Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Advances in Ocean Remote Sensing through Data and Algorithm Fusion
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Ocean Remote Sensing through Data and Algorithm Fusion

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 June 2022) | Viewed by 12998

Special Issue Editors

Prof. Dr. Jungho Im

E-Mail Website
Guest Editor
Department of Civil, Urban, Earth, and Environmental Engineering, UNIST (Ulsan National Institute of Science and Technology), Ulsan, Republic of Korea
Interests: satellite remote sensing; aerosols; air quality; wild fire; urban heatwave; drought; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. YongHoon Kim

E-Mail Website
Guest Editor
Department of Earth and Space Sciences, West Chester University of Pennsylvania, West Chester, PA 19383, USA
Interests: remote sensing of ocean color; artificial intelligence; marine pollution; environmental fluid dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For decades, satellite remote sensing, in conjunction with in situ data collection, has been widely used for ocean applications such as water quality monitoring, coastal management, sea surface temperature estimation, and sea ice quantification. The ocean has a very dynamic environment, which is often difficult to effectively monitor from satellite sensors for various reasons. Some major such reasons include cloud contamination, relatively low temporal resolution, atmospheric effects, limited availability of in situ data matched with satellite data, and the dynamic interaction between surface and subsurface materials. Many studies have been conducted to mitigate such limitations through spatiotemporal interpolation, multisensor data fusion, the synergistic use of satellite observations and numerical models, and the use of advanced modeling techniques such as deep learning.

There have been efforts to assess and monitor ocean parameters and phenomena using polar-orbiting satellite remote sensing systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua, Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Visible Infrared Imaging Radiometer Suite (VIIRS), and geostationary satellite sensor systems, such as the Geostationary Operational Environmental Satellite (GOES) series, Geostationary Ocean Color Imager (GOCI), and Himawari-8 Advanced Himawari Imager (AHI). In particular, GOCI is the world’s first geostationary ocean color satellite sensor, which was launched in 2010. The advent of the geostationary ocean color satellite sensor has significantly improved the temporal resolution of satellite-derive ocean products (e.g., 8 times a day). GOCI-2, onboard GeoKompsat 2B, is scheduled to be launched in 2020 with more advanced specifications compared to GOCI. GOCI-2 will collect data (10 times/day) at a total of 13 bands from 380 to 900 nm with 250 m resolution (at the Equator), which is expected to greatly improve satellite-based ocean monitoring and assessment.

In light of the improvement in sensor technology and image processing techniques, we invite you to contribute research papers to this Special Issue focusing on ocean remote sensing through data and algorithm fusion, which can provide valuable ocean information for related scientists, managers, and policy makers. Original research articles are solicited over a wide range of topics which may focus on but are not limited to:

  • Deep learning-based estimation of ocean parameters from satellite sensor data;
  • Multi-sensor data fusion for ocean remote sensing;
  • Relationship between satellite-derived ocean parameters and climate patterns;
  • Satellite-based monitoring and assessment of disastrous oceanic events;
  • Improvement of the temporal resolution of satellite-derived ocean parameters;
  • Synergistic use of satellite remote sensing data and numerical models for monitoring and forecasting of ocean phenomena;
  • Marine pollution analysis through the integration of remote sensing, in situ data, and numerical model outputs.

Prof. Jungho Im
Prof. YongHoon Kim
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.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

21 pages, 8082 KiB  
Article
Ocean Wave Inversion Based on Hybrid Along- and Cross-Track Interferometry
by Daozhong Sun, Yunhua Wang, Zhichao Xu, Yanmin Zhang, Yubin Zhang, Junmin Meng, Hanwei Sun and Lei Yang
Remote Sens. 2022, 14(12), 2793; https://doi.org/10.3390/rs14122793 - 10 Jun 2022
Cited by 2 | Viewed by 1488
Abstract
The hybrid interferometric synthetic aperture radar system is a combination of an along-track configuration and cross-track configuration. Based on linear ocean wave theory, an ocean wave inversion algorithm for a hybrid interferometric synthetic aperture radar system is proposed in this work. Using the [...] Read more.
The hybrid interferometric synthetic aperture radar system is a combination of an along-track configuration and cross-track configuration. Based on linear ocean wave theory, an ocean wave inversion algorithm for a hybrid interferometric synthetic aperture radar system is proposed in this work. Using the interferometric synthetic aperture radar images acquired by the TerraSAR-X and TanDEM-X satellites and the interferometric synthetic aperture radar images acquired by an airborne interferometric radar altimeter with a certain degree of squint, the profile of ocean waves and the corresponding orbital velocities were retrieved by combining the new inversion algorithm with the cross-spectra. Meanwhile, key parameters of ocean waves, such as the significant wave height, significant wave orbital velocity, propagation direction, and wavelength of the dominant waves, were also extracted from the ocean wave spectra retrieved in this study. In order to evaluate the reliability of the new inversion algorithm, the retrieved significant wave heights were compared with those provided by the European Centre for Medium-Range Weather Forecasts and measured by a Global Navigation Satellite System buoy. The results showed that for the ocean waves retrieved from the spaceborne hybrid interferometric synthetic aperture radar images, the differences between the retrieved significant wave heights of the four subareas selected in this paper and those provided by European Centre for Medium-Range Weather Forecasts were approximately 0.01, –0.17, –0.55, and –0.37 m, respectively, and for the ocean waves retrieved from the airborne interferometric radar altimeter images, the differences between the retrieved significant wave heights corresponding to the M920 and M3120 images used in this paper and those measured by the Global Navigation Satellite System buoy were approximately –0.05 and –0.09, respectively. Therefore, the method proposed in this work could retrieve the ocean wave spectra well when the velocity bunching had a small influence; however, as the nonlinear influence of the velocity bunching increased, the difference between the significant wave heights retrieved using this method and provided by the European Centre for Medium-Range Weather Forecasts also increased. Full article
(This article belongs to the Special Issue Advances in Ocean Remote Sensing through Data and Algorithm Fusion)
►▼ Show Figures

Graphical abstract

18 pages, 89379 KiB  
Article
A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea
by Yun-Jeong Choi, Hyun-Ju Ban, Hee-Jeong Han and Sungwook Hong
Remote Sens. 2022, 14(3), 793; https://doi.org/10.3390/rs14030793 - 08 Feb 2022
Cited by 4 | Viewed by 2093
Abstract
Accurate cloud-masking procedures to distinguish cloud-free pixels from cloudy pixels are essential for optical satellite remote sensing. Many studies on satellite-based cloud-detection have been performed using the spectral characteristics of clouds in terms of reflectance and temperature. This study proposes a cloud-detection method [...] Read more.
Accurate cloud-masking procedures to distinguish cloud-free pixels from cloudy pixels are essential for optical satellite remote sensing. Many studies on satellite-based cloud-detection have been performed using the spectral characteristics of clouds in terms of reflectance and temperature. This study proposes a cloud-detection method using reflectance in four bands: 0.56 μm, 0.86 μm, 1.38 μm, and 1.61 μm. Methodologically, we present a conversion relationship between the normalized difference water index (NDWI) and the green band in the visible spectrum for thick cloud detection using moderate-resolution imaging spectroradiometer (MODIS) observations. NDWI consists of reflectance at the 0.56 and 0.86 μm bands. For thin cloud detection, the 1.38 and 1.61 μm bands were applied with empirically determined threshold values. Case study analyses for the four seasons from 2000 to 2019 were performed for the sea surface area of the Yellow Sea and Bohai Sea. In the case studies, the comparison of the proposed cloud-detection method with the MODIS cloud mask (CM) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data indicated a probability of detection of 0.933, a false-alarm ratio of 0.086, and a Heidke Skill Score of 0.753. Our method demonstrated an additional important benefit in distinguishing clouds from sea ice or yellow dust, compared to the MODIS CM products, which usually misidentify the latter as clouds. Consequently, our cloud-detection method could be applied to a variety of low-orbit and geostationary satellites with 0.56, 0.86, 1.38, and 1.61 μm bands. Full article
(This article belongs to the Special Issue Advances in Ocean Remote Sensing through Data and Algorithm Fusion)
►▼ Show Figures

Figure 1

23 pages, 9743 KiB  
Article
High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension
by Sihun Jung, Cheolhee Yoo and Jungho Im
Remote Sens. 2022, 14(3), 575; https://doi.org/10.3390/rs14030575 - 25 Jan 2022
Cited by 12 | Viewed by 4295
Abstract
Sea SurfaceTemperature (SST) is a critical parameter for monitoring the marine environment and understanding various ocean phenomena. While SST can be regularly retrieved from satellite data, it often suffers from missing data due to various reasons including cloud contamination. In this study, we [...] Read more.
Sea SurfaceTemperature (SST) is a critical parameter for monitoring the marine environment and understanding various ocean phenomena. While SST can be regularly retrieved from satellite data, it often suffers from missing data due to various reasons including cloud contamination. In this study, we proposed a novel two-step data fusion framework for generating high-resolution seamless daily SST from multi-satellite data sources. The proposed approach consists of (1) SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using the SSTs derived from two satellite sensors (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer 2(AMSR2)), and (2) SST improvement through data fusion using random forest for consistency with in situ measurements with two schemes (i.e., scheme 1 using the reconstructed MODIS SST variables and scheme 2 using both MODIS and AMSR2 SST variables). The proposed approach was evaluated over the Kuroshio Extension in the Northwest Pacific, where a highly dynamic SST pattern can be found, from 2015 to 2019. The results showed that the reconstructed MODIS and AMSR2 SSTs through DINCAE yielded very good performance with Root Mean Square Errors (RMSEs) of 0.85 and 0.60 °C and Mean Absolute Errors (MAEs) of 0.59 and 0.45 °C, respectively. The results from the second step showed that scheme 2 and scheme 1 produced RMSEs of 0.75 and 0.98 °C and MAEs of 0.53 and 0.68 °C, respectively, compared to the in situ measurements, which proved the superiority of scheme 2 using multi-satellite data sources. Scheme 2 also showed comparable or even better performance than two operational SST products with similar spatial resolution. In particular, scheme 2 was good at simulating features with fine resolution (~50 km). The proposed approach yielded promising results over the study area, producing seamless daily SST products with high quality and high feature resolution. Full article
(This article belongs to the Special Issue Advances in Ocean Remote Sensing through Data and Algorithm Fusion)
►▼ Show Figures

Graphical abstract

16 pages, 14103 KiB  
Article
A Novel Framework of Integrating UV and NIR Atmospheric Correction Algorithms for Coastal Ocean Color Remote Sensing
by Feng Qiao, Jianyu Chen, Zhihua Mao, Bing Han, Qingjun Song, Yuying Xu and Qiankun Zhu
Remote Sens. 2021, 13(21), 4206; https://doi.org/10.3390/rs13214206 - 20 Oct 2021
Cited by 3 | Viewed by 2005
Abstract
Atmospheric correction is a fundamental process of ocean color remote sensing to remove the atmospheric effect from the top-of-atmosphere. Generally, Near Infrared (NIR) based algorithms perform well for clear waters, while Ultraviolet (UV) based algorithms can obtain good results for turbid waters. However, [...] Read more.
Atmospheric correction is a fundamental process of ocean color remote sensing to remove the atmospheric effect from the top-of-atmosphere. Generally, Near Infrared (NIR) based algorithms perform well for clear waters, while Ultraviolet (UV) based algorithms can obtain good results for turbid waters. However, the latter tends to produce noisy patterns for clear waters. An ideal and practical solution to deal with such a dilemma is to apply NIR- and UV-based algorithms for clear and turbid waters, respectively. We propose a novel atmospheric correction method that integrates the advantages of UV- and NIR-based atmospheric correction (AC) algorithms for coastal ocean color remote sensing. The new approach is called UV-NIR combined AC algorithm. The performance of the new algorithm is evaluated based on match-ups between GOCI images and the AERONET-OC dataset. The results show that the values of retrieved Rrs (Remote Sensing Reflectance) at visible bands agreed well with the in-situ observations. Compared with the SeaDAS (SeaWiFS Data Analysis System) standard NIR algorithm, the new AC algorithm can achieve better precision and provide more available data. Full article
(This article belongs to the Special Issue Advances in Ocean Remote Sensing through Data and Algorithm Fusion)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

13 pages, 12753 KiB  
Letter
A New Method of De-Aliasing Large-Scale High-Frequency Barotropic Signals in the Mediterranean Sea
by Denghui Hu and Yongsheng Xu
Remote Sens. 2020, 12(13), 2157; https://doi.org/10.3390/rs12132157 - 06 Jul 2020
Cited by 2 | Viewed by 1973
Abstract
With the development of satellite observation technology, higher resolution and shorter return cycle have also placed higher demands on satellite data processing. The non-tide high-frequency barotropic oscillation in the marginal sea produces large aliasing errors in satellite altimeter observations. In previous studies, the [...] Read more.
With the development of satellite observation technology, higher resolution and shorter return cycle have also placed higher demands on satellite data processing. The non-tide high-frequency barotropic oscillation in the marginal sea produces large aliasing errors in satellite altimeter observations. In previous studies, the satellite altimeter aliasing correction generally relied on a few bottom pressure data or the model data. Here, we employed the high-frequency tide gauge data to extract the altimeter non-tide aliasing correction in the west Mediterranean Sea. The spatial average method and EOF analysis method were adopted to track the high-frequency oscillation signals from 15 tide gauge records (TGs), and then were used to correct the aliasing errors in the Jason-1 and Envisat observations. The results showed that the EOF analysis method is better than the spatial average method in the altimeter data correction. After EOF correction, 90% of correlation (COR) between TG and sea level of Jason-1 has increased ~5%, and ~3% increase for the Envisat sea level; for the spatial average correction method, only ~70% of Jason-1 and Envisat data at the TGs location has about 2% increase in correlation. The EOF correction reduced the average percentage of error variance (PEL) by ~30%, while the spatial average correction increased the average percentage of PEL by ~20%. After correction by the EOF method, the altimeter observations are more consistent with the distribution of strong currents and eddies in the west Mediterranean Sea. The results prove that the proposed EOF method is more effective and accurate for the non-tide aliasing correction. Full article
(This article belongs to the Special Issue Advances in Ocean Remote Sensing through Data and Algorithm Fusion)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop