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Satellite Remote Sensing for Ocean and Coastal Environment Monitoring
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Satellite Remote Sensing for Ocean and Coastal Environment Monitoring

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 2025 | Viewed by 3893

Special Issue Editors

Dr. Haidong Pan

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Guest Editor
Lab of Marine Science and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Interests: ocean tides; satellite altimeters; tidal analysis; sea levels; ocean dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Daosheng Wang

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Guest Editor
College of Marine Science and Technology, China University of Geosciences, Wuhan 430074, China
Interests: data assimilation; numerical simulation; tide; ocean dynamics
Dr. Jungang Yang

E-Mail Website
Guest Editor
Lab of Marine Physics and Remote Sensing, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Interests: synthetic aperture radar; altimeter; mesoscale eddy; marine dynamic; environment remote sensing; wind and wave remote sensing

Special Issue Information

Dear Colleagues,

Knowledge of the ocean environment, especially in the coastal regions, is essential for numerous human activities such as tidal power, navigation and ocean engineering. Remote sensing technologies like satellite altimeters and GNSS reform traditional ocean researches through providing observations with nearly global coverage. Nowadays, nearly all ocean environment elements, including sea level anomalies, sea surface salinity, sea surface temperature, sea surface pressure, winds, chlorophyll-a concentrations, water transparency and sea waves, can be observed by remote sensing technologies. Evidently, remote sensing observations provide valuable opportunities to explore basin-wide changes in the ocean environment and ocean dynamic processes with different scales of space and time such as ocean tides, mesoscale eddy, storm surges, coastal currents, sea level rise and ocean circulation. Furthermore, remote sensing observations have been assimilated into numerical models and thus greatly improve model performances.

Therefore, this Special Issue of Remote Sensing endeavors to assemble novel researches that utilize multi-source remote sensing observations, as well as numerical models to explore diverse ocean dynamic processes and their influences on a changing ocean environment in the global ocean, especially in the coastal areas with complicated hydrodynamic contexts and vital socio-economic problems. We welcome you to submit one or more research and review articles to the Special Issue titled “Satellite Remote Sensing for Ocean and Coastal Environment Monitoring”.

Dr. Haidong Pan
Dr. Daosheng Wang
Dr. Jungang Yang
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

  • sea level rise
  • ocean tides
  • model assimilation
  • sea surface temperature
  • sea surface salinity
  • chlorophyll
  • water transparency
  • sea waves
  • mesoscale eddy
  • ocean dynamics

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Published Papers (5 papers)

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Research

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17 pages, 12533 KiB  
Article
Potential Impact of Sea Surface Temperature Variability on the 2007 Sudden Bloom of Ulva prolifera in the Southern Yellow Sea
by Yufeng Pan, Pin Li, Jiaxuan Sun, Siyu Liu, Lvyang Xing, Di Yu and Qi Feng
Remote Sens. 2024, 16(23), 4407; https://doi.org/10.3390/rs16234407 - 25 Nov 2024
Viewed by 478
Abstract
Since 2007, Ulva prolifera (U. prolifera) originating in northern Jiangsu (NJ) has consistently expanded to the southern coast of the Shandong Peninsula. However, the underlying reasons for the 2007 sudden bloom of U. prolifera on a large scale remain unknown. This [...] Read more.
Since 2007, Ulva prolifera (U. prolifera) originating in northern Jiangsu (NJ) has consistently expanded to the southern coast of the Shandong Peninsula. However, the underlying reasons for the 2007 sudden bloom of U. prolifera on a large scale remain unknown. This study uses remote sensing data from MODIS/AQUA spanning the period 2003–2022 to investigate the sea surface temperature (SST) structure changes in the southern Yellow Sea (SYS) over the past 20 years. The results demonstrate the following. (1) Since 2007, the NJ northward current and the Yangtze estuary warm current have exhibited higher temperatures, earlier northward intrusions, and larger influence areas, leading to a faster warming rate in NJ before mid-May. This rapid increase in SST to a level suitable for early U. prolifera growth triggers large-scale blooms. (2) The change in temperature structure is primarily induced by a prolonged and intense La Niña event in 2007–2008. However, since 2016, under stable global climate conditions, the temperature structure of the SYS has returned to the pre-2007 state, corresponding to a decrease in the scale of U. prolifera blooms. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring)
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18 pages, 9708 KiB  
Article
Behavior and Energy of the M2 Internal Tide in the Madagascar–Mascarene Region
by Qian Wu, Jing Meng, Xu Chen and Yulin Guo
Remote Sens. 2024, 16(22), 4299; https://doi.org/10.3390/rs16224299 - 18 Nov 2024
Viewed by 550
Abstract
Internal tides serve as essential intermediate steps in the cascading of oceanic energy, playing a crucial role in oceanic mixing. M2 internal tides are the dominant tidal constituent in many oceanic regions, significantly influencing ocean dynamics. The Madagascar–Mascarene Region has high-energy internal tides, [...] Read more.
Internal tides serve as essential intermediate steps in the cascading of oceanic energy, playing a crucial role in oceanic mixing. M2 internal tides are the dominant tidal constituent in many oceanic regions, significantly influencing ocean dynamics. The Madagascar–Mascarene Region has high-energy internal tides, but due to a lack of observational studies, their propagation remains underexplored and warrants further investigation. In this study, we used satellite altimetry data to capture the sea surface manifestation of the first-mode M2 internal tides in the region. The results show that the Mascarene Plateau plays a key role in shaping the region’s uneven internal tide distribution. The Mascarene Strait is the most intense generation area, with an east-west energy flux of 1.42 GW. Using the internal tidal energy concentration index, we decomposed the internal tidal beams, finding the primary beam oriented at 148°. These beams propagate outward for over 800 km, with a maximum distance exceeding 1000 km. Geostrophic currents intensify the northward refraction of westward-propagating internal tides in the Mascarene Basin, particularly between 15°S and 20°S. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring)
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23 pages, 11057 KiB  
Article
Denoising of Photon-Counting LiDAR Bathymetry Based on Adaptive Variable OPTICS Model and Its Accuracy Assessment
by Peize Li, Yangrui Xu, Yanpeng Zhao, Kun Liang and Yuanjie Si
Remote Sens. 2024, 16(18), 3438; https://doi.org/10.3390/rs16183438 - 16 Sep 2024
Viewed by 668
Abstract
Spaceborne photon-counting LiDAR holds significant potential for shallow-water bathymetry. However, the received photon data often contain substantial noise, complicating the extraction of elevation information. Currently, a denoising algorithm named ordering points to identify the clustering structure (OPTICS) draws people’s attention because of its [...] Read more.
Spaceborne photon-counting LiDAR holds significant potential for shallow-water bathymetry. However, the received photon data often contain substantial noise, complicating the extraction of elevation information. Currently, a denoising algorithm named ordering points to identify the clustering structure (OPTICS) draws people’s attention because of its strong performance under high background noise. However, this algorithm’s fixed input variables can lead to inaccurate photon distribution parameters in areas near the water bottom, which results in inadequate denoising in these areas, affecting bathymetric accuracy. To address this issue, an Adaptive Variable OPTICS (AV-OPTICS) model is proposed in this paper. Unlike the traditional OPTICS model with fixed input variables, the proposed model dynamically adjusts input variables based on point cloud distribution. This adjustment ensures accurate measurement of photon distribution parameters near the water bottom, thereby enhancing denoising effects in these areas and improving bathymetric accuracy. The findings indicate that, compared to traditional OPTICS methods, AV-OPTICS achieves higher F1-values and lower cohesions, demonstrating better denoising performance near the water bottom. Furthermore, this method achieves an average MAE of 0.28 m and RMSE of 0.31 m, indicating better bathymetric accuracy than traditional OPTICS methods. This study provides a promising solution for shallow-water bathymetry based on photon-counting LiDAR data. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring)
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31 pages, 19050 KiB  
Article
An Ensemble Machine Learning Approach for Sea Ice Monitoring Using CFOSAT/SCAT Data
by Yanping Luo, Yang Liu, Chuanyang Huang and Fangcheng Han
Remote Sens. 2024, 16(17), 3148; https://doi.org/10.3390/rs16173148 - 26 Aug 2024
Viewed by 907
Abstract
Sea ice is a crucial component of the global climate system. The China–French Ocean Satellite Scatterometer (CFOSAT/SCAT, CSCAT) employs an innovative rotating fan beam system. This study applied principal component analysis (PCA) to extract classification features and developed an ensemble machine learning approach [...] Read more.
Sea ice is a crucial component of the global climate system. The China–French Ocean Satellite Scatterometer (CFOSAT/SCAT, CSCAT) employs an innovative rotating fan beam system. This study applied principal component analysis (PCA) to extract classification features and developed an ensemble machine learning approach for sea ice detection. PCA identified key features from CSCAT’s backscatter information, representing outer and sweet swath observations. The ensemble model’s performances (OA and Kappa) for the Northern and Southern Hemispheres were 0.930, 0.899, and 0.844, 0.747, respectively. CSCAT achieved an accuracy of over 0.9 for close ice and open water but less than 0.3 for open ice, with misclassification of open ice as closed ice. The sea ice extent discrepancy between CSCAT and the National Snow and Ice Data Center (NSIDC) was −0.06 ± 0.36 million km2 in the Northern Hemisphere and −0.03 ± 0.48 million km2 in the Southern Hemisphere. CSCAT’s sea ice closely matched synthetic aperture radar (SAR) imagery, indicating effective sea ice and open water differentiation. CSCAT accurately distinguished sea ice from open water but struggled with open ice classification, with misclassifications in the Arctic’s Greenland Sea and Hudson Bay, and the Antarctic’s sea ice–water boundary. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring)
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Other

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11 pages, 3246 KiB  
Technical Note
Wavelength Cut-Off Error of Spectral Density from MTF3 of SWIM Instrument Onboard CFOSAT: An Investigation from Buoy Data
by Yuexin Luo, Ying Xu, Hao Qin and Haoyu Jiang
Remote Sens. 2024, 16(16), 3092; https://doi.org/10.3390/rs16163092 - 22 Aug 2024
Viewed by 592
Abstract
The Surface Waves Investigation and Monitoring instrument (SWIM) provides the directional wave spectrum within the wavelength range of 23–500 m, corresponding to a frequency range of 0.056–0.26 Hz in deep water. This frequency range is narrower than the 0.02–0.485 Hz frequency range of [...] Read more.
The Surface Waves Investigation and Monitoring instrument (SWIM) provides the directional wave spectrum within the wavelength range of 23–500 m, corresponding to a frequency range of 0.056–0.26 Hz in deep water. This frequency range is narrower than the 0.02–0.485 Hz frequency range of buoys used to validate the SWIM nadir Significant Wave Height (SWH). The modulation transfer function used in the current version of the SWIM data product normalizes the energy of the wave spectrum using the nadir SWH. A discrepancy in the cut-off frequency/wavelength ranges between the nadir and off-nadir beams can lead to an overestimation of off-nadir cut-off SWHs and, consequently, the spectral densities of SWIM wave spectra. This study investigates such errors in SWHs due to the wavelength cut-off effect using buoy data. Results show that this wavelength cut-off error of SWH is small in general thanks to the high-frequency extension of the resolved frequency range. The corresponding high-frequency cut-off errors are systematic errors amenable to statistical correction, and the low-frequency cut-off error can be significant under swell-dominated conditions. By leveraging the properties of these errors, we successfully corrected the high-frequency cut-off SWH error using an artificial neural network and mitigated the low-frequency cut-off SWH error with the help of a numerical wave hindcast. These corrections significantly reduced the error in the estimated cut-off SWH, improving the bias, root-mean-square error, and correlation coefficient from 0.086 m, 0.111 m, and 0.9976 to 0 m, 0.039 m, and 0.9994, respectively. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring)
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