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
Rainfall Differences and Possible Causes of Similar-Track Tropical Cyclones Affected and Unaffected by Binary Tropical Cyclones (BTCs) in China
Remote Sens. 2024, 16(10), 1692; https://doi.org/10.3390/rs16101692 (registering DOI) - 9 May 2024
Abstract
Binary tropical cyclones (BTCs) typically refer to the coexistence of two tropical cyclones (TCs) within a specific distance range, often resulting in disastrous rainstorms in coastal areas of China. However, the differences in rainfall and underlying causes between BTC-influenced typhoons and general typhoons
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Binary tropical cyclones (BTCs) typically refer to the coexistence of two tropical cyclones (TCs) within a specific distance range, often resulting in disastrous rainstorms in coastal areas of China. However, the differences in rainfall and underlying causes between BTC-influenced typhoons and general typhoons remain unclear. In this article, the TC closer to the rainfall center in the BTC is referred to as the target typhoon (tTC), while the other is termed the accompanying typhoon (cmp_TC). This study compares and analyzes the rainfall differences and potential causes of tTCs and similar typhoons (sim_TC) with a comparable track but which are unaffected by BTCs from 1981 to 2020. The results show that: (1) On average, tTCs and cmp_TCs experience 18.79% heavier maximum daily rainfall compared to general TCs, with a significantly increased likelihood of rainfall ≥250 mm. (2) Given similar tracks, the average rainfall for tTCs (212.62 mm) is 30.2% heavier than that for sim_TCs (163.30 mm). (3) The analysis of potential impact factors on rainfall (translation speed, intensity, direction change) reveals that sim_TCs move at an average of 21.38 km/h, which is about 19.66% faster than the 17.87 km/h of tTCs, potentially accounting for the observed differences in rainfall. (4) Further investigation into the causes of west–east oriented BTC rainfall in the Northern Fujian (N_Fujian) region suggests that water vapor transport and slowing down of the translation speed are the possible mechanisms of BTC influence. Specifically, 80% of tTCs receive water vapor from the direction of their cmp_TC, and the steering flow for tTC is only 59.88% of that for sim_TC.
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(This article belongs to the Special Issue Remote Sensing of Precipitation Extremes)
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Comprehensive Analysis on GPS Carrier Phase under Various Cutoff Elevation Angles and Its Impact on Station Coordinates’ Repeatability
by
Sorin Nistor, Norbert-Szabolcs Suba, Aurelian Stelian Buda, Kamil Maciuk and Ahmed El-Mowafy
Remote Sens. 2024, 16(10), 1691; https://doi.org/10.3390/rs16101691 (registering DOI) - 9 May 2024
Abstract
When processing the carrier phase, the global navigation satellite system (GNSS) grants the highest precision for geodetic measurements. The analysis centers (ACs) from the International GNSS Service (IGS) provide different data such as precise clock data, precise orbits, reference frame, ionosphere and troposphere
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When processing the carrier phase, the global navigation satellite system (GNSS) grants the highest precision for geodetic measurements. The analysis centers (ACs) from the International GNSS Service (IGS) provide different data such as precise clock data, precise orbits, reference frame, ionosphere and troposphere data, as well as other geodetic products. Each individual AC has its own strategy for delivering the abovementioned products, with one of the key elements being the cutoff elevation angle. Typically, this angle is arbitrarily chosen using generic values without studying the impact of this choice on the obtained results, in particular when very precise positions are considered. This article addresses this issue. To this end, the article has two key sections, and the first is to evaluate the impact of using the two different cutoff elevation angles that are most widely used: (a) 3 degrees cutoff and (b) 10 degrees cutoff elevation angle. This analysis is completed in two major parts: (i) the analysis of the root mean square (RMS) for the carrier phase and (ii) the analysis of the station position in terms of repeatability. The second key section of the paper is a comprehensive carrier phase analysis conducted by adopting a new approach using a mean of the 25-point average RMS (A-RMS) and the single-point RMS and using an ionosphere-free linear combination. By using the ratio between the 25-point average RMS and the single-point RMS we can define the type of scatter that dominates the phase solution. The analyzed data span a one-year period. The tested GNSS stations belong to the EUREF Permanent Network (EPN) and the International GNSS Service (IGS). These comprise 55 GNSS stations, of which only 23 GNSS stations had more than 95% data availability for the entire year. The RMS and A-RMS are analyzed in conjunction with the precipitable water vapor (PWV), which shows clear signs of temporal correlation. Of the 23 GNSS stations, three stations show an increase of around 50% of the phase RMS when using a 3° cutoff elevation angle, and only four stations have a difference of 5% between the phase RMS when using both cutoff elevation angles. When using the A-RMS, there is an average improvement of 37% of the phase scatter for the 10° cutoff elevation angle, whereas for the 3° cutoff elevation angle, the improvement is around 33%. Based on studying this ratio, four stations indicate that the scatter is dominated by the stronger-than-usual dominance of long-period variations, whereas the others show short-term noise. In terms of station position repeatability, the weighted root mean square (WRMS) is used as an indicator, and the results between the differences of using a 3° and 10° cutoff elevation angle strategy show a difference of −0.16 mm for the North component, −0.21 mm for the East component and a value of −0.75 mm for the Up component, indicating the importance of using optimal cutoff angles.
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(This article belongs to the Special Issue Advanced Remote Sensing Technology in Modern Geodesy)
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Advances in Thermal Infrared Remote Sensing Technology for Geothermal Resource Detection
by
Seng Wang, Wei Xu and Tianqi Guo
Remote Sens. 2024, 16(10), 1690; https://doi.org/10.3390/rs16101690 - 9 May 2024
Abstract
This paper discusses thermal infrared (TIR) remote sensing technology applied to the delineation of geothermal resources, a significant renewable energy source. The technical characteristics and current status of TIR remote sensing is discussed and related to the integration of geological structure, geophysical data,
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This paper discusses thermal infrared (TIR) remote sensing technology applied to the delineation of geothermal resources, a significant renewable energy source. The technical characteristics and current status of TIR remote sensing is discussed and related to the integration of geological structure, geophysical data, and geochemical analyses. Also discussed are surface temperature inversion algorithms used to delineate anomalous ground-surface temperatures. Unlike traditional geophysical and geochemical exploration methods, remote sensing technology exhibits considerable advantages in terms of convenience and coverage extent. The paper addresses the major challenges and issues associated with using TIR remote sensing technology in geothermal prospecting.
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(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing II)
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Open AccessArticle
Assessing Lidar Ratio Impact on CALIPSO Retrievals Utilized for the Estimation of Aerosol SW Radiative Effects across North Africa, the Middle East, and Europe
by
Anna Moustaka, Marios-Bruno Korras-Carraca, Kyriakoula Papachristopoulou, Michael Stamatis, Ilias Fountoulakis, Stelios Kazadzis, Emmanouil Proestakis, Vassilis Amiridis, Kleareti Tourpali, Thanasis Georgiou, Stavros Solomos, Christos Spyrou, Christos Zerefos and Antonis Gkikas
Remote Sens. 2024, 16(10), 1689; https://doi.org/10.3390/rs16101689 - 9 May 2024
Abstract
North Africa, the Middle East, and Europe (NAMEE domain) host a variety of suspended particles characterized by different optical and microphysical properties. In the current study, we investigate the importance of the lidar ratio (LR) on Cloud-Aerosol Lidar with Orthogonal Polarization–Cloud-Aerosol Lidar and
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North Africa, the Middle East, and Europe (NAMEE domain) host a variety of suspended particles characterized by different optical and microphysical properties. In the current study, we investigate the importance of the lidar ratio (LR) on Cloud-Aerosol Lidar with Orthogonal Polarization–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIOP-CALIPSO) aerosol retrievals towards assessing aerosols’ impact on the Earth-atmosphere radiation budget. A holistic approach has been adopted involving collocated Aerosol Robotic Network (AERONET) observations, Radiative Transfer Model (RTM) simulations, as well as reference radiation measurements acquired using spaceborne (Clouds and the Earth’s Radiant Energy System-CERES) and ground-based (Baseline Surface Radiation Network-BSRN) instruments. We are assessing the clear-sky shortwave (SW) direct radiative effects (DREs) on 550 atmospheric scenes, identified within the 2007–2020 period, in which the primary tropospheric aerosol species (dust, marine, polluted continental/smoke, elevated smoke, and clean continental) are probed using CALIPSO. RTM runs have been performed relying on CALIOP retrievals in which the default and the DeLiAn (Depolarization ratio, Lidar ratio, and Ångström exponent)-based aerosol-speciated LRs are considered. The simulated fields from both configurations are compared against those produced when AERONET AODs are applied. Overall, the DeLiAn LRs leads to better results mainly when mineral particles are either solely recorded or coexist with other aerosol species (e.g., sea-salt). In quantitative terms, the errors in DREs are reduced by ~26–27% at the surface (from 5.3 to 3.9 W/m2) and within the atmosphere (from −3.3 to −2.4 W/m2). The improvements become more significant (reaching up to ~35%) for moderate-to-high aerosol loads (AOD ≥ 0.2).
Full article
(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval Using Satellite Sensors II: Focusing on Radiative Effects)
Open AccessArticle
Spectral Superresolution Using Transformer with Convolutional Spectral Self-Attention
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Xiaomei Liao, Lirong He, Jiayou Mao and Meng Xu
Remote Sens. 2024, 16(10), 1688; https://doi.org/10.3390/rs16101688 - 9 May 2024
Abstract
Hyperspectral images (HSI) find extensive application across numerous domains of study. Spectral superresolution (SSR) refers to reconstructing HSIs from readily available RGB images using the mapping relationships between RGB images and HSIs. In recent years, convolutional neural networks (CNNs) have become widely adopted
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Hyperspectral images (HSI) find extensive application across numerous domains of study. Spectral superresolution (SSR) refers to reconstructing HSIs from readily available RGB images using the mapping relationships between RGB images and HSIs. In recent years, convolutional neural networks (CNNs) have become widely adopted in SSR research, primarily because of their exceptional ability to extract features. However, most current CNN-based algorithms are weak in terms of extracting the spectral features of HSIs. While certain algorithms can reconstruct HSIs through the fusion of spectral and spatial data, their practical effectiveness is hindered by their substantial computational complexity. In light of these challenges, we propose a lightweight network, Transformer with convolutional spectral self-attention (TCSSA), for SSR. TCSSA comprises a CNN-Transformer encoder and a CNN-Transformer decoder, in which the convolutional spectral self-attention blocks (CSSABs) are the basic modules. Multiple cascaded encoding and decoding modules within TCSSA facilitate the efficient extraction of spatial and spectral contextual information from HSIs. The convolutional spectral self-attention (CSSA) as the basic unit of CSSAB combines CNN with self-attention in the transformer, effectively extracting both spatial local features and global spectral features from HSIs. Experimental validation of TCSSA’s effectiveness is performed on three distinct datasets: GF5 for remote sensing images along with CAVE and NTIRE2022 for natural images. The experimental results demonstrate that the proposed method achieves a harmonious balance between reconstruction performance and computational complexity.
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(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III)
Open AccessArticle
Deformation Analysis and Prediction of a High-Speed Railway Suspension Bridge under Multi-Load Coupling
by
Simin Liu, Weiping Jiang, Qusen Chen, Jian Wang, Xuyan Tan, Ruiqi Liu and Zhongtao Ye
Remote Sens. 2024, 16(10), 1687; https://doi.org/10.3390/rs16101687 - 9 May 2024
Abstract
High-speed railway suspension bridges (HSRSBs) have been constructed with the new advancements in technology. The deformation prediction for HSRSBs is essential to their safety and maintenance. The conventional prediction methods are developed for bridges without high-speed railway. Different factors, including temperature (TEMP), time
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High-speed railway suspension bridges (HSRSBs) have been constructed with the new advancements in technology. The deformation prediction for HSRSBs is essential to their safety and maintenance. The conventional prediction methods are developed for bridges without high-speed railway. Different factors, including temperature (TEMP), time delay compensation (TDC), train live load (TLL), are considered in these methods. However, the train side (TS) and train instantaneous position (TIP) have a significant impact on deformation for HSRSBs, and they are not used in the prediction. More importantly, the coupling issue among different factors is so significant that it cannot be neglected. In this study, we propose a deformation prediction model based on a backpropagation (BP) neural network. This model uses different factors as model input, including TEMP, TDC, TLL, TS, and TIP. The coupling issue is addressed by using the new model. The new model was evaluated using a dataset of 10-day field measurements. It achieves a mean absolute error (MAE) of 8.81 mm, a mean relative error (MRE) of 9.82%, and coefficient of determination (R2) of 0.94. The new model will provide high-precision prediction for deformation and will be used in the development of an early warning system.
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(This article belongs to the Special Issue Land Deformation and Engineering Structural Health Monitoring Using Geo-Spatial Technologies)
Open AccessArticle
Hyperspectral Image Mixed Noise Removal via Double Factor Total Variation Nonlocal Low-Rank Tensor Regularization
by
Yongjie Wu, Wei Xu and Liangliang Zheng
Remote Sens. 2024, 16(10), 1686; https://doi.org/10.3390/rs16101686 - 9 May 2024
Abstract
A hyperspectral image (HSI) is often corrupted by various types of noise during image acquisition, e.g., Gaussian noise, impulse noise, stripes, deadlines, and more. Thus, as a preprocessing step, HSI denoising plays a vital role in many subsequent tasks. Recently, a variety of
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A hyperspectral image (HSI) is often corrupted by various types of noise during image acquisition, e.g., Gaussian noise, impulse noise, stripes, deadlines, and more. Thus, as a preprocessing step, HSI denoising plays a vital role in many subsequent tasks. Recently, a variety of mixed noise removal approaches have been developed for HSI, and the methods based on spatial–spectral double factor and total variation (DFTV) regularization have achieved comparable performance. Additionally, the nonlocal low-rank tensor model (NLR) is often employed to characterize spatial nonlocal self-similarity (NSS). Generally, fully exploring prior knowledge can improve the denoising performance, but it significantly increases the computational cost when the NSS prior is employed. To solve this problem, this article proposes a novel DFTV-based NLR regularization (DFTVNLR) model for HSI mixed noise removal. The proposed model employs low-rank tensor factorization (LRTF) to characterize the spectral global low-rankness (LR), introduces 2-D and 1-D TV constraints on double-factor to characterize the spatial and spectral local smoothness (LS), respectively. Meanwhile, the NLR is applied to the spatial factor to characterize the NSS. Then, we developed an algorithm based on proximal alternating minimization (PAM) to solve the proposed model effectively. Particularly, we effectively controlled the computational cost from two aspects, namely taking small-sized double factor as regularization object and putting the time-consuming NLR model before the main loop with fewer iterations to solve it independently. Finally, considerable experiments on simulated and real noisy HSI substantiate that the proposed method is superior to the related state-of-the-art methods in balancing the denoising effect and speed.
Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
Open AccessArticle
High-Speed Spatial–Temporal Saliency Model: A Novel Detection Method for Infrared Small Moving Targets Based on a Vectorized Guided Filter
by
Aersi Aliha, Yuhan Liu, Guangyao Zhou and Yuxin Hu
Remote Sens. 2024, 16(10), 1685; https://doi.org/10.3390/rs16101685 - 9 May 2024
Abstract
Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and
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Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and are easily buried by noise and difficult to detect. A novel high-speed spatial–temporal saliency model (HS-STSM) based on a guided filter (GF) is proposed, which innovatively introduces GF into IR target detection to extract the local anisotropy saliency in the spatial domain, and substantially suppresses the background region as well as the bright clutter false alarms present in the background. Moreover, the proposed model extracts the motion saliency of the target in the temporal domain through vectorization of IR image sequences. Additionally, the proposed model significantly improves the detection efficiency through a vectorized filtering process and effectively suppresses edge components in the background by integrating a prior weight. Experiments conducted on five real infrared image sequences demonstrate the superior performance of the model compared to existing algorithms in terms of the detection rate, noise suppression, real-time processing, and robustness to the background.
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Open AccessArticle
A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing
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Chunshan Li, Yushuai Yang, Xiaofei Yang, Dianhui Chu and Weijia Cao
Remote Sens. 2024, 16(10), 1684; https://doi.org/10.3390/rs16101684 - 9 May 2024
Abstract
The efficient and timely identification of oil spill areas is crucial for ocean environmental protection. Synthetic aperture radar (SAR) is widely used in oil spill detection due to its all-weather monitoring capability. Meanwhile, existing deep learning-based oil spill detection methods mainly rely on
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The efficient and timely identification of oil spill areas is crucial for ocean environmental protection. Synthetic aperture radar (SAR) is widely used in oil spill detection due to its all-weather monitoring capability. Meanwhile, existing deep learning-based oil spill detection methods mainly rely on the classical U-Net framework and have achieved impressive results. However, SAR images exhibit high noise, blurry boundaries, and irregular shapes of target areas, as well as speckles and shadows, which lead to the loss of performance in existing algorithms. In this paper, we propose a novel network architecture to achieve more precise segmentation of oil spill areas by reintroducing rich semantic contextual information before obtaining the final segmentation mask. Specifically, the proposed architecture can re-fuse feature maps from different levels at the decoder end. We design a multi-convolutional layer (MCL) module to extract basic feature information from SAR images, and a feature extraction module (FEM) module further extracts and fuses feature maps generated by the U-Net decoder at different levels. Through these operations, the network can learn rich global and local contextual information, enable sufficient interaction of feature information at different stages, enhance the model’s contextual awareness, and improve its ability to recognize complex textures and blurry boundaries, thereby enhancing the segmentation accuracy of SAR images. Compared to many U-Net based segmentation networks, our method shows promising results and achieves state-of-the-art performance on multiple evaluation metrics.
Full article
(This article belongs to the Special Issue Quantitative Inversion and Validation of Satellite Remote Sensing Products)
Open AccessArticle
Near-Real Prediction of Earthquake-Triggered Landslides on the Southeastern Margin of the Tibetan Plateau
by
Aomei Zhang, Xianmin Wang, Chong Xu, Qiyuan Yang, Haixiang Guo and Dongdong Li
Remote Sens. 2024, 16(10), 1683; https://doi.org/10.3390/rs16101683 - 9 May 2024
Abstract
Earthquake-triggered landslides (ETLs) feature large quantities, extensive distributions, and enormous losses to human lives and critical infrastructures. Near-real spatial prediction of ETLs can rapidly predict the locations of coseismic landslides just after a violent earthquake and is a vital technical support for emergency
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Earthquake-triggered landslides (ETLs) feature large quantities, extensive distributions, and enormous losses to human lives and critical infrastructures. Near-real spatial prediction of ETLs can rapidly predict the locations of coseismic landslides just after a violent earthquake and is a vital technical support for emergency response. However, near-real prediction of ETLs has always been a great challenge with relatively low accuracy. This work proposes an ensemble prediction model of EnPr by integrating machine learning tree models and a deep learning convolutional neural network. EnPr exhibits relatively strong prediction and generalization performance and achieves relatively accurate prediction of ETLs. Six great seismic events occurring from 2008 to 2022 on the southeastern margin of the Tibetan Plateau are selected to conduct ETL prediction. In a chronological order, the 2008 Ms 8.0 Wenchuan, 2010 Ms 7.1 Yushu, 2013 Ms 7.0 Lushan, and 2014 Ms 6.5 Ludian earthquakes are employed for model training and learning. The 2017 Ms 7.0 Jiuzhaigou and 2022 Ms 6.1 Lushan earthquakes are adopted for ETL prediction. The prediction accuracy merits of ACC and AUC attain 91.28% and 0.85, respectively, for the Jiuzhaigou earthquake. The values of ACC and AUC achieve 93.78% and 0.88, respectively, for the Lushan earthquake. The proposed EnPr algorithm outperforms the algorithms of XGBoost, random forest (RF), extremely randomized trees (ET), convolutional neural network (CNN), and Transformer. Moreover, this work reveals that seismic intensity, high and steep relief, pre-seismic fault tectonics, and pre-earthquake road construction have played significant roles in coseismic landslide occurrence and distribution. The EnPr model uses globally accessible open datasets and can therefore be used worldwide for new large seismic events in the future.
Full article
(This article belongs to the Special Issue Advancements in Remote Sensing and Artificial Intelligence for Geohazards)
Open AccessTechnical Note
A Signal Matching Method of In-Orbit Calibration of Altimeter in Tracking Mode Based on Transponder
by
Qingyu Fang, Wei Guo, Caiyun Wang, Peng Liu, Te Wang, Sijia Han, Shijie Yang, Yufei Zhang, Hailong Peng, Chaofei Ma and Bo Mu
Remote Sens. 2024, 16(10), 1682; https://doi.org/10.3390/rs16101682 - 9 May 2024
Abstract
In this paper, a matching method for altimeter and transponder signals in Sub-optimal Maximum Likelihood Estimate (SMLE) tracking mode is proposed. In the in-orbit calibration of the altimeter in SMLE tracking mode using the reconstructive transponder, it is necessary to separate the forwarding
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In this paper, a matching method for altimeter and transponder signals in Sub-optimal Maximum Likelihood Estimate (SMLE) tracking mode is proposed. In the in-orbit calibration of the altimeter in SMLE tracking mode using the reconstructive transponder, it is necessary to separate the forwarding signal from the ground echo signal. At the same time, the fluctuations in the received signal of the altimeter, which are caused by the forwarding signal of the transponder, can be eliminated. The transponder generates a bias when measuring the arrival time of the transmitting signal from the altimeter and embeds this bias in both the transponder-recorded data and the altimeter-recorded data. Therefore, the two sets of data have one-to-one correspondence, and they are superimposed using the sliding sum method. Moreover, the distance between the altimeter and the transponder is a parabolic geometric relationship, and the outliers are eliminated by the fitting error minimization decision, and the transponder signal is separated from the ground echo. The final altimeter transmitting–receiving signal path is obtained. Furthermore, the principles underlying this method can be used for any transponder that can adjust the response signal delay during calibration.
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(This article belongs to the Topic Radar Signal and Data Processing with Applications)
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Open AccessArticle
Radar-Based Precipitation Nowcasting Based on Improved U-Net Model
by
Youwei Tan, Ting Zhang, Leijing Li and Jianzhu Li
Remote Sens. 2024, 16(10), 1681; https://doi.org/10.3390/rs16101681 - 9 May 2024
Abstract
Rainfall nowcasting is the basis of extreme rainfall monitoring, flood prevention, and water resource scheduling. Based on the structural features of the U-Net model, we proposed the Double Recurrent Residual Attention Gates U-Net (DR2A-UNet) deep-learning model to carry out radar echo extrapolation. The
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Rainfall nowcasting is the basis of extreme rainfall monitoring, flood prevention, and water resource scheduling. Based on the structural features of the U-Net model, we proposed the Double Recurrent Residual Attention Gates U-Net (DR2A-UNet) deep-learning model to carry out radar echo extrapolation. The model was trained with mean square error (MSE) and balanced mean square error (BMSE) as loss functions, respectively. The dynamic Z-R relationship was applied for quantitative rainfall estimation. The reference U-Net model, U-Net++, and the ConvLSTM were used as control experiments to carry out radar echo extrapolation. The results showed that the model trained by BMSE had better extrapolation. For 1 h lead time, the rainfall nowcasted by each model could reflect the actual rainfall process. DR2A-UNet performed significantly better than other models for intense rainfall, with a higher extrapolation accuracy for echo intensity and variability processes. At the 2 h lead time, the nowcast accuracy of each model was significantly reduced, but the echo extrapolation and rainfall nowcasting of DR2A-UNet were better.
Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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Open AccessArticle
Analysis of Meteorological Drivers of Taihu Lake Algal Blooms over the Past Two Decades and Development of a VOCs Emission Inventory for Algal Bloom
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Zihang Liao, Shun Lv, Chenwu Zhang, Yong Zha, Suyang Wang and Min Shao
Remote Sens. 2024, 16(10), 1680; https://doi.org/10.3390/rs16101680 - 9 May 2024
Abstract
Cyanobacterial blooms represent a common environmental issue in aquatic systems, and these blooms bring forth numerous hazards, with the generation of volatile organic compounds (VOCs) being one of them. Global climate change has led to alterations in various climatic factors affecting algal growth,
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Cyanobacterial blooms represent a common environmental issue in aquatic systems, and these blooms bring forth numerous hazards, with the generation of volatile organic compounds (VOCs) being one of them. Global climate change has led to alterations in various climatic factors affecting algal growth, indirectly impacting the quantity of VOCs released by algae. With advancements in remote sensing technology, exploration of the spatiotemporal distributions of algae in large water bodies has become feasible. This study focuses on Taihu Lake, characterized by frequent occurrences of cyanobacterial blooms. Utilizing MODIS satellite imagery from 2001 to 2020, we analyzed the spatiotemporal characteristics of cyanobacterial blooms in Taihu Lake and its subregions. Employing the LightGBM machine learning model and the (SHapley Additive exPlanations) SHAP values, we quantitatively analyzed the major meteorological drivers influencing cyanobacterial blooms in each region. VOC-related source spectra and emission intensities from cyanobacteria in Taihu Lake are collected based on the literature review and are used to compile the first inventory of VOC emissions from blue-green algae blooms in Taihu Lake. The results indicate that since the 21st century, the situation of cyanobacterial blooms in Taihu Lake has continued to deteriorate with increasing variability. The relative impact of meteorological factors varies across different regions, but temperature consistently shows the highest sensitivity in all areas. The VOCs released from the algal blooms increase with the proliferation of the blooms, posing a continuous threat to the atmospheric environment of the surrounding cities. This study aims to provide a scientific basis for further improvement of air quality in urban areas adjacent to large lakes.
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(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)
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Open AccessArticle
A Dual-FSM GI LiDAR Imaging Control Method Based on Two-Dimensional Flexible Turntable Composite Axis Tracking
by
Yu Cao, Meilin Xie, Haitao Wang, Wei Hao, Min Guo, Kai Jiang, Lei Wang, Shan Guo and Fan Wang
Remote Sens. 2024, 16(10), 1679; https://doi.org/10.3390/rs16101679 - 9 May 2024
Abstract
In this study, a tracking and pointing control system with a dual-FSM (fast steering mirror) two-dimensional flexible turntable composite axis is proposed. It is applied to the target-tracking accuracy control in a GI LiDAR (ghost imaging LiDAR) system. Ghost imaging is a multi-measurement
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In this study, a tracking and pointing control system with a dual-FSM (fast steering mirror) two-dimensional flexible turntable composite axis is proposed. It is applied to the target-tracking accuracy control in a GI LiDAR (ghost imaging LiDAR) system. Ghost imaging is a multi-measurement imaging method; the dual-FSM GI LiDAR tracking and pointing imaging control system proposed in this study mainly solves the problems of the high-resolution remote sensing imaging of high-speed moving targets and various nonlinear disturbances when this technology is transformed into practical applications. Addressing the detrimental effects of nonlinear disturbances originating from internal flexible mechanisms and assorted external environmental factors on motion control’s velocity, stability, and tracking accuracy, a nonlinear active disturbance rejection control (NLADRC) method based on artificial neural networks is advanced. Additionally, to overcome the limitations imposed by receiving aperture constraints in GI LiDAR systems, a novel optical path design for the dual-FSM GI LiDAR tracking and imaging system is put forth. The implementation of the described methodologies culminated in the development of a dual-FSM GI LiDAR tracking and imaging system, which, upon thorough experimental validation, demonstrated significant improvements. Notably, it achieved an improvement in the coarse tracking accuracy from 193.29 μrad (3σ) to 87.21 μrad (3σ) and enhanced the tracking accuracy from 10.1 μrad (σ) to 1.5 μrad (σ) under specified operational parameters. Furthermore, the method notably diminished the overshoot during the target capture process from 28.85% to 12.8%, concurrently facilitating clear recognition of the target contour. This research contributes significantly to the advancement of GI LiDAR technology for practical application, showcasing the potential of the proposed control and design strategies in enhancing system performance in the face of complex disturbances.
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(This article belongs to the Special Issue Remote Sensing Cross-Modal Research: Algorithms and Practices)
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Open AccessArticle
Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment
by
Lijie Chen, Zhe Wang, Xiaogang Ma, Jingwen Zhao, Xiang Que, Jinfu Liu, Ruohai Chen and Yimin Li
Remote Sens. 2024, 16(10), 1678; https://doi.org/10.3390/rs16101678 - 9 May 2024
Abstract
With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially
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With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially those of wetlands with strong land use–natural resource conservation conflict. Moreover, there is a lack of well-established indicators for evaluating the health of wetland carbon stocks. To address this issue, we proposed a novel framework for the safety assessment of wetland carbon stocks using the Super Slack-Based Measure (Super-SBM), and we then conducted an empirical study on the Quanzhou Bay Estuary Wetland (QBEW). This framework integrates the unexpected output indicator (i.e., carbon emissions), the expected output indicators, including the GDP per capita and carbon stock estimates calculated via machine learning (ML)-based remote sensing inversion, and the input indicators, such as environmental governance investigations, climate conditions, socio-economic activities, and resource utilization. The results show that the annual average safety assessment for carbon pools in the QBEW was a meager 0.29 in 2015, signaling a very poor state, likely due to inadequate inputs or excessive unexpected outputs. However, there has been a substantial improvement since then, as evidenced by the fact that all the safety assessments have exceeded the threshold of 1 from 2018 onwards, reflecting a transition to a “weakly effective” status within a safe and acceptable range. Moreover, our investigation employing the Super-SBM model to calculate the “slack variables” yielded valuable insights into optimization strategies. This research advances the field by establishing a safety measurement framework for wetland carbon pools that leverages efficiency assessment methods, thereby offering a quantitative safeguard mechanism that supports the achievement of the “3060” dual-carbon target.
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(This article belongs to the Special Issue Incorporating Knowledge-Infused Approaches in Remote Sensing)
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Open AccessArticle
Consistency of Aerosol Optical Properties between MODIS Satellite Retrievals and AERONET over a 14-Year Period in Central–East Europe
by
Lucia-Timea Deaconu, Alexandru Mereuță, Andrei Radovici, Horațiu Ioan Ștefănie, Camelia Botezan and Nicolae Ajtai
Remote Sens. 2024, 16(10), 1677; https://doi.org/10.3390/rs16101677 - 9 May 2024
Abstract
Aerosols influence Earth’s climate by interacting with radiation and clouds. Remote sensing techniques aim to enhance our understanding of aerosol forcing using ground-based and satellite retrievals. Despite technological advancements, challenges persist in reducing uncertainties in satellite remote sensing. Our study examines retrieval biases
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Aerosols influence Earth’s climate by interacting with radiation and clouds. Remote sensing techniques aim to enhance our understanding of aerosol forcing using ground-based and satellite retrievals. Despite technological advancements, challenges persist in reducing uncertainties in satellite remote sensing. Our study examines retrieval biases in MODIS sensors on Terra and Aqua satellites compared to AERONET ground-based measurements. We assess their performance and the correlation with the AERONET aerosol optical depth (AOD) using 14 years of data (2010–2023) from 29 AERONET stations across 10 Central–East European countries. The results indicate discrepancies between MODIS Terra and Aqua retrievals: Terra overestimates the AOD at 16 AERONET stations, while Aqua underestimates the AOD at 21 stations. The examination of temporal biases in the AOD using the calculated estimated error (ER) between AERONET and MODIS retrievals reveals a notable seasonality in coincident retrievals. Both sensors show higher positive AOD biases against AERONET in spring and summer compared to fall and winter, with few ER values for Aqua indicating poor agreement with AERONET. Seasonal variations in correlation strength were noted, with significant improvements from winter to summer (from R2 of 0.58 in winter to R2 of 0.76 in summer for MODIS Terra and from R2 of 0.53 in winter to R2 of 0.74 in summer for MODIS Aqua). Over the fourteen-year period, monthly mean aerosol AOD trends indicate a decrease of −0.00027 from AERONET retrievals and negative monthly mean trends of the AOD from collocated MODIS Terra and Aqua retrievals of −0.00023 and −0.00025, respectively. An aerosol classification analysis showed that mixed aerosols comprised over 30% of the total aerosol composition, while polluted aerosols accounted for more than 22%, and continental aerosols contributed between 22% and 24%. The remaining 20% consists of biomass-burning, dust, and marine aerosols. Based on the aerosol classification method, we computed the bias between the AERONET AE and MODIS AE, which showed higher AE values for AERONET retrievals for a mixture of aerosols and biomass burning, while for marine aerosols, the MODIS AE was larger and for dust the results were inconclusive.
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(This article belongs to the Special Issue Remote Sensing of Aerosols, Planetary Boundary Layer, and Clouds)
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Open AccessArticle
A Deep Learning Classification Scheme for PolSAR Image Based on Polarimetric Features
by
Shuaiying Zhang, Lizhen Cui, Zhen Dong and Wentao An
Remote Sens. 2024, 16(10), 1676; https://doi.org/10.3390/rs16101676 - 9 May 2024
Abstract
Polarimetric features extracted from polarimetric synthetic aperture radar (PolSAR) images contain abundant back-scattering information about objects. Utilizing this information for PolSAR image classification can improve accuracy and enhance object monitoring. In this paper, a deep learning classification method based on polarimetric channel power
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Polarimetric features extracted from polarimetric synthetic aperture radar (PolSAR) images contain abundant back-scattering information about objects. Utilizing this information for PolSAR image classification can improve accuracy and enhance object monitoring. In this paper, a deep learning classification method based on polarimetric channel power features for PolSAR is proposed. The distinctive characteristic of this method is that the polarimetric features input into the deep learning network are the power values of polarimetric channels and contain complete polarimetric information. The other two input data schemes are designed to compare the proposed method. The neural network can utilize the extracted polarimetric features to classify images, and the classification accuracy analysis is employed to compare the strengths and weaknesses of the power-based scheme. It is worth mentioning that the polarized characteristics of the data input scheme mentioned in this article have been derived through rigorous mathematical deduction, and each polarimetric feature has a clear physical meaning. By testing different data input schemes on the Gaofen-3 (GF-3) PolSAR image, the experimental results show that the method proposed in this article outperforms existing methods and can improve the accuracy of classification to a certain extent, validating the effectiveness of this method in large-scale area classification.
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(This article belongs to the Special Issue Remote Sensing Image Classification and Semantic Segmentation)
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Open AccessReview
Marine Infrastructure Detection with Satellite Data—A Review
by
Robin Spanier and Claudia Kuenzer
Remote Sens. 2024, 16(10), 1675; https://doi.org/10.3390/rs16101675 - 9 May 2024
Abstract
A rapid development of marine infrastructures can be observed along the global coasts. Offshore wind farms, oil and gas platforms, artificial islands, aquaculture, and more, are being constructed without a proper quantification of these human activities. Therefore, effective monitoring is required to maintain
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A rapid development of marine infrastructures can be observed along the global coasts. Offshore wind farms, oil and gas platforms, artificial islands, aquaculture, and more, are being constructed without a proper quantification of these human activities. Therefore, effective monitoring is required to maintain transparency towards environmental standards, marine resource management, inventorying objects, and global security. This study reviews remote sensing-based approaches to offshore infrastructure detection over the past 12 years. We analyzed 89 studies from over 30 scientific journals, highlighting spatial and temporal trends, methodological approaches, and regional and thematic research foci. Our results show a significant increase in research interest, especially since 2019. Asia, and especially China, is the predominant focus region in terms of first authorship, funding, and areas of investigation. Aquaculture is the most studied infrastructure, followed by platforms, offshore wind farms, and artificial islands. Gaofen, Sentinel, and Landsat are the most used satellite sensors for detection. The apparent shift towards automated detection methods, especially Deep Learning algorithms, reflects advances in computer vision. This study highlights the key role of earth observation in the field of off-shore infrastructure detection, which can contribute towards outlining effective monitoring practices for marine activities, as well as highlighting important knowledge gaps.
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(This article belongs to the Section Ocean Remote Sensing)
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Open AccessFeature PaperArticle
Determining Riverine Surface Roughness at Fluvial Mesohabitat Level and Its Influence on UAV-Based Thermal Imaging Accuracy
by
Johannes Kuhn, Joachim Pander, Luis Habersetzer, Roser Casas-Mulet and Juergen Geist
Remote Sens. 2024, 16(10), 1674; https://doi.org/10.3390/rs16101674 - 9 May 2024
Abstract
Water surface roughness (SR) is a highly relevant parameter governing data reliability in remote sensing applications, yet lacking appropriate methodology in riverine habitats. In order to assess thermal accuracy linked to SR of thermal imaging derived from an unmanned aerial vehicle (UAV), we
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Water surface roughness (SR) is a highly relevant parameter governing data reliability in remote sensing applications, yet lacking appropriate methodology in riverine habitats. In order to assess thermal accuracy linked to SR of thermal imaging derived from an unmanned aerial vehicle (UAV), we developed the SR Measurement Device (SRMD). The SRMD uses the concept of in situ quantification of wave frequency and wave amplitude. Data of nine installed SRMDs in four different fluvial mesohabitat classes presented a range of 0 to 47 waves per 30 s and an amplitude range of 0 to 6 cm. Even subtle differences between mesohabitat classes run, riffle, and no-/low-flow still and pool areas could be detected with the SRMD. However, SR revealed no significant influence on the accuracy of thermal infrared (TIR) imagery data in our study case. Overall, the presented device expands existing methods of riverine habitat assessments and has the potential to produce highly relevant data of SR for various ecological and technical applications, ranging from remote sensing of surface water and habitat quality characterizations to bank stability and erosion risk assessments.
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(This article belongs to the Special Issue Remote Sensing and GIS in Freshwater Environments)
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Open AccessArticle
Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands
by
Qian Li, Che Wang, Lu An and Minghu Ding
Remote Sens. 2024, 16(10), 1673; https://doi.org/10.3390/rs16101673 - 9 May 2024
Abstract
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The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of
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The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of this uncertainty. However, there is a scarcity of research in this field. Therefore, in this study, we computed surface melt in the vicinity of Automatic Weather Stations (AWSs) by employing Defense Meteorological Satellite Program (DMSP) Ka-band data and Soil Moisture and Ocean Salinity (SMOS) satellite L-band data and extracted SGL pixels by utilizing Sentinel-2 data. A comparison between surface melt results derived from AWS air temperature estimates and those obtained with remote sensing inversion in the two different bands was conducted for sites below the mean snowline elevation during the summers of 2016 to 2020. Compared with sites with no SGLs, the commission error (CO) of DMSP morning and evening data at sites where these water bodies were present increased by 36% and 30%, respectively, and the number of days with CO increased by 12 and 3 days, respectively. The omission error (OM) of SMOS morning and evening data increased by 33% and 32%, respectively, and the number of days with OM increased by 17 and 21 days, respectively. Identifying the source of error is a prerequisite for the improvement of surface melt algorithms, for which this study provides a basis.
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