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Satellite Remote Sensing for Ocean and Coastal Environment Monitoring (Second Edition)

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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 August 2026 | Viewed by 4493

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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

E-Mail Website
Guest Editor

College of Marine Science and Technology, China University of Geosciences, Wuhan 430074, China
Interests: data assimilation; numerical simulation; tide; ocean dynamics
Special Issues, Collections and Topics in MDPI journals

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: ocean remote sensing; ocean dynamic environment AI detection and forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are launching the second edition of the Special Issue entitled “Satellite Remote Sensing for Ocean and Coastal Environment Monitoring”.

Knowledge of the ocean environment, especially in 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 research through providing observations with nearly global coverage. Currently, 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. Consequently, 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 eddies, storm surges, coastal currents, sea level rises 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 research that utilizes 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 harsh socio-economic problems. We welcome you to submit research articles and reviews to this Special Issue.

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 250 words) can be sent to the Editorial Office for assessment.

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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Related Special Issue

Satellite Remote Sensing for Ocean and Coastal Environment Monitoring in Remote Sensing (14 articles)

Published Papers (6 papers)

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Research

28 pages, 6961 KB

Open AccessArticle

Small Target Detection in Forward-Looking Sonar Images via LoG5S-LAD Framework

by Yuhang Wei, Jian Wang, Jiani Wen, Zengming Zhang and Haisen Li

Remote Sens. 2026, 18(10), 1518; https://doi.org/10.3390/rs18101518 - 12 May 2026

Viewed by 69

Abstract

In maritime search and rescue and underwater surveillance missions employing forward-looking sonar, strong reverberation and complex underwater environments often substantially degrade the target signal-to-clutter ratio (SCR), presenting significant challenges for target detection. Existing algorithms typically simplify the point spread function (PSF) into an ideal isotropic model, thereby overlooking the inherent anisotropy induced by its sidelobe structures. This physical model mismatch leads to target energy leakage and severely limits detection performance in complex backgrounds. To overcome the limitations of current target models and detection algorithms, this paper introduces a Gaussian 5 Superposition (G5S) model to accurately characterize the physical features of the PSF and proposes a Laplacian-of-G5S-based Local Adaptive Detection (LoG5S-LAD) method through the construction of a LoG5S filtering operator. Initially, a high-SCR target likelihood map is generated using Hessian-matrix-based geometric gating and LoG5S matched filtering techniques. Subsequently, robust background suppression and the effective preservation of faint targets are achieved through morphological artifact suppression, connected component screening, and a high-energy exemption mechanism. The effectiveness of the proposed framework is validated through model fitting experiments, as well as comprehensive simulations and detection tests across various sonar configurations. Experimental results indicate that the G5S model demonstrates precise fitting capabilities and strong physical adaptability. Furthermore, the proposed LoG5S-LAD algorithm significantly enhances the SCR while maintaining robust detection performance for faint and small-scale targets. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring (Second Edition))

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28 pages, 16425 KB

Open AccessArticle

Spatiotemporal Variability of Chlorophyll-a and Its Influencing Factors in the Bohai Sea from 2003 to 2022

by Mao Wang, Bing Han, Kai Guo, Haiyan Zhang, Jiaming Wei and Qiaoying Yuan

Remote Sens. 2026, 18(6), 922; https://doi.org/10.3390/rs18060922 - 18 Mar 2026

Viewed by 421

Abstract

Sea-surface chlorophyll-a concentration (Chl-a) is a core indicator reflecting phytoplankton biomass and marine ecological conditions. Its spatiotemporal variation patterns are closely related to environmental changes and human activities, especially in coastal waters around heavily populated areas, e.g., the Bohai Sea in China. Benefiting from long time-series ocean-color (i.e., Chl-a provided by Aqua-MODIS) multi-source merged sea surface temperature (SST) and wind speed (i.e., ERA5) and dissolved inorganic nitrogen concentration (DIN) data, this study investigated the long-term variation characteristics of Chl-a in the Bohai Sea and its influencing factors during the period of 2003 to 2022. After rigorous quality control and data reconstruction, this study analyzed the interannual, seasonal, and spatial variation patterns of Chl-a in the Bohai Sea across five ecological functional subregions (Bohai Bay, the Qinhuangdao coast, Liaodong Bay, Laizhou Bay, and the central Bohai Sea), and explored the influence of SST, wind speed, and DIN on variations in Chl-a. The results showed that the spatial distribution of Chl-a in the Bohai Sea exhibited a significant coastal–offshore gradient, with higher concentrations in coastal bays and the Qinhuangdao coast and lower concentrations in the central Bohai Sea. Temporally, despite a long-term trend of first increasing and then decreasing with a peak around 2011, Chl-a underwent a significant regime shift around 2015. After the shift, the average concentration decreased by 0.36 mg/m³ compared with that before the shift. On a seasonal scale, the average Chl-a concentration over the whole Bohai showed the largest decrease in summer (−0.65 mg/m³) and the smallest decrease in winter (−0.21 mg/m³), with contrasting changes among subregions: the Qinhuangdao coast had the most significant decrease (−1.54 mg/m³), while Laizhou Bay remained basically stable. Driver mechanism analysis indicated that Chl-a in the Bohai Sea was significantly negatively correlated with SST (r = −0.51, p = 0.022) and significantly negatively correlated with wind speed (r = −0.77, p < 0.01). Furthermore, both SST and wind speed have undergone significant regime shifts toward a warmer and a windier state, respectively. The timing of these climatic shifts coincided with or preceded the Chl-a regime shift, which may help suppress phytoplankton blooms and maintain lower Chl-a levels. In addition, the surface DIN concentration in Bohai Bay decreased by 23.6% after the Chl-a regime shift, indicating a reduction in nutrient input may be responsible for the decrease in Chl-a in this region. The research results reveal the long-term variation patterns and multi-factor synergistic regulatory mechanism of Chl-a in the Bohai Sea, providing a scientific reference for red-tide monitoring and early warning as well as regional ecological environment management in the Bohai Sea. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring (Second Edition))

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24 pages, 3564 KB

Open AccessArticle

Achieving Consistent Estimates of Particulate Organic Carbon from Satellites, Ships and Argo Floats

by Graham D. Quartly, Shubha Sathyendranath and Martí Galí

Remote Sens. 2026, 18(5), 832; https://doi.org/10.3390/rs18050832 - 9 Mar 2026

Viewed by 631

Abstract

Carbon fluxes from the atmosphere to the ocean and from the ocean surface to the deep ocean are a key pathway in the long-term sequestration of anthropogenic CO₂. Particulate Organic Carbon (POC), which comprises living plankton, detritus and other microscopic organisms, is a very dynamic carbon pool in surface waters, so an ability to assess POC reliably from satellites and autonomous profilers is fundamental to the quantification of the reservoirs and fluxes of carbon within the ocean, and to assess their response to climate change. In situ records from sample filtration during dedicated hydrographic surveys are limited both in terms of spatial coverage and time, so reliable algorithms are required that make use of readily available autonomously collected data that provide much better spatial and temporal coverage. In this paper, algorithms that use ocean colour data from satellites to estimate POC are re-assessed, and then the satellite-derived products are compared with near-surface in situ observations from biogeochemical (BGC) Argo profilers. The satellites and in situ BGC-Argo records match each other to within 30%, but a regional bias persists that may be related to the BGC-Argo fluorometers overestimating the chlorophyll concentration in the Southern Ocean. A simple coarse-resolution regional correction to the observed chlorophyll-a concentration and backscatter coefficient, plus the removal of clear outliers, improves the agreement to approximately 15%. The association of POC with the surface chlorophyll value is so strong that an algorithm based on chlorophyll-a alone provides an almost equally good estimate of POC compared with more complex algorithms that incorporate additional bio-optical variables such as the backscattering coefficient. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring (Second Edition))

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24 pages, 10247 KB

Open AccessArticle

A Segmented Adaptive Filtering Method for Nearshore Bathymetry Using ICESat-2 Dataset

by Yifu Chen, Ziqiang Wang, Wuxing Song, Yuan Le, Liqin Zhou, Haichao Guo, Lin Wu and Lin Yi

Remote Sens. 2026, 18(4), 568; https://doi.org/10.3390/rs18040568 - 11 Feb 2026

Viewed by 515

Abstract

Equipped with an Advanced Topographic Laser Altimeter System (ATLAS), ICESat-2 (Ice, Cloud and land Elevation Satellite-2) is a photon-counting laser altimetry mission with strong potential for nearshore bathymetry. In this study, a novel filtering and bathymetric method termed a segmented adaptive filtering bathymetry has been proposed. Sea-surface photons are identified from peaks in the elevation-density histogram, enabling separation of surface and seafloor photons. The seafloor photons are then partitioned into along-track segments, where seafloor signal photons are extracted using an adaptive elliptical kernel whose parameters and orientation are determined from local density patterns and seafloor slope. The seafloor profile is obtained by polynomial fitting, and nearshore depth is estimated from the elevations of the surface and seafloor signal photons. To ensure and improve the accuracy and reliability of the proposed method, ICESat-2 data from Qilianyu Islands at the South China Sea and West Island at the Florida Keys of the United States were adopted to perform experiments. Furthermore, the bathymetric results obtained by ICESat-2 datasets at different experimental areas were compared with the reference bathymetry obtained by the airborne light detection and ranging (LiDAR) bathymetry (ALB) system. Finally, the bathymetric accuracy validation and assessment were performed. The highest accuracy of root mean square error (RMSE) and coefficient of determination (R²) has reached 0.37 m and 98%, respectively. The accuracy validation of bathymetric results at different study areas demonstrated that the method proposed in this study can automatically and effectively achieve high-precision nearshore bathymetry and topographic surveys. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring (Second Edition))

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25 pages, 15799 KB

Open AccessArticle

Coastal Zone Imager Sargassum Index Model Reveals the Change Details of Sargassum in Coastal Waters of China

by Beibei Zhang, Lina Cai, Xiaomin Ye and Jiahua Li

Remote Sens. 2026, 18(1), 78; https://doi.org/10.3390/rs18010078 - 25 Dec 2025

Viewed by 694

Abstract

This study reveals the distribution of floating macroalgae Sargassum in the East China Sea and Yellow Sea using HY-1C/D Coastal Zone Imager (CZI) data. A new inversion model, utilizing green and near-infrared bands, was developed for the 50 m resolution CZI data. This model effectively distinguishes Sargassum from Ulva prolifera and is effective in turbid coastal waters. Sargassum spatiotemporal distribution and drift patterns over five years were analyzed. Key findings demonstrate that (1) floating Sargassum exhibits distinct spatiotemporal distribution patterns. Sargassum initially emerges along Zhejiang’s eastern coast in February. During March and April, it concentrates east of Hangzhou Bay. While in May, Sargassum appears in the Yellow Sea, and is distributed near the Shandong Peninsula by June. Small patches of Sargassum are also found in the Yellow Sea from November to January. (2) Its distribution is influenced by various factors like nutrients, temperature, salinity, currents, and winds. Suitable nutrients, temperature, and salinity promote growth, while currents and winds, particularly in April–May, drive its northward drift from the East China Sea into the Yellow Sea. The Yellow Sea population originates from both drifting populations and local growth. (3) This research highlights the utility of HY-1C/D satellite data in coastal zone research, facilitating ecological monitoring and protection. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring (Second Edition))

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18 pages, 3107 KB

Open AccessArticle

Eutrophication Assessment Revealed by the Distribution of Chlorophyll-a in the South China Sea

by Jingwen Wu, Dong Jiang, Zhichao Cai, Jing Lv, Guowei Liu and Bingtian Li

Remote Sens. 2025, 17(19), 3388; https://doi.org/10.3390/rs17193388 - 9 Oct 2025

Cited by 1 | Viewed by 1338

Abstract

Chlorophyll-a is a key indicator characterizing the health of marine ecosystems. This study aimed to assess eutrophication risk by investigating the spatio-temporal evolution of chlorophyll-a in the South China Sea (SCS). Based on MODIS-Aqua remote sensing data from 2003 to 2024, five spatial interpolation methods were compared, and Ordinary Kriging was selected as the optimal method (r = 0.96) for reconstructing the chlorophyll-a distribution. The findings indicate that chlorophyll-a is higher in winter and autumn than in summer and spring, with significant enrichment observed near coastal areas. Concentrations decrease with increasing distance from the shore. The Mekong River estuary consistently exhibits high values, while the concentration in the SCS Basin remains persistently low. Furthermore, the spatial extent where chlorophyll concentrations exceed the bloom threshold was evaluated to highlight potential eutrophication risk. These results provide a scientific basis for understanding the response mechanism of the SCS ecosystem to climate change and have important implications for regional marine environmental management and ecological conservation. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring (Second Edition))

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