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Special Issue "Remote Sensing Applications in Ocean Observation II"

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

Deadline for manuscript submissions: 31 July 2023 | Viewed by 2102

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Special Issue Editor

Prof. Dr. Chung-Ru Ho

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Guest Editor

Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung 202301, Taiwan
Interests: remote sensing; physical oceanography; global change; satellite oceanography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the launch of Seasat, TIROS-N, and Nimbus-7 satellites equipped with ocean observation sensors in 1978, there has been a new era of studying ocean from satellites. Today, ocean remote sensing data observed from satellites have been widely used in oceanographic studies. Drones and coast-based sensors are also used to observe ocean phenomena. Therefore, this Special Issue will comprehensively cover the application of remote sensing data/techniques in ocean observations using data from spaceborne, airborne, and ground sensors, as well as artificial intelligence and Big Data technologies. The scope of this Special Issue includes, but is not limited to, the use of ocean color sensors, radiometers, scatterometers, altimeters, radars, and LiDAR applications in ocean observations, such as internal waves, eddies, oil spills, algae blooms, sea ice, stray waves, upwelling, bathymetry, atmosphere–ocean coupling, etc. Studies on the use of drones to observe marine debris and coastal radars to observe ocean waves and coastal currents are also welcome.

Prof. Dr. Chung-Ru Ho
Guest Editor

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 2500 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

ocean remote sensing
internal waves
eddies
oil spills
algal blooms
sea ices
rogue waves
upwelling
bathymetry
air-sea interaction
marine debris

Published Papers (3 papers)

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Research

Open AccessArticle

Optimization of Airborne Scatterometer NRCS Semicircular Sampling for Sea Wind Retrieval

Alexey Nekrasov

Alena Khachaturian

and

Colin Fidge

Remote Sens. 2023, 15(6), 1613; https://doi.org/10.3390/rs15061613 - 16 Mar 2023

Viewed by 436

Abstract

Airborne scatterometer capability depends on not only the device’s technical characteristics but also the scheme used for surface observations. Typically, a rotating-beam scatterometer uses a circular scheme for sampling normalized radar cross-sections (NRCS) at wind measurements over the sea. Here, we investigate wind retrieval using an updated semicircular scheme, providing the NRCS sampling at various combinations of incidence angles within the range 30° to 60°. The effectiveness of the wind retrieval using our semicircular sampling scheme was evaluated using Monte Carlo simulations, and we then developed corresponding wind algorithms that used a geophysical model function (GMF). As a result of the study, we found that a semicircular sampling scheme is well suited for wind retrieval over the sea using a rotating-beam scatterometer. We showed that a semicircular scheme can provide wind retrieval accuracies similar to those achievable with a conventional circular scheme, although the semicircular scheme requires approximately three times the number of NRCS samples integrated in each azimuth sector. Most importantly, however, the semicircular scheme enabled a maximum altitude for wind retrieval of twice the height possible with a circular scheme. In this study, we also demonstrate that the wind speed accuracy tends to increase with an increase in the incidence angle and similarly for the wind direction accuracy. Nonetheless, we then show that the simultaneous use of the NRCS sampling scheme at several incidence angles can increase the wind retrieval accuracy, especially when three or four incidence angles are used. The obtained results can be used to enhance airborne scatterometers and multimode radars operated in a scatterometer mode, including airborne high-altitude conical scanning radars, and can be applied to new remote sensing systems’ development. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation II)

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Open AccessArticle

Monitoring Green Tide in the Yellow Sea Using High-Resolution Imagery and Deep Learning

and

Remote Sens. 2023, 15(4), 1101; https://doi.org/10.3390/rs15041101 - 17 Feb 2023

Viewed by 775

Abstract

Green tide beaching events have occurred frequently in the Yellow Sea since 2007, causing a series of ecological and economic problems. Satellite imagery has been widely applied to monitor green tide outbreaks in open water. Traditional satellite sensors, however, are limited by coarse resolution or a low revisit rate, making it difficult to provide timely distribution of information about green tides in the nearshore. In this study, both PlanetScope Super Dove images and unmanned aerial vehicle (UAV) images are used to monitor green tide beaching events on the southern side of Shandong Peninsula, China. A deep learning model (VGGUnet) is used to extract the green tide features and quantify the green tide coverage area or biomass density. Compared with the U-net model, the VGGUnet model has a higher accuracy on the Super Dove and UAV images, with F1-scores of 0.93 and 0.92, respectively. The VGGUnet model is then applied to monitor the distribution of green tide on the beach and in the nearshore water; the results suggest that the VGGUnet model can accurately extract green tide features while discarding other confusing features. By using the Super Dove and UAV images, green tide beaching events can be accurately monitored and are consistent with field investigations. From the perspective of near real-time green tide monitoring, high-resolution imagery combined with deep learning is an effective approach. The findings pave the way for monitoring and tracking green tides in coastal zones, as well as assisting in the prevention and control of green tide disasters. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation II)

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Open AccessArticle

An Iterative Algorithm for Predicting Seafloor Topography from Gravity Anomalies

and

Remote Sens. 2023, 15(4), 1069; https://doi.org/10.3390/rs15041069 - 15 Feb 2023

Cited by 1 | Viewed by 464

Abstract

As high-resolution global coverage cannot easily be achieved by direct bathymetry, the use of gravity data is an alternative method to predict seafloor topography. Currently, the commonly used algorithms for predicting seafloor topography are mainly based on the approximate linear relationship between topography and gravity anomaly. In actual application, it is also necessary to process the corresponding data according to some empirical methods, which can cause uncertainty in predicting topography. In this paper, we established analytical observation equations between the gravity anomaly and topography, and obtained the corresponding iterative solving method based on the least square method after linearizing the equations. Furthermore, the regularization method and piecewise bilinear interpolation function are introduced into the observation equations to effectively suppress the high-frequency effect of the boundary sea region and the low-frequency effect of the far sea region. Finally, the seafloor topography beneath a sea region (117.25°–118.25°E, 13.85°–14.85°N) in the South China Sea is predicted as an actual application, where gravity anomaly data of the study area with a resolution of 1′ × 1′ are from the DTU17 model. Comparing the prediction results with the data of ship soundings from the National Geophysical Data Center (NGDC), the root-mean-square (RMS) error and relative error can be up to 127.4 m and approximately 3.4%, respectively. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation II)

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