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Remote Sensing
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Marine Disaster Monitoring Using Satellites
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Marine Disaster Monitoring Using Satellites

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 13959

Special Issue Editors

Prof. Dr. Weizeng Shao

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Guest Editor
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
Interests: SAR; tropical cyclone; numeric modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Juhong Zou

E-Mail Website
Guest Editor
National Satellite Ocean Application Service, No.8 Dahuisi Road, Beijing 100081, China
Interests: scatterometry; microwave remote sensing; satellite oceanography
Dr. Ferdinando Nunziata

E-Mail Website
Guest Editor
Dipartimento di Ingegneria, Università di Napoli Parthenope, 80133 Napoli, NA, Italy
Interests: synthetic aperture radar for sea observation; microwave radiometry; sea surface scattering; GNSS reflectometry
Special Issues, Collections and Topics in MDPI journals
Dr. Gang Zheng

E-Mail Website
Guest Editor
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, No.36 Baochubei Road, Hangzhou 310012, China
Interests: satellite oceanography; microwave remote sensing; AI oceanography; tropical cyclone remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coastal zones worldwide have developed fast in recent decades, with dramatic increases in the coastal population and human marine activities. However, marine disasters seriously endanger human lives, severely damage the ocean environment, and cause substantial economic losses. Marine monitoring under extreme sea states is essential to disaster evaluation and development predictions and, thus, to effective relief and prevention measures. Compared with traditional moored observation, remote sensing techniques have extensive spatial coverage and other unique merits critical to marine disaster monitoring. In the last 20 years, China and other countries have launched various radar satellites, ocean dynamic environment satellites, ocean color satellites, etc. These ocean observation satellites play a significant role in our better understanding of disaster mechanisms and the reduction of disaster-induced damages. The intrinsic complexity of marine disasters and the growing requirement of disaster monitoring mean we need to make continuous efforts to improve methodology and data products. This Special Issue aims at presenting and consolidating state-of-the-art research in the development of Chinese and other satellite applications in marine disaster monitoring. The Special Issue welcomes original and novel papers on methods, techniques, data, applications, etc., for monitoring marine disasters. The topics include but are not limited to ocean dynamical, ecological disasters and marine pollution using Chinese and other operational satellites.

We are looking forward to receiving your paper.

Prof. Dr. Weizeng Shao
Dr. Juhong Zou
Prof. Dr. Ferdinando Nunziata
Dr. Gang Zheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Microwave and ocean color
  • Dynamics target detection
  • Marine disaster
  • Modeling

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Published Papers (5 papers)

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Research

19 pages, 5371 KiB  
Article
Marine Heatwaves in the South China Sea: Tempo-Spatial Pattern and Its Association with Large-Scale Circulation
by Yan Li, Guoyu Ren, Qingyuan Wang, Lin Mu and Qianru Niu
Remote Sens. 2022, 14(22), 5829; https://doi.org/10.3390/rs14225829 - 17 Nov 2022
Cited by 13 | Viewed by 2612
Abstract
A marine heatwave (MHW) can significantly harm marine ecosystems and fisheries. Based on a remotely sensed sea surface temperature (SST) product, this study investigated MHWs behaviors in the South China Sea (SCS) throughout the warm season (May to September) from 1982 to 2020. [...] Read more.
A marine heatwave (MHW) can significantly harm marine ecosystems and fisheries. Based on a remotely sensed sea surface temperature (SST) product, this study investigated MHWs behaviors in the South China Sea (SCS) throughout the warm season (May to September) from 1982 to 2020. The distributions of the three MHW indices used in this study showed significant latitudinal variations: more frequent, longer, and more intense MHWs appear in the northern SCS, and less frequent, shorter, and weaker MHWs appear in the southern SCS. Using the empirical orthogonal function (EOF) method, we found that the first leading modes of the three MHW indices account for more than half of the total variance. The first leading modes reveal uniform anomalies throughout the SCS, with the maximum in the deep central portion and its surroundings. Their corresponding time series showed significant interdecadal variations, with a turning point around 2009. Since 2010, the SCS has seen an increase in the frequency, length, and severity of MHWs. The incidence of MHWs has been linked to the presence of stable near-surface anticyclonic anomalies, which reduced cloud cover and increased solar radiation. This abnormal pattern was usually accompanied by the intensification and westward shift of the western North Pacific subtropical high (WNPSH). The findings imply that MHWs in the SCS may be predictable on interannual and decadal scales. Full article
(This article belongs to the Special Issue Marine Disaster Monitoring Using Satellites)
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17 pages, 3630 KiB  
Article
An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea
by Jifeng Qi, Chuanyu Liu, Jianwei Chi, Delei Li, Le Gao and Baoshu Yin
Remote Sens. 2022, 14(13), 3207; https://doi.org/10.3390/rs14133207 - 4 Jul 2022
Cited by 13 | Viewed by 3027
Abstract
Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface [...] Read more.
Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface data and Argo data in the South China Sea (SCS). The input data include sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW), and geographic information (including longitude and latitude). We select three stable machine learning models, namely, extreme gradient boosting (XGBoost), RandomForest and light gradient boosting machine (LightGBM) as our benchmark models, and then use an artificial neural network (ANN) technique to combine outputs from the three individual models. The proposed Ens-ML model using sea surface data only by SSH, SST, SSS, and SSW performs less satisfactorily than that considering the contribution of geographical information, indicating that the geographical information is essential to estimate the OSTS accurately. The estimated OSTS from the Ens-ML model are compared with Argo data. The results show that the proposed Ens-ML model can accurately estimate the OSTS (upper 1000 m) in the SCS, which is relatively more accurate and precise than the individual models. The performance of the Ens-ML model also varies with season, and better estimation is obtained in winter, which is probably due to stronger mixing and weaker stratification. This study shows the great potential and advantage of the multi-model ensemble of machine learning algorithm for the ocean’s interior information retrieving, showing great potential in expanding the scope of ocean observations. Full article
(This article belongs to the Special Issue Marine Disaster Monitoring Using Satellites)
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15 pages, 1411 KiB  
Communication
Comprehensive Study on the Tropospheric Wet Delay and Horizontal Gradients during a Severe Weather Event
by Victoria Graffigna, Manuel Hernández-Pajares, Francisco Azpilicueta and Mauricio Gende
Remote Sens. 2022, 14(4), 888; https://doi.org/10.3390/rs14040888 - 12 Feb 2022
Cited by 6 | Viewed by 2344
Abstract
GNSS meteorology is today one of the most growing technologies to monitor severe weather events. In this paper, we present the usage of 160 GPS reference stations over the period of 14 days to monitor and track Hurricane Harvey, which struck Texas in [...] Read more.
GNSS meteorology is today one of the most growing technologies to monitor severe weather events. In this paper, we present the usage of 160 GPS reference stations over the period of 14 days to monitor and track Hurricane Harvey, which struck Texas in August 2017. We estimate the Zenith Wet Delay (ZWD) and the tropospheric gradients with 30 s interval using TOMION v2 software and carry out the processing in Precise Point Positioning (PPP) mode. We study the relationship of these parameters with atmospheric variables extracted from Tropical Rainfall Measuring Mission (TRMM) satellite mission and climate reanalysis model ERA5. This research finds that the ZWD shows patterns related to the rainfall rate and to the location of the hurricane. We also find that the tropospheric gradients are correlated with water vapor gradients before and after the hurricane, and with the wind and the pressure gradients only after the hurricane. This study also shows a new finding regarding the spectral distribution of the gradients, with a clear diurnal period present, which is also found on the ZWD itself. This kind of study approaches the GNSS meteorology to the increasing requirements of meteorologist in terms of monitoring severe weather events. Full article
(This article belongs to the Special Issue Marine Disaster Monitoring Using Satellites)
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11 pages, 5021 KiB  
Communication
Analyzing Sea Surface Wind Distribution Characteristics of Tropical Cyclone Based on Sentinel-1 SAR Images
by Yuan Gao, Jie Zhang, Changlong Guan and Jian Sun
Remote Sens. 2021, 13(22), 4501; https://doi.org/10.3390/rs13224501 - 9 Nov 2021
Cited by 4 | Viewed by 2045
Abstract
The spaceborne synthetic aperture radar (SAR) cross-polarization signal remains sensitive to sea surface wind speed with high signal-to-noise ratio under tropical cyclone (TC) conditions. It has the capability of observing TC intensity and size information over the ocean with large coverage and high [...] Read more.
The spaceborne synthetic aperture radar (SAR) cross-polarization signal remains sensitive to sea surface wind speed with high signal-to-noise ratio under tropical cyclone (TC) conditions. It has the capability of observing TC intensity and size information over the ocean with large coverage and high spatial resolution. In this paper, TC wind distribution characteristics were studied based on SAR images. We collected 41 Sentinel-1A/B cross-polarization images covering TC eye, which were acquired between 2016 and 2020. For each case, sea surface wind speeds were retrieved by the modified MS1A model in a spatial resolution of 1 km. After deriving the value and location of maximum wind speed, wind fields were simulated symmetrically within a 200 km radius. Two new methodologies were proposed to calculate the decay index and the symmetry index based on the retrieved and simulated wind fields. Characteristics of the two indices were analyzed with respect to maximum wind. In addition, the maximum and averaged wind speeds of the right, back and left side of the motion direction were compared with TC intensity and storm motion speed. Statistical results indicate that right-side wind speed is the strongest for maximum and average, the wind difference between the left and right side is dependent on storm motion speed. Full article
(This article belongs to the Special Issue Marine Disaster Monitoring Using Satellites)
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18 pages, 7767 KiB  
Article
Monitoring the Dissipation of the Floating Green Macroalgae Blooms in the Yellow Sea (2007–2020) on the Basis of Satellite Remote Sensing
by Deyu An, Dingfeng Yu, Xiangyang Zheng, Yan Zhou, Ling Meng and Qianguo Xing
Remote Sens. 2021, 13(19), 3811; https://doi.org/10.3390/rs13193811 - 23 Sep 2021
Cited by 11 | Viewed by 2649
Abstract
Large scale green macroalgae blooms (MABs) caused by Ulva prolifera have occurred regularly in the Yellow Sea since 2007. In the MAB dissipation phase, the landing or sinking and decomposition of U. prolifera would alter the physical-chemical environment of seawater and [...] Read more.
Large scale green macroalgae blooms (MABs) caused by Ulva prolifera have occurred regularly in the Yellow Sea since 2007. In the MAB dissipation phase, the landing or sinking and decomposition of U. prolifera would alter the physical-chemical environment of seawater and cause ecological, environmental, and economic problems. To understand MAB dissipation features, we used multiple sensors to analyze the spatiotemporal variation of the MAB dissipation phase in the southern Yellow Sea. The results show the variation in the daily dissipation rate (DR) was inconsistent from year to year. Based on the DR variation, a simple method of estimating MAB dissipation days was proposed for the first time. Verification results of the method, from 2018 to 2020, showed the estimated dissipation days were relatively consistent with the results obtained by remote sensing imagery. From 2007 to 2020, the order in which macroalgae landed in the coastal cities of Shandong Peninsula can be roughly divided into two types. In one type, the macroalgae landed first in Rizhao, followed by Qingdao, Rushan, and Haiyang. In the other type, they landed in the reverse order. The MABs annual distribution density showed significant differences in the southern Yellow Sea. These results provided a basis for evaluating the MABs’ impact on marine ecology and formulating the green-tide prevention and control strategies. Full article
(This article belongs to the Special Issue Marine Disaster Monitoring Using Satellites)
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