNR conditions and non-Gaussian impulsive noise circumstances. Experimental verification conducted in the South China Sea further verifies its the effectiveness for practical application. This work can lay a solid foundation to break through the challenges of underwater remote vector acoustic DOA estimation under low-SNR conditions and complex ocean ambient noise and can provide important guidance for future research work.
Full article
(This article belongs to the Special Issue Space-Air-Ground-Ocean Integrated Sensing and Information Transmission)
Open AccessArticle
Greenness and Actual Evapotranspiration in the Unrestored Riparian Corridor of the Colorado River Delta in Response to In-Channel Water Deliveries in 2021 and 2022
by
Pamela L. Nagler, Ibrahima Sall, Martha Gomez-Sapiens, Armando Barreto-Muñoz, Christopher J. Jarchow, Karl Flessa and Kamel Didan
Remote Sens. 2024, 16(10), 1801; https://doi.org/10.3390/rs16101801 (registering DOI) - 18 May 2024
Abstract
Natural resource managers may utilize remotely sensed data to monitor vegetation within their decision-making frameworks for improving habitats. Under binational agreements between the United States and Mexico, seven reaches were targeted for riparian habitat enhancement. Monitoring was carried out using Landsat 8 16-day
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Natural resource managers may utilize remotely sensed data to monitor vegetation within their decision-making frameworks for improving habitats. Under binational agreements between the United States and Mexico, seven reaches were targeted for riparian habitat enhancement. Monitoring was carried out using Landsat 8 16-day intervals of the two-band enhanced vegetation index 2 (EVI2) for greenness and actual evapotranspiration (ETa). In-channel water was delivered in 2021 and 2022 at four places in Reach 4. Three reaches (Reaches 4, 5 and 7) showed no discernable difference in EVI2 from reaches that did not receive in-channel water (Reaches 1, 2, 3 and 6). EVI2 in 2021 was higher than 2020 in all reaches except Reach 3, and EVI2 in 2022 was lower than 2021 in all reaches except Reach 7. ET(EVI2) was higher in 2020 than in 2021 and 2022 in all seven reaches; it was highest in Reach 4 (containing restoration sites) in all years. Excluding restoration sites, compared with 2020, unrestored reaches showed that EVI2 minimally increased in 2021 and 2022, while ET(EVI2) minimally decreased despite added water in 2021–2022. Difference maps comparing 2020 (no-flow year) to 2021 and 2022 (in-channel flows) reveal areas in Reaches 5 and 7 where the in-channel flows increased greenness and ET(EVI2).
Full article
(This article belongs to the Section Biogeosciences Remote Sensing)
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Open AccessArticle
Modeling and Locating the Wind Erosion at the Dry Bottom of the Aral Sea Based on an InSAR Temporal Decorrelation Decomposition Model
by
Yubin Song, Xuelian Xun, Hongwei Zheng, Xi Chen, Anming Bao, Ying Liu, Geping Luo, Jiaqiang Lei, Wenqiang Xu, Tie Liu, Olaf Hellwich and Qing Guan
Remote Sens. 2024, 16(10), 1800; https://doi.org/10.3390/rs16101800 (registering DOI) - 18 May 2024
Abstract
The dust originating from the extinct lake of the Aral Sea poses a considerable threat to the surrounding communities and ecosystems. The accurate location of these wind erosion areas is an essential prerequisite for controlling sand and dust activity. However, few relevant indicators
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The dust originating from the extinct lake of the Aral Sea poses a considerable threat to the surrounding communities and ecosystems. The accurate location of these wind erosion areas is an essential prerequisite for controlling sand and dust activity. However, few relevant indicators reported in this current study can accurately describe and measure wind erosion intensity. A novel wind erosion intensity (WEI) of a pixel resolution unit was defined in this paper based on deformation due to the wind erosion in this pixel resolution unit. We also derived the relationship between WEI and soil InSAR temporal decorrelation (ITD). ITD is usually caused by the surface change over time, which is very suitable for describing wind erosion. However, within a pixel resolution unit, the ITD signal usually includes soil and vegetation contributions, and extant studies concerning this issue are considerably limited. Therefore, we proposed an ITD decomposition model (ITDDM) to decompose the ITD signal of a pixel resolution unit. The least-square method (LSM) based on singular value decomposition (SVD) is used to estimate the ITD of soil (SITD) within a pixel resolution unit. We verified the results qualitatively by the landscape photos, which can reflect the actual conditions of the soil. At last, the WEI of the Aral Sea from 23 June 2020, to 5 July 2020 was mapped. The results confirmed that (1) based on the ITDDM model, the SITD can be accurately estimated by the LSM; (2) the Aral Sea is experiencing severe wind erosion; and (3) the middle, northeast, and southeast bare areas of the South Aral Sea are where salt dust storms may occur.
Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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Open AccessArticle
Conditional Diffusion Model for Urban Morphology Prediction
by
Tiandong Shi, Ling Zhao, Fanfan Liu, Ming Zhang, Mengyao Li, Chengli Peng and Haifeng Li
Remote Sens. 2024, 16(10), 1799; https://doi.org/10.3390/rs16101799 (registering DOI) - 18 May 2024
Abstract
Predicting urban morphology based on local attributes is an important issue in urban science research. The deep generative models represented by generative adversarial network (GAN) models have achieved impressive results in this area. However, in such methods, the urban morphology is assumed to
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Predicting urban morphology based on local attributes is an important issue in urban science research. The deep generative models represented by generative adversarial network (GAN) models have achieved impressive results in this area. However, in such methods, the urban morphology is assumed to follow a specific probability distribution and be able to directly approximate the distribution via GAN models, which is not a realistic strategy. As demonstrated by the score-based model, a better strategy is to learn the gradient of the probability distribution and implicitly approximate the distribution. Therefore, in this paper, an urban morphology prediction method based on the conditional diffusion model is proposed. Implementing this approach results in the decomposition of the attribute-based urban morphology prediction task into two subproblems: estimating the gradient of the conditional distribution, and gradient-based sampling. During the training stage, the gradient of the conditional distribution is approximated by using a conditional diffusion model to predict the noise added to the original urban morphology. In the generation stage, the corresponding conditional distribution is parameterized based on the noise predicted by the conditional diffusion model, and the final prediction result is generated through iterative sampling. The experimental results showed that compared with GAN-based methods, our method demonstrated improvements of 5.5%, 5.9%, and 13.2% in the metrics of low-level pixel features, shallow structural features, and deep structural features, respectively.
Full article
Open AccessArticle
Long-Time Coherent Integration for the Spatial-Based Bistatic Radar Based on Dual-Scale Decomposition and Conditioned CPF
by
Suqi Li, Yihan Wang, Yanfeng Liang and Bailu Wang
Remote Sens. 2024, 16(10), 1798; https://doi.org/10.3390/rs16101798 (registering DOI) - 18 May 2024
Abstract
This paper addresses the problem of weak maneuvering target detection in the space-based bistatic radar system through long-time coherent integration (LTCI). The space-based bistatic radar is vulnerable to the high-order range migration (RM) and Doppler frequency migration (DFM), since the target, the receiver
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This paper addresses the problem of weak maneuvering target detection in the space-based bistatic radar system through long-time coherent integration (LTCI). The space-based bistatic radar is vulnerable to the high-order range migration (RM) and Doppler frequency migration (DFM), since the target, the receiver and the transmitter all can play fast movement independently. To correct high- order RM and DFM, this usually involves joint high-dimensional parameter searching, incurring a large computational burden. In our previous work, a dual-scale (DS) decomposition of motion parameters was proposed, in which the optimal GRFT is conditionally decoupled into two cascade procedures called the modified generalized inverse Fourier transform (GIFT) and generalized Fourier transform (GFT), resulting in the DS-GRFT detector. However, even if the DS-GRFT detector preserves the superior performance and dramatically decreases the complexity, high-dimensional searching is still required. In this paper, by analyzing the structure of the DS-GRFT detector, we further designed a conditioned cubic phase function (CCPF) tailored to the range–slow-time signal after GIFT, breaking the joint high-dimensional searching into independent one-dimensional searching. Then, by connecting the proposed CCPF with the GIFT, we achieved a new LTCI detector called the DS-GIFT-CCPF detector, which obtained a significant computational cost reduction with acceptable performance loss, as demonstrated in numerical experiments.
Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing)
Open AccessArticle
GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion
by
Yong Wang, Zhenglong He, Xiangqiang Zeng, Juncheng Zeng, Zongxi Cen, Luyang Qiu, Xiaowei Xu and Qunxiong Zhuo
Remote Sens. 2024, 16(10), 1797; https://doi.org/10.3390/rs16101797 (registering DOI) - 18 May 2024
Abstract
Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a
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Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone’s perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research.
Full article
(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition))
Open AccessArticle
Deep Blind Fault Activity—A Fault Model of Strong Mw 5.5 Earthquake Seismogenic Structures in North China
by
Guanshen Liu, Renqi Lu, Dengfa He, Lihua Fang, Yang Zhang, Peng Su and Wei Tao
Remote Sens. 2024, 16(10), 1796; https://doi.org/10.3390/rs16101796 (registering DOI) - 18 May 2024
Abstract
North China is one of the high-risk areas for destructive and strong earthquakes in mainland China and has experienced numerous strong historical earthquakes. An earthquake of magnitude MW 5.5 struck Pingyuan County, Dezhou city, in Shandong Province, China, on 6 August 2023.
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North China is one of the high-risk areas for destructive and strong earthquakes in mainland China and has experienced numerous strong historical earthquakes. An earthquake of magnitude MW 5.5 struck Pingyuan County, Dezhou city, in Shandong Province, China, on 6 August 2023. This earthquake was the strongest in the eastern North China Craton since the 1976 Tangshan earthquake. Since the earthquake did not produce surface ruptures, the seismogenic structure for fault responsible for the Pingyuan MW 5.5 earthquake is still unclear. To reveal the subsurface geological structure near the earthquake epicenter, this study used high-resolution two-dimensional (2D) seismic reflection profiles and constructed a three-dimensional (3D) geometric model of the Tuqiao Fault by interpreting the faults in the seismic reflection profiles. This study further combined focal mechanism solutions, aftershock clusters, and other seismological data to discuss the seismogenic fault of the Pingyuan MW 5.5 earthquake. The results show that the Tuqiao Fault is not the seismogenic fault of the MW 5.5 earthquake. The actual seismogenic structure may be related to the NE-oriented high-angle strike-slip blind fault developed in the basement. We further propose three possible fault models for the strong seismogenic structure in North China to discuss the potential seismotectonics in this region.
Full article
(This article belongs to the Special Issue Earthquake Disaster Monitoring Using Remote Sensing Image Processing and Geophysical Techniques)
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Open AccessArticle
DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion
by
Qiancheng Wei, Ying Liu, Xiaoping Jiang, Ben Zhang, Qiya Su and Muyao Yu
Remote Sens. 2024, 16(10), 1795; https://doi.org/10.3390/rs16101795 (registering DOI) - 18 May 2024
Abstract
The fusion of infrared and visible images aims to leverage the strengths of both modalities, thereby generating fused images with enhanced visible perception and discrimination capabilities. However, current image fusion methods frequently treat common features between modalities (modality-commonality) and unique features from each
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The fusion of infrared and visible images aims to leverage the strengths of both modalities, thereby generating fused images with enhanced visible perception and discrimination capabilities. However, current image fusion methods frequently treat common features between modalities (modality-commonality) and unique features from each modality (modality-distinctiveness) equally during processing, neglecting their distinct characteristics. Therefore, we propose a DDFNet-A for infrared and visible image fusion. DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. The extracted low and high features were then fused using distinct methods. In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network. DDFNet-A achieved competitive results on three datasets, with EN, MI, VIFF, , FMI, and metrics reaching the best performance on the TNO dataset, achieving 7.1217, 2.1620, 0.7739, 0.5426, 0.8129, and 0.9079, respectively. These values are , , , , , and higher than those of the second-best methods, respectively. The experimental results confirm that our DDFNet-A achieves better fusion performance than state-of-the-art (SOTA) methods.
Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
Open AccessArticle
An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
by
Abdulaziz Alluhaybi, Panos Psimoulis and Rasa Remenyte-Prescott
Remote Sens. 2024, 16(10), 1794; https://doi.org/10.3390/rs16101794 (registering DOI) - 18 May 2024
Abstract
Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites’ redundancy, a potential
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Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites’ redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS’s performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS’s positioning performance. Various methods, including optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, five optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The assessment of the optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites’ signal. The optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investigate ABC’s performance, the method was applied for the selection of an optimal GNSS satellite subset according to the number of total available tracked GNSS satellites (up to 31 satellites), leading to more than 300 million possible combinations of 15 GNSS satellites. ABC was able to select the optimal satellite subsets with 100% accuracy.
Full article
(This article belongs to the Topic Artificial Intelligence in Navigation)
Open AccessArticle
How Representative Are European AERONET-OC Sites of European Marine Waters?
by
Ilaria Cazzaniga and Frédéric Mélin
Remote Sens. 2024, 16(10), 1793; https://doi.org/10.3390/rs16101793 (registering DOI) - 18 May 2024
Abstract
Data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) have been extensively used to assess Ocean Color radiometric products from various satellite sensors. This study, focusing on Ocean Color radiometric operational products from the Sentinel-3 Ocean and Land Colour Instrument
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Data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) have been extensively used to assess Ocean Color radiometric products from various satellite sensors. This study, focusing on Ocean Color radiometric operational products from the Sentinel-3 Ocean and Land Colour Instrument (OLCI), aims at investigating where in the European seas the results of match-up analyses at the European marine AERONET-OC sites could be applicable. Data clustering is applied to OLCI remote sensing reflectance from the various sites to define different sets of optical classes, which are later used to identify class-based uncertainties. A set of fifteen classes grants medium-to-high classification levels to most European seas, with exceptions in the South-East Mediterranean Sea, the Atlantic Ocean, or the Gulf of Bothnia. In these areas, spectra are very often identified as novel with respect to the generated set of classes, suggesting their under-representation in AERONET-OC data. Uncertainties are finally mapped onto European seas according to class membership. The largest uncertainty values are obtained in the blue spectral region for almost all classes. In clear waters, larger values are obtained in the blue bands. Conversely, larger values are shown in the green and red bands in coastal and turbid waters.
Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Recent Progress in Ocean Colour Remote Sensing)
Open AccessTechnical Note
Eutrophication and HAB Occurrence Control in Lakes of Different Origins: A Multi-Source Remote Sensing Detection Strategy
by
Giovanni Laneve, Alejandro Téllez, Ashish Kallikkattil Kuruvila, Milena Bruno and Valentina Messineo
Remote Sens. 2024, 16(10), 1792; https://doi.org/10.3390/rs16101792 (registering DOI) - 18 May 2024
Abstract
Remote sensing techniques have become pivotal in monitoring algal blooms and population dynamics in freshwater bodies, particularly to assess the ecological risks associated with eutrophication. This study focuses on remote sensing methods for the analysis of 4 Italian lakes with diverse geological origins,
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Remote sensing techniques have become pivotal in monitoring algal blooms and population dynamics in freshwater bodies, particularly to assess the ecological risks associated with eutrophication. This study focuses on remote sensing methods for the analysis of 4 Italian lakes with diverse geological origins, leveraging water quality samples and data from the Sentinel-2 and Landsat 5.7–8 platforms. Chl-a, a well-correlated indicator of phytoplankton biomass abundance and eutrophication, was estimated using ordinary least squares linear regression to calibrate surface reflectance with chl-a concentrations. Temporal gaps between sample and image acquisition were considered, and atmospheric correction dedicated to water surfaces was implemented using ACOLITE and those specific to each satellite platform. The developed models achieved determination coefficients higher than 0.69 with mean square errors close to 3 mg/m3 for water bodies with low turbidity. Furthermore, the time series described by the models portray the seasonal variations in the lakes water bodies.
Full article
(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)
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Open AccessArticle
Constrained Iterative Adaptive Algorithm for the Detection and Localization of RFI Sources Based on the SMAP L-Band Microwave Radiometer
by
Xinxin Wang, Xiang Wang, Lin Wang, Jianchao Fan and Enbo Wei
Remote Sens. 2024, 16(10), 1791; https://doi.org/10.3390/rs16101791 (registering DOI) - 18 May 2024
Abstract
The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency
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The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.
Full article
Open AccessArticle
Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime
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Qiang Na, Biao Cao, Boxiong Qin, Fan Mo, Limeng Zheng, Yongming Du, Hua Li, Zunjian Bian, Qing Xiao and Qinhuo Liu
Remote Sens. 2024, 16(10), 1790; https://doi.org/10.3390/rs16101790 (registering DOI) - 18 May 2024
Abstract
Land surface temperature (LST) is a fundamental parameter in global climate, environmental, and geophysical studies. Remote sensing is an essential approach for obtaining large-scale and frequently updated LST data. However, due to the wide field of view of remote sensing sensors, the observed
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Land surface temperature (LST) is a fundamental parameter in global climate, environmental, and geophysical studies. Remote sensing is an essential approach for obtaining large-scale and frequently updated LST data. However, due to the wide field of view of remote sensing sensors, the observed LST with diverse view geometries suffers from inconsistency caused by the thermal radiation directionality (TRD) effect, which results in LST products being incomparable, especially during daytime. To address this issue and correct current off-nadir LSTs to nadir LSTs, a semi-physical time-evolved kernel-driven model (TEKDM) is proposed, which depicts multitemporal TRD patterns during the daytime. In addition, we employ a Bayesian optimization method to calibrate seven unknown parameters in the TEKDM. Validation results using the U.S. Climate Reference Network (USCRN) sites show that the RMSE (MBE) for GOES-16 and MODIS off-nadir LST products is reduced from 3.29 K (−2.0 K) to 2.34 K (−0.02 K), with an RMSE reduction of 0.95 K (29%) and a significant reduction in systematic bias. Moreover, the proposed method successfully eliminates the angular and temporal dependence of the LST difference between the satellite off-nadir LST and in situ nadir LST. In summary, this study presents a feasible approach for estimating the high-accuracy nadir LST, which can enhance the applicability of LST products in various domains.
Full article
(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing II)
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Open AccessReview
Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review
by
Rui Mota, José M. Pacheco, Adriano Pimentel and Artur Gil
Remote Sens. 2024, 16(10), 1789; https://doi.org/10.3390/rs16101789 (registering DOI) - 18 May 2024
Abstract
Volcanic clouds pose significant threats to air traffic, human health, and economic activity, making early detection and monitoring crucial. Accurate determination of eruptive source parameters is crucial for forecasting and implementing preventive measures. This review article aims to identify the most common remote
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Volcanic clouds pose significant threats to air traffic, human health, and economic activity, making early detection and monitoring crucial. Accurate determination of eruptive source parameters is crucial for forecasting and implementing preventive measures. This review article aims to identify the most common remote sensing methods for monitoring volcanic clouds. To achieve this, we conducted a systematic literature review of scientific articles indexed in the Web of Science database published between 2010 and 2022, using multiple query strings across all fields. The articles were reviewed based on research topics, remote sensing methods, practical applications, case studies, and outcomes using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our study found that satellite-based remote sensing approaches are the most cost-efficient and accessible, allowing for the monitoring of volcanic clouds at various spatial scales. Brightness temperature difference is the most commonly used method for detecting volcanic clouds at a specified temperature threshold. Approaches that apply machine learning techniques help overcome the limitations of traditional methods. Despite the constraints imposed by spatial and temporal resolution and optical limitations of sensors, multiplatform approaches can overcome these limitations and improve accuracy. This study explores various techniques for monitoring volcanic clouds, identifies research gaps, and lays the foundation for future research.
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(This article belongs to the Special Issue Applications of Remote Sensing Technology in Volcano Hazard Monitoring)
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Open AccessArticle
Lightweight Super-Resolution Generative Adversarial Network for SAR Images
by
Nana Jiang, Wenbo Zhao, Hui Wang, Huiqi Luo, Zezhou Chen and Jubo Zhu
Remote Sens. 2024, 16(10), 1788; https://doi.org/10.3390/rs16101788 (registering DOI) - 18 May 2024
Abstract
Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method
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Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method introduces Depthwise Separable Convolution (DSConv) in residual blocks to compress the original Generative Adversarial Network (GAN) and uses the SeLU activation function to construct a lightweight residual module (LRM) suitable for SAR image characteristics. Furthermore, we combine the LRM with an optimized Coordinated Attention (CA) module, enhancing the lightweight network’s capability to learn feature representations. Experimental results on spaceborne SAR images demonstrate that compared to other deep generative networks focused on SAR image super-resolution reconstruction, LSRGAN achieves compression ratios of in model storage requirements and in computational resource demands. In this work, we significantly reduce the model complexity, improve the quality of spaceborne SAR images, and validate the effectiveness of the SAR image super-resolution algorithm as well as the feasibility of real-time on-board processing technology.
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(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)
Open AccessArticle
Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images
by
Gong Cheng, Zixuan Wang, Cheng Huang, Yingdong Yang, Jun Hu, Xiangsheng Yan, Yilun Tan, Lingyi Liao, Xingwang Zhou, Yufang Li, Syed Hussain, Mohamed Faisal and Huan Li
Remote Sens. 2024, 16(10), 1787; https://doi.org/10.3390/rs16101787 (registering DOI) - 18 May 2024
Abstract
Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide
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Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.
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(This article belongs to the Section Earth Observation for Emergency Management)
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Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features
by
Li Wen, Tanya Mason, Megan Powell, Joanne Ling, Shawn Ryan, Adam Bernich and Guyo Gufu
Remote Sens. 2024, 16(10), 1786; https://doi.org/10.3390/rs16101786 - 17 May 2024
Abstract
Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation,
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Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray–Darling Basin—Australia’s largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring.
Full article
(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems II)
Open AccessTechnical Note
Satellite-Based Estimation of Near-Surface NO2 Concentration in Cloudy and Rainy Areas
by
Fuliang Deng, Yijian Chen, Wenfeng Liu, Lanhui Li, Xiaojuan Chen, Pravash Tiwari and Kai Qin
Remote Sens. 2024, 16(10), 1785; https://doi.org/10.3390/rs16101785 - 17 May 2024
Abstract
Satellite-based remote sensing enables the quantification of tropospheric NO2 concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO2 observations in such meteorological conditions
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Satellite-based remote sensing enables the quantification of tropospheric NO2 concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO2 observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO2 column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO2 concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO2 measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R2 = 0.87) and precision (RMSE = 5.36 μg/m3). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO2 mapping in cloudy and rainy areas.
Full article
(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)
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