remotesensing-logo

Journal Browser

Journal Browser

Diurnal to Decadal Observation of the Ocean with Geostationary Satellite Sensors

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

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 20876

Special Issue Editors


E-Mail Website
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

E-Mail Website
Guest Editor
1. NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, USA
2. Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, USA
Interests: remote sensing; ocean color; bio-optical algorithms; water quality; phytoplankton productivity; human/climate-induced changes in marine ecosystem
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The first geostationary ocean color satellite sensor, Geostationary Ocean Color Imager (GOCI), onboard the Korean Communication, Ocean, and Meteorological Satellite (COMS), has been providing hourly measurements (eight times per day during the day time) of ocean color with a relatively high spatial resolution at 500 m. GOCI imageries have been widely used to study biological/biogeochemical processes and water quality properties in the Northwestern Pacific Ocean, particularly, diurnal changes in coastal ocean waters, and have showed the great potential of geostationary ocean color sensors to understand short-term variabilities. GOCI-derived products, including floating macroalgae, aerosol optical properties, and marine fog, are valuable for early warning with respect to marine and atmospheric issues. The operation of GOCI observations now provides 10 years of ocean color products to investigate decadal changes in coastal and ocean environments.

The next Korean geostationary ocean color sensor (GOCI-II) with more bands (13 bands from UV to NIR) and higher spatial resolution (250 m at nadir) launched in February 2020. GOCI-II will continue to provide short-term to decadal monitoring in the marine ecosystems of the marginal seas of the Northwestern Pacific Ocean. Moreover, recent advancements in meteorological imagers, such as Advance Himawari Imager onboard the Himawary-8 and -9 satellites and Advanced Meteorological Imager onboard Geo-KOMSAT-2A, provide three visible bands in addition to infrared bands, which will open new opportunities to study fast varying processes in coastal and in-land waters.

For this Special Issue, we encourage authors to contribute papers on all ocean color applications with GOCI and other geostationary satellite sensors, including diurnal to decadal variabilities in water quality, phytoplankton productivity, biological/biogeochemical properties, and fisheries in the marine and coastal ecosystem. We also welcome papers on all relevant subjects, such as sensor calibration, atmospheric correction, validation/evaluation of the oceanic color products, and development of optical/biogeochemical algorithms.

Dr. Youngje Park
Dr. SeungHyun Son
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

  • GOCI
  • Geostationary satellite
  • Remote sensing
  • Ocean color
  • Diurnal changes
  • Decadal changes
  • Marine ecosystem
  • Phytoplankton productivity
  • Water quality
  • Validation/evaluation

Published Papers (8 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

24 pages, 6245 KiB  
Article
Meteorological Satellite Observations Reveal Diurnal Exceedance of Water Quality Guideline Thresholds in the Coastal Great Barrier Reef
by Larissa Patricio-Valerio, Thomas Schroeder, Michelle J. Devlin, Yi Qin and Scott Smithers
Remote Sens. 2023, 15(9), 2335; https://doi.org/10.3390/rs15092335 - 28 Apr 2023
Viewed by 1830
Abstract
The Great Barrier Reef (GBR) is a marine protected area subject to natural and anthropogenic disturbances. Water quality is critical for the health and protecting resilience of GBR coral ecosystems against the synergistic and cumulative pressures of tropical cyclones, marine heat waves, and [...] Read more.
The Great Barrier Reef (GBR) is a marine protected area subject to natural and anthropogenic disturbances. Water quality is critical for the health and protecting resilience of GBR coral ecosystems against the synergistic and cumulative pressures of tropical cyclones, marine heat waves, and outbreaks of crown-of-thorns starfish. The concentration of Total Suspended Solids (TSS) is a key water quality parameter measured at multiple spatio-temporal scales from in situ probes to satellite observations. High TSS concentrations can adversely impact coral and seagrasses on the inshore GBR. We present diurnal TSS derived from Himawari-8 Geostationary satellite observations at 10 min frequency and demonstrate its applicability for improved monitoring of GBR water quality. Diurnal TSS obtained from Himawari-8 observations were compared to TSS computed from in situ bio-optical measurements at the Lucinda Jetty Coastal Observatory (LJCO). The coastal waters at LJCO experience diurnal variability of TSS (~7 mg L−1), where magnitude peaks followed the slack tides, and the largest diurnal changes were associated with freshwater discharge residuals from the wet season. Exceedance maps revealed that TSS is above guideline thresholds in the open coastal and mid-shelf waters for ~60% of the valid monthly observations, including during dry season months. Full article
Show Figures

Figure 1

22 pages, 7309 KiB  
Article
A Novel Multi-Candidate Multi-Correlation Coefficient Algorithm for GOCI-Derived Sea-Surface Current Vector with OSU Tidal Model
by He Cui, Jianyu Chen, Zhenyi Cao, Haiqing Huang and Fang Gong
Remote Sens. 2022, 14(18), 4625; https://doi.org/10.3390/rs14184625 - 16 Sep 2022
Cited by 2 | Viewed by 1263
Abstract
The maximum cross-coefficient (MCC) algorithm based on the template matching technique is a typical algorithm for obtaining the sea-surface currents (SSCs) in marginal seas. However, this algorithm has mismatches between images in highly turbid water. In this study, we implemented the MCC algorithm [...] Read more.
The maximum cross-coefficient (MCC) algorithm based on the template matching technique is a typical algorithm for obtaining the sea-surface currents (SSCs) in marginal seas. However, this algorithm has mismatches between images in highly turbid water. In this study, we implemented the MCC algorithm to Geostationary Ocean Color Imager-derived total suspended matter to obtain the SSCs in the Yellow Sea and the East China Sea. We propose a novel vector optimization algorithm, which is combined with the accurate estimate of tidal ellipses from the OSU tidal model. This method considers the three greatest candidate acquisitions from multi-correlation coefficients as potential vectors. The rotation direction of the vector within the tidal oscillation is used to identify and substitute for the spurious vector. The obtained average speed of SSC reached 0.60 m/s, which was close to the buoy-measured average speed of 0.58 m/s. Compared with the existing spurious vector eliminating method, the average angular error was improved by 20%, and the average relative amplitude error was improved by 4% in our case study. On the basis of ensuring data integrity, the inversion accuracy was improved. Full article
Show Figures

Figure 1

21 pages, 44901 KiB  
Article
Evaluation of GOCI Remote Sensing Reflectance Spectral Quality Based on a Quality Assurance Score System in the Bohai Sea
by Xiaoyan Liu, Qian Yang, Yunhua Wang and Yu Zhang
Remote Sens. 2022, 14(5), 1075; https://doi.org/10.3390/rs14051075 - 22 Feb 2022
Cited by 2 | Viewed by 1796
Abstract
In the application of ocean color remote sensing, remote sensing reflectance spectral (Rrs(λ)) is the most important and basic parameter for the development of bio-optical algorithms. Atmospheric correction of ocean color data is a key factor in obtaining accurate water [...] Read more.
In the application of ocean color remote sensing, remote sensing reflectance spectral (Rrs(λ)) is the most important and basic parameter for the development of bio-optical algorithms. Atmospheric correction of ocean color data is a key factor in obtaining accurate water Rrs(λ) data. Based on the QA (quality assurance) score spectral quality evaluation system, the quality of Rrs(λ) spectral of GOCI (Geostationary Ocean Color Imager) obtained from four atmospheric-correction algorithms in the Bohai Sea were evaluated and analyzed in this paper. The four atmospheric-correction algorithms are the NASA (National Aeronautics and Space Administration) standard near-infrared atmospheric-correction algorithm (denoted as Seadas—Default), MUMM (Management Unit of the North Sea Mathematical Models, denoted as Seadas—MUMM), and the standard atmospheric-correction algorithms of KOSC GOCI GDPS2.0 (denoted as GDPS2.0) and GDPS1.3 (denoted as GDPS1.3). It is shown that over 90% of the Rrs(λ) data are in good quality with a score ≥4/6 for the GDPS1.3 algorithm. The probability of Rrs(λ) with a QA score of 1 is significantly higher for the GDPS1.3 algorithm (57.36%), compared with Seadas—Default (37.91%), Seadas—MUMM (35.96%), and GDPS2.0 (33.05%). The field and MODIS measurements of Rrs(λ) were compared with simultaneous GOCI Rrs(λ), and they demonstrate that the QA score system is useful in evaluating the spectral shape of Rrs(λ). The comparison results indicate that higher QA scores have higher accuracy of the Rrs band ratio. The QA score system is helpful to develop and evaluate bio-optical algorithms based on the band ratio. The hourly variation of QA score from UTC 00:16 to 07:16 was investigated as well, and it demonstrates that the data quality of GOCI Rrs(λ) can vary in an hour scale. The GOCI data with high quality should be selected with caution when studying the hourly variation of biogeochemical properties in the Bohai Sea from GOCI measurements. Full article
Show Figures

Graphical abstract

18 pages, 99657 KiB  
Article
Impacts of the Kuroshio and Tidal Currents on the Hydrological Characteristics of Yilan Bay, Northeastern Taiwan
by Po-Chun Hsu, Hung-Jen Lee and Ching-Yuan Lu
Remote Sens. 2021, 13(21), 4340; https://doi.org/10.3390/rs13214340 - 28 Oct 2021
Cited by 4 | Viewed by 2235
Abstract
Yilan Bay is in the northeast corner of Taiwan at the junction of the East China Sea (ECS) and the Pacific Ocean. This study clarified the composition of water masses adjacent to Yilan Bay. The upper seawater in the bay is characterized by [...] Read more.
Yilan Bay is in the northeast corner of Taiwan at the junction of the East China Sea (ECS) and the Pacific Ocean. This study clarified the composition of water masses adjacent to Yilan Bay. The upper seawater in the bay is characterized by Kuroshio surface water, Taiwan warm current water, and shelf mixed water masses. The flow field in this area is mainly determined by the inter-actions among the northeastern Taiwan countercurrent, Kuroshio Current (KC), and tidal currents. The fall season is the main rainfall period in Yilan Bay, which causes a large amount of river runoff and a further increase in chlorophyll concentration, and the salinity of the upper water layer is observed much lower than other seasons. Water with a high chlorophyll concentration can flow into the ECS with ebb currents and the KC with ebb and flood currents. Combining hourly geosynchronous ocean color imager data and numerical simulation flow field helps us understand short-term changes of chlorophyll concentration. The trajectories of the drifters and virtual particle simulations help us understand the sources and movement of ocean currents in Yilan Bay. The seasonal swing of the KC path outside the bay is an important factor affecting the flow field and hydrological characteristics. Full article
Show Figures

Figure 1

19 pages, 7804 KiB  
Article
Potential Associations between Low-Level Jets and Intraseasonal and Semi-Diurnal Variations in Coastal Chlorophyll—A over the Beibuwan Gulf, South China Sea
by Shuhong Liu, Danling Tang, Hong Yan, Guicai Ning, Chengcheng Liu and Yuanjian Yang
Remote Sens. 2021, 13(6), 1194; https://doi.org/10.3390/rs13061194 - 20 Mar 2021
Cited by 1 | Viewed by 2814
Abstract
Low-level jet (LLJ) significantly affects the synoptic-scale hydrometeorological conditions in the South China Sea, although the impact of LLJs on the marine ecological environment is still unclear. We used multi-satellite observation data and meteorological reanalysis datasets to study the potential impact of LLJs [...] Read more.
Low-level jet (LLJ) significantly affects the synoptic-scale hydrometeorological conditions in the South China Sea, although the impact of LLJs on the marine ecological environment is still unclear. We used multi-satellite observation data and meteorological reanalysis datasets to study the potential impact of LLJs on the marine biophysical environment over the Beibuwan Gulf (BBG) in summer during 2015–2019. In terms of the summer average, the sea surface wind vectors on LLJ days became stronger in the southwesterly direction relative to those on non-LLJ days, resulting in enhanced Ekman pumping (the maximum upwelling exceeds 10 × 10−6 m s−1) in most areas of the BBG, accompanied by stronger photosynthetically active radiation (increased by about 20 μmol m−2 s−1) and less precipitation (decreased by about 3 mm day−1). These LLJ-induced hydrometeorological changes led to an increase of about 0.3 °C in the nearshore sea surface temperature and an increase of 0.1–0.5 mg m−3 (decrease of 0.1–0.3 mg m−3) in the chlorophyll-a (chl-a) concentrations in nearshore (offshore) regions. Intraseasonal and diurnal changes in the incidence and intensity of LLJs potentially resulted in changes in the biophysical ocean environment in nearshore regions on intraseasonal and semi-diurnal timescales. The semi-diurnal peak and amplitude of chl-a concentrations on LLJ days increased with respect to those on non-LLJ days. Relative to the southern BBG, LLJ events exhibit greater impacts on the northern BBG, causing increases of the semi-diurnal peak and amplitude with 1.5 mg m−3 and 0.7 mg m−3, respectively. This work provides scientific evidence for understanding the potential mechanism of synoptic-scale changes in the marine ecological environment in marginal seas with frequent LLJ days. Full article
Show Figures

Graphical abstract

20 pages, 13401 KiB  
Article
Using TPI to Map Spatial and Temporal Variations of Significant Coastal Upwelling in the Northern South China Sea
by Weian Shi, Zhi Huang and Jianyu Hu
Remote Sens. 2021, 13(6), 1065; https://doi.org/10.3390/rs13061065 - 11 Mar 2021
Cited by 15 | Viewed by 2364
Abstract
Based on Himawari-8 Sea Surface Temperature (SST) data and the semi-automatic Topographic Position Index (TPI)-based mapping method, this study maps the significant coastal upwelling in the northern South China Sea (NSCS). The results show that the Minnan coastal upwelling mainly occurs within 100 [...] Read more.
Based on Himawari-8 Sea Surface Temperature (SST) data and the semi-automatic Topographic Position Index (TPI)-based mapping method, this study maps the significant coastal upwelling in the northern South China Sea (NSCS). The results show that the Minnan coastal upwelling mainly occurs within 100 km off the south coast of Fujian; the Yuedong coastal upwelling appears to the east of Pearl River Estuary, limited to the area shallower than 40 m; and the Qiongdong coastal upwelling occurs most frequently in the area shallower than 75 m off the east coast of Hainan Island. Based on the results, this paper quantitatively describes the temporal and spatial variations of upwelling duration, influence area, upwelling SST anomaly, and chlorophyll-a (Chl-a) increase. Different coastal upwelling regions in the NSCS are significantly different in characteristics. The Qiongdong coastal upwelling has the longest duration and occurs most frequently, the Yuedong coastal upwelling has the largest influence area and Chl-a increase, and the Minnan coastal upwelling is quite strong in the NSCS. Full article
Show Figures

Graphical abstract

24 pages, 23647 KiB  
Article
Diurnal to Seasonal Variations in Ocean Chlorophyll and Ocean Currents in the North of Taiwan Observed by Geostationary Ocean Color Imager and Coastal Radar
by Po-Chun Hsu, Ching-Yuan Lu, Tai-Wen Hsu and Chung-Ru Ho
Remote Sens. 2020, 12(17), 2853; https://doi.org/10.3390/rs12172853 - 02 Sep 2020
Cited by 9 | Viewed by 4142
Abstract
The waters in the north of Taiwan are located at the southern end of the East China Sea (ECS), adjacent to the Taiwan Strait (TS), and the Kuroshio region. To understand the physical dynamic process of ocean currents and the temporal and spatial [...] Read more.
The waters in the north of Taiwan are located at the southern end of the East China Sea (ECS), adjacent to the Taiwan Strait (TS), and the Kuroshio region. To understand the physical dynamic process of ocean currents and the temporal and spatial distribution of the ocean chlorophyll concentration in the north of Taiwan, hourly coastal ocean dynamics applications radar (CODAR) flow field data and geostationary ocean color imager (GOCI) data are analyzed here. According to data from December 2014 to May 2020, the water in the TS flows along the northern coast of Taiwan into the Kuroshio region with a velocity of 0.13 m/s in spring and summer through the ECS. In winter, the Kuroshio invades the ECS shelf, where the water flows into the TS through the ECS with a velocity of 0.08 m/s. The seasonal variation of ocean chlorophyll concentration along the northwestern coast of Taiwan is obvious, where the average chlorophyll concentration from November to January exceeds 2.0 mg/m3, and the lowest concentration in spring is 1.4 mg/m3. It is apparent that the tidal currents in the north of Taiwan flow eastward and westward during ebb and flood periods, respectively. Affected by the background currents, the flow velocity exhibits significant seasonal changes, namely, 0.43 m/s in summer and 0.27 m/s in winter during the ebb period and is 0.26 m/s in summer and 0.45 m/s in winter during the flood period. The chlorophyll concentration near the shore is also significantly affected by the tidal currents. Based on CODAR data, virtual drifter experiments, and GOCI data, this research provides novel and important knowledge of ocean current movement process in the north of Taiwan and indicates diurnal to seasonal variations in the ocean chlorophyll concentration, facilitating future research on the interaction between the TS, ECS, and Kuroshio. Full article
Show Figures

Graphical abstract

Other

Jump to: Research

10 pages, 25317 KiB  
Technical Note
An Objective Method with a Continuity Constraint for Improving Surface Velocity Estimates from the Geostationary Ocean Color Imager
by Zifeng Hu, Lan Li, Jun Zhao and Dongxiao Wang
Remote Sens. 2022, 14(1), 14; https://doi.org/10.3390/rs14010014 - 21 Dec 2021
Cited by 3 | Viewed by 2453
Abstract
Mapping surface currents with high spatiotemporal resolution over a wide coverage is crucial for understanding ocean dynamics and associated biogeochemical processes. The most widely used algorithm for estimating surface velocities from sequential satellite observations is the maximum cross-correlation (MCC) method. However, many unrealistic [...] Read more.
Mapping surface currents with high spatiotemporal resolution over a wide coverage is crucial for understanding ocean dynamics and associated biogeochemical processes. The most widely used algorithm for estimating surface velocities from sequential satellite observations is the maximum cross-correlation (MCC) method. However, many unrealistic vectors still exist, despite the utilization of various filtering techniques. In this study, an objective method has been developed through the combination of MCC and multivariate optimum interpolation (MOI) analysis under a continuity constraint. The MCC method, with and without MOI, is applied to sequences of simulated sea surface temperature (SST) fields with a 1/48° spatial resolution over the East China Sea continental shelf. Integration of MOI into MCC reduces the average absolute differences between the model’s ‘actual’ velocity and the SST-derived velocity by 19% in relative magnitude and 22% in direction, respectively. Application of the proposed method to Geostationary Ocean Color Imager (GOCI) satellite observations produces good agreement between derived surface velocities and the Oregon State University (OSU) regional tidal model outputs. Our results demonstrate that the incorporation of MOI into MCC can provide a significant improvement in the reliability and accuracy of satellite-derived velocity fields. Full article
Show Figures

Figure 1

Back to TopTop