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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: closed (30 November 2021) | Viewed by 31862

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Dr. Kyung-Ae Park

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Guest Editor

Department of Earth Science Education, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
Interests: satellite remote sensing; physical oceanography; climate change; sea surface temperature; air-sea interaction; sea surface wind; mesoscale eddy; physio-biological process

Dr. Youngje Park

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Guest Editor

Korea Institute of Ocean Science and Technology, Busan, Korea
Interests: ocean color; algorithms; validation; geostationary satellite; ocean optics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ming-An Lee

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Guest Editor

Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, 20224, 2 Pei-Ning Rd, Keelung, Taiwan
Interests: satellite oceanography; fisheries and sea conditions; fisheries; climate change; marine ecology; marine environment; marine biodiversity; aquatic ecosystems; ecology and evolution; environmental impact assessment; natural resource management
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaofeng Li

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Guest Editor

Institute of Oceanography, Chinese Academy of Sciences (IOCAS), 7 Nanhai Road, Qingdao 266071, China
Interests: satellite oceanography; artificial intelligence; big data

Special Issue Information

Dear Colleagues,

With the advent of new space platforms and advanced sensor technology in the optical region of the electromagnetic spectrum, multi-spectral optical remote sensing data has significantly contributed to the improvement of our understanding of oceanic phenomena and processes over the past decades. Optical oceanic remote sensing has the potential to provide a comprehensive understanding of all aspects of physical and biological processes in the surface layer of the ocean. Recent advances in optical oceanic remote sensing have been accomplished by significant technological improvements in terms of the quality and quantity of observation data, observation frequency capability, and spatial and spectral resolutions. These advances have made it possible to procure vast amounts of information on the spatiotemporal variability of oceanic features at diverse spatial and temporal scales. Geostationary satellite observations with multiple images per day have facilitated rapid progress in studying the short-term dynamic variability of coastal waters. Such optical observations have been extensively utilized to investigate and understand the spatiotemporal variability of the chlorophyll-a concentration of phytoplankton, which can be used as an indicator of the low-level marine ecosystem, as well as to study harmful algae such as green algae, brown algae, and algae that causes red tides. Other oceanic features associated with suspended sediment, water quality, coastal bathymetry, vessel-related matters, oil and chemical spills, sea ice, and sea fog have also been extensively monitored. This Special Issue is devoted to the most recent advances in the studies of optical remote sensing technology and its applications in ocean studies, with an emphasis on the following topics:

Validation of ocean color products in the global ocean and local seas
Algorithms of ocean color variables using near-polar orbit and geostationary satellites
Spatial and temporal variability of oceanic phenomena from optical signals
Biological blooms of marine algae (phytoplankton, green algae, brown algae, red tide)
Understanding of physical and biological processes
Surface current derivation using satellite optical images
Change in oceanic biological features to wind forcing
Monitoring of suspended sediment or suspended particulate matters at the coastal region
Detection and monitoring of red tide bloom
Air-sea interaction and upper ocean variability
Assessment and monitoring of water quality
Ship detection using optical and hyperspectral methods
Delineation of shoreline and estimation of coastal bathymetry
Sea ice monitoring using optical images
Applications of optical images related to aquaculture and fisheries
Multi-spectral sea fog monitoring
Methods and oceanic applications of hyperspectral remote sensing
Applications of deep-learning methods to remotely-sensed optical images

Prof. Kyung-Ae Park
Dr. Young-Je Park
Prof. Ming-An Lee
Dr. Xiaofeng Li
Guest Editors

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Keywords

Optical remote sensing
Ocean color
Chlorophyll-a concentration
Suspended particulate matter
Water quality
Algal blooms
Red tide
Coastal upwelling
Biological response
Sea surface current
Oceanic front
Mesoscale eddy
Aquaculture
Ship detection
Shoreline
Coastal bathymetry
Surface waves
Sea fog
Sea ice monitoring
Internal wave

Published Papers (10 papers)

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Research

16 pages, 6258 KiB

Open AccessArticle

Habitat Suitability Modeling for the Feeding Ground of Immature Albacore in the Southern Indian Ocean Using Satellite-Derived Sea Surface Temperature and Chlorophyll Data

by Sandipan Mondal, Ali Haghi Vayghan, Ming-An Lee, Yi-Chen Wang and Bambang Semedi

Remote Sens. 2021, 13(14), 2669; https://doi.org/10.3390/rs13142669 - 07 Jul 2021

Cited by 15 | Viewed by 2812

Abstract

In the current study, remotely sensed sea surface ocean temperature (SST) and sea surface chlorophyll (SSC), an indicator of tuna abundance, were used to determine the optimal feeding habitat zone of the southern Indian Ocean (SIO) albacore using a habitat suitability model applied to the 2000–2016 Taiwanese longline fishery data. The analysis showed a stronger correlation between the 2-month lag SSC and standardized catch per unit effort (CPUE) than 0-, 1-, 3-, and 4-month lag SSC. SST also exhibited a stronger correlation with standardized CPUE. Therefore, SST and SSC_2 were selected as final variables for model construction. An arithmetic mean model with SST and SSC_2 was deemed suitable to predict the albacore feeding habitat zone in the SIO. The preferred ranges of SSC_2 and SST for the feeding habitat of immature albacore were 0.07–0.09 mg m⁻³ and 16.5–18.5 °C, respectively, and mainly centralized at 17.5 °C SST and 0.08 mg m⁻³ SSC_2. The selected habitat suitability index model displayed a high correlation (R² = 0.8276) with standardized CPUE. Overall, temperature and ocean chlorophyll were found to be essential for albacore habitat formation in the SIO, consistent with previous studies. The results of this study can contribute to ecosystem-based fisheries management in the SIO by providing insights into the habitat preference of immature albacore tuna in the SIO. Full article

(This article belongs to the Special Issue Optical Oceanographic Observation)

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16 pages, 7726 KiB

Open AccessArticle

Predicting Skipjack Tuna Fishing Grounds in the Western and Central Pacific Ocean Based on High-Spatial-Temporal-Resolution Satellite Data

by Tung-Yao Hsu, Yi Chang, Ming-An Lee, Ren-Fen Wu and Shih-Chun Hsiao

Remote Sens. 2021, 13(5), 861; https://doi.org/10.3390/rs13050861 - 25 Feb 2021

Cited by 26 | Viewed by 4319

Abstract

Skipjack tuna are the most abundant commercial species in Taiwan’s pelagic purse seine fisheries. However, the rapidly changing marine environment increases the challenge of locating target fish in the vast ocean. The aim of this study was to identify the potential fishing grounds of skipjack tuna in the Western and Central Pacific Ocean (WCPO). The fishing grounds of skipjack tuna were simulated using the habitat suitability index (HSI) on the basis of global fishing activities and remote sensing data from 2012 to 2015. The selected environmental factors included sea surface temperature and front, sea surface height, sea surface salinity, mixed layer depth, chlorophyll a concentration, and finite-size Lyapunov exponents. The final input factors were selected according to their percentage contribution to the total efforts. Overall, 68.3% of global datasets and 35.7% of Taiwanese logbooks’ fishing spots were recorded within 5 km of suitable habitat in the daily field. Moreover, 94.9% and 79.6% of global and Taiwan data, respectively, were identified within 50 km of suitable habitat. Our results showed that the model performed well in fitting daily forecast and actual fishing position data. Further, results from this study could benefit habitat monitoring and contribute to managing sustainable fisheries for skipjack tuna by providing wide spatial coverage information on habitat variation. Full article

(This article belongs to the Special Issue Optical Oceanographic Observation)

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22 pages, 8476 KiB

Open AccessArticle

Assessing Summertime Primary Production Required in Changed Marine Environments in Upwelling Ecosystems Around the Taiwan Bank

by Po-Yuan Hsiao, Teruhisa Shimada, Kuo-Wei Lan, Ming-An Lee and Cheng-Hsin Liao

Remote Sens. 2021, 13(4), 765; https://doi.org/10.3390/rs13040765 - 19 Feb 2021

Cited by 8 | Viewed by 2723

Abstract

The Taiwan Bank (TB) is located in the southern Taiwan Strait, where the marine environments are affected by South China Sea Warm Current and Kuroshio Branch Current in summer. The bottom water flows upward along the edge of the continental shelf, forming an upwelling region that is an essential high-productivity fishing ground. Using trophic dynamic theory, fishery resources can be converted into primary production required (PPR) by primary production, which indicates the environmental tolerance of marine ecosystems. This study calculated the PPR of benthic and pelagic species, sea surface temperature (SST), upwelling size, and net primary production (NPP) to analyze fishery resource structure and the spatial distribution of PPR in upwelling, non-upwelling, and thermal front (frontal) areas of the TB in summer. Pelagic species, predominated by those in the Scombridae, Carangidae families and Trachurus japonicus, accounted for 77% of PPR (67% of the total catch). The benthic species were dominated by Mene maculata and members of the Loliginidae family. The upwelling intensity was the strongest in June and weakest in August. Generalized additive models revealed that the benthic species PPR in frontal habitats had the highest deviance explained (28.5%). Moreover, frontal habitats were influenced by NPP, which was also the main factor affecting the PPR of benthic species in all three habitats. Pelagic species were affected by high NPP, as well as low SST and negative values of the multivariate El Niño–Southern Oscillation (ENSO) index in upwelling habitats (16.9%) and non-upwelling habitats (11.5%). The composition of pelagic species varied by habitat; this variation can be ascribed to impacts from the ENSO. No significant differences were noted in benthic species composition. Overall, pelagic species resources are susceptible to climate change, whereas benthic species are mostly insensitive to climatic factors and are more affected by NPP. Full article

(This article belongs to the Special Issue Optical Oceanographic Observation)

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8 pages, 3484 KiB

Open AccessCommunication

Sea State from Single Optical Images: A Methodology to Derive Wind-Generated Ocean Waves from Cameras, Drones and Satellites

by Rafael Almar, Erwin W. J. Bergsma, Patricio A. Catalan, Rodrigo Cienfuegos, Leandro Suarez, Felipe Lucero, Alexandre Nicolae Lerma, Franck Desmazes, Eleonora Perugini, Margaret L. Palmsten and Chris Chickadel

Remote Sens. 2021, 13(4), 679; https://doi.org/10.3390/rs13040679 - 13 Feb 2021

Cited by 14 | Viewed by 3856 | Correction

Abstract

Sea state is a key variable in ocean and coastal dynamics. The sea state is either sparsely measured by wave buoys and satellites or modelled over large scales. Only a few attempts have been devoted to sea state measurements covering a large domain; in particular its estimation from optical images. With optical technologies becoming omnipresent, optical images offer incomparable spatial resolution from diverse sensors such as shore-based cameras, airborne drones (unmanned aerial vehicles/UAVs), or satellites. Here, we present a standalone methodology to derive the water surface elevation anomaly induced by wind-generated ocean waves from optical imagery. The methodology was tested on drone and satellite images and compared against ground truth. The results show a clear dependence on the relative azimuth view angle in relation to the wave crest. A simple correction is proposed to overcome this bias. Overall, the presented methodology offers a practical way of estimating ocean waves for a wide range of applications. Full article

(This article belongs to the Special Issue Optical Oceanographic Observation)

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17 pages, 9085 KiB

Open AccessArticle

Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)

by Ji-Eun Park and Kyung-Ae Park

Remote Sens. 2021, 13(4), 585; https://doi.org/10.3390/rs13040585 - 06 Feb 2021

Cited by 3 | Viewed by 2520

Abstract

The detection and removal of erroneous pixels is a critical pre-processing step in producing chlorophyll-a (chl-a) concentration values to adequately understand the bio-physical oceanic process using optical satellite data. Geostationary Ocean Color Imager (GOCI) chl-a images revealed that numerous speckle noises with enormously high and low values were randomly scattered throughout the seas around the Korean Peninsula as well as in the Northwest Pacific. Most of the previous methods used to remove abnormal chl-a concentrations have focused on inhomogeneity in spatial features, which still frequently produce problematic values. Herein, a scheme was developed to detect and eliminate chl-a speckles as well as erroneous pixels near the boundary of clouds; for the purpose, a deep neural network (DNN) algorithm was applied to a large-sized GOCI database from the 6-year period of 2012–2017. The input data of the proposed DNN model were composed of the GOCI level-2 remote-sensing reflectance of each band, chl-a concentration image, median filtered, and monthly climatology chl-a image. The quality of the individual images as well as the monthly composites of chl-a data was improved remarkably after the DNN speckle-removal procedure. The quantitative analyses showed that the DNN algorithm achieved high classification accuracy with regard to the detection of error pixels with both very high and very low chl-a values, and better performance compared to the general arithmetic algorithms of the median filter and threshold scheme. This implies that the implemented method can be useful for investigating not only the short-term variations based on hourly chl-a data but also long-term variabilities with composite products of the GOCI chl-a concentration over the span of a decade. Full article

(This article belongs to the Special Issue Optical Oceanographic Observation)

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17 pages, 4981 KiB

Open AccessArticle

Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

by Jae-Jin Park, Kyung-Ae Park, Pierre-Yves Foucher, Philippe Deliot, Stephane Le Floch, Tae-Sung Kim, Sangwoo Oh and Moonjin Lee

Remote Sens. 2021, 13(2), 318; https://doi.org/10.3390/rs13020318 - 18 Jan 2021

Cited by 8 | Viewed by 2957

Abstract

With an increase in the overseas maritime transport of hazardous and noxious substances (HNSs), HNS-related spill accidents are on the rise. Thus, there is a need to completely understand the physical and chemical properties of HNSs. This can be achieved through establishing a library of spectral characteristics with respect to wavelengths from visible and near-infrared (VNIR) bands to shortwave infrared (SWIR) wavelengths. In this study, a ground HNS measurement experiment was conducted for artificially spilled HNS by using two hyperspectral cameras at VNIR and SWIR wavelengths. Representative HNSs such as styrene and toluene were spilled into an outdoor pool and their spectral characteristics were obtained. The relative ratio of HNS to seawater decreased and increased at 550 nm and showed different constant ratios at the SWIR wavelength. Noise removal and dimensional compression procedures were conducted by applying principal component analysis on HNS hyperspectral images. Pure HNS and seawater endmember spectra were extracted using four spectral mixture techniques—N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA). The accuracy of detection values of styrene and toluene through the comparison of the abundance fraction were 99.42% and 99.56%, respectively. The results of this study are useful for spectrum-based HNS detection in marine HNS accidents. Full article

(This article belongs to the Special Issue Optical Oceanographic Observation)

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18 pages, 5704 KiB

Open AccessArticle

Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data

by Min-Sun Lee, Kyung-Ae Park and Fiorenza Micheli

Remote Sens. 2021, 13(2), 298; https://doi.org/10.3390/rs13020298 - 16 Jan 2021

Cited by 13 | Viewed by 3638

Abstract

Red tide causes significant damage to marine resources such as aquaculture and fisheries in coastal regions. Such red tide events occur globally, across latitudes and ocean ecoregions. Satellite observations can be an effective tool for tracking and investigating red tides and have great potential for informing strategies to minimize their impacts on coastal fisheries. However, previous satellite-based red tide detection algorithms have been mostly conducted over short time scales and within relatively small areas, and have shown significant differences from actual field data, highlighting a need for new, more accurate algorithms to be developed. In this study, we present the newly developed normalized red tide index (NRTI). The NRTI uses Geostationary Ocean Color Imager (GOCI) data to detect red tides by observing in situ spectral characteristics of red tides and sea water using spectroradiometer in the coastal region of Korean Peninsula during severe red tide events. The bimodality of peaks in spectral reflectance with respect to wavelengths has become the basis for developing NRTI, by multiplying the heights of both spectral peaks. Based on the high correlation between the NRTI and the red tide density, we propose an estimation formulation to calculate the red tide density using GOCI data. The formulation and methodology of NRTI and density estimation in this study is anticipated to be applicable to other ocean color satellite data and other regions around the world, thereby increasing capacity to quantify and track red tides at large spatial scales and in real time. Full article

(This article belongs to the Special Issue Optical Oceanographic Observation)

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21 pages, 5170 KiB

Open AccessArticle

Underwater Topography Inversion in Liaodong Shoal Based on GRU Deep Learning Model

by Zihao Leng, Jie Zhang, Yi Ma and Jingyu Zhang

Remote Sens. 2020, 12(24), 4068; https://doi.org/10.3390/rs12244068 - 11 Dec 2020

Cited by 11 | Viewed by 2622

Abstract

The Liaodong Shoal in the east of the Bohai Sea has obvious water depth variation. The clear shallow water area and deep turbid area coexist, which is characterized by complex submarine topography. The traditional semi-theoretical and semi-empirical models are often difficult to provide optimal inversion results. In this paper, based on the traditional principle of water depth inversion in shallow areas, a new framework is proposed in combination with the deep turbid sea area. This new framework extends the application of traditional optical water depth inversion methods, can meet the needs of the depth inversion work in the composite sea environment. Moreover, the gate recurrent unit (GRU) deep-learning model is introduced to approximate the unified inversion model by numerical calculation. In this paper, based on the above-mentioned inversion framework, the water depth inversion work is processed by using the wide range images of GF-1 satellite, then the relevant analysis and accuracy evaluation are carried out. The results show that: (1) for the overall water depth inversion, the determination coefficient R² is higher than 0.9 and the MRE is lower than 20% are obtained, and the evaluation index shows that the GRU model can better retrieve the underwater topography of this region. (2) Compared with the traditional log-linear model, Stumpf model, and multi-layer feedforward neural network, the GRU model was significantly improved in various evaluation indices. (3) The model has the best inversion performance in the 24–32 m-depth section, with a MRE of about 4% and a MAE of about 1.42 m, which is more suitable for the inversion work in the comparative section area. (4) The inversion diagram indicates that this model can well reflect the regional seabed characteristics of multiple radial sand ridges, and the overall inversion result is excellent and practical. Full article

(This article belongs to the Special Issue Optical Oceanographic Observation)

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14 pages, 6354 KiB

Open AccessArticle

Satellite Observations of Typhoon-Induced Sea Surface Temperature Variability in the Upwelling Region off Northeastern Taiwan

by Yi-Chun Kuo, Ming-An Lee and Yi Chang

Remote Sens. 2020, 12(20), 3321; https://doi.org/10.3390/rs12203321 - 12 Oct 2020

Cited by 4 | Viewed by 2462

Abstract

Typhoon-induced cooling in the cold dome region off northeastern Taiwan has a major influence on ocean biogeochemistry. It has previously been studied using numerical models and hydrographic observations. Strong cooling is related to upwelling of the Kuroshio subsurface water accompanied by the westward intrusion of the continental shelf by Kuroshio water. By employing satellite observations, local measurements, and a reanalysis of model data, this study compared 18 typhoon-induced sea surface temperature (SST) responses in the cold dome region and determined that SST responses can differ dramatically depending on the relative location of a typhoon path, the Kuroshio Current, and the topography off northeastern Taiwan. The results indicated that local westward and northward wind stress is positively correlated with upwelling intensity. Decreased northward transport in the Taiwan Strait created a condition that favored the Kuroshio intrusion, thus, the typhoon-induced change in Taiwan Strait transport was also positively correlated with the intensity of cooling. However, the strength of Ekman pumping was weakly correlated with the intensity of SST cooling. Nevertheless, Ekman pumping helped reduce the cover of warm water, facilitating the intrusion of the Kuroshio Current. Full article

(This article belongs to the Special Issue Optical Oceanographic Observation)

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19 pages, 5960 KiB

Open AccessArticle

Application of Spectral Mixture Analysis to Vessel Monitoring Using Airborne Hyperspectral Data

by Jae-Jin Park, Tae-Sung Kim, Kyung-Ae Park, Sangwoo Oh, Moonjin Lee and Pierre-Yves Foucher

Remote Sens. 2020, 12(18), 2968; https://doi.org/10.3390/rs12182968 - 11 Sep 2020

Cited by 6 | Viewed by 2758

Abstract

As marine transportation has increased in coastal regions, maritime accidents associated with vessels have steadily increased. Remotely sensed satellite or airborne images can aid rapid vessel monitoring over wide areas at high resolutions. In this study, airborne hyperspectral experiments were performed to detect marine vessels mainly including fishing boat and yacht by applying pixel-based mixture techniques and to estimate the size of the vessels through an objective ellipse fitting method. Various spectral libraries of marine objects and seawaters were constructed through in-situ experiments for spectral analysis of the internal structures of vessels. The hyperspectral images were dimensionally reduced through principal component analysis. Several hyperspectral mixture algorithms, such as N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA), were used for the detection of vessels. The N-FINDR and VCA techniques presented a total of 14 vessels, the ICA technique detected seven vessels, and the PPI technique detected two vessels. The pixel-based probability of detection (POD) and false alarm ratio (FAR) for all 14 vessels were 96.40% and 4.30%, respectively. The sizes of the vessels were estimated by extracting the boundaries of the vessels through a two-dimensional gradient and applying the ellipse fitting method. Compared with the digital mapping camera (DMC) images with resolutions of 0.10 m, the root-mean-square errors of the length and width of the vessels were approximately 1.19 m and 0.81 m, respectively. The application of spectral mixing methods provided a high probability of detecting the objects, as well as the overall structures of the decks of the vessels. Full article

(This article belongs to the Special Issue Optical Oceanographic Observation)

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