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Ocean Monitoring from Geostationary Platform
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Ocean Monitoring from Geostationary Platform

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 8792

Special Issue Editors

Dr. JongKuk Choi

E-Mail Website
Guest Editor
Korea Institute of Ocean Science & Technology, 385, Haeyang-ro, Yeongdo-gu, Busan Metropolitan City, Korea
Interests: application of GOCI to monitoring the marine environment
Prof. Dr. Wonkook Kim

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan Metropolitan City, Korea
Interests: ocean color; quantitative remote sensing; hyperspectral sensing

Special Issue Information

Dear Colleagues,

Since SeaSat and Tiros-N, the first ocean satellites that carry microwave, radar, and optical sensors such as AVHRR and CZCS, launched in 1978, the space-borne observation capability for oceans has remarkably advanced, making the synoptic data sets indispensable in all fields of oceanography and climate studies. While polar orbiting satellites have shown great success in capturing oceanic phenomena of global scale, geostationary satellites such as COMS, Geo-KOMPSAT-2, GOES, Himawari-8/-9, Insat, and the Fengyun series demonstrated unique benefits in ocean monitoring, providing high observation frequencies for designated areas. The merits of geostationary platforms will also be further exploited in upcoming missions such as GEO-XO by NOAA and GLIMR by NASA. 

This Special Issue endeavors to assemble novel studies that utilize advanced remote sensing technology to monitor ocean surface based upon the data from geostationary platforms. Numerous studies already demonstrated that the geostationary platforms have great advantages in monitoring short-term variations in the ocean, such as dynamics in suspended sediments, migration of harmful algal blooms, low salinity water intrusion, and formation of oceanic eddies and filaments.  The subjects of this issue include, but are not limited to

  • Investigation of local or regional oceanic phenomena of high temporal frequency, conducted with geostationary platforms such as high towers, hoverflies (tethered drone), helikites, and geostationary satellites.
  • Potentials and suggestions of new observation concepts for geostationary platforms
  • Challenges in data processing of geostationary satellite data caused by, for example, varying satellite and sun geometry, spherical atmosphere, etc.
  • New applications or products derived from geostationary satellites
  • Methodology and experiments for calibration and validation of data from geostationary platforms
  • Synergistic fusion with polar-orbiting satellite data or with any other physical models such as ocean current simulation, for a particular oceanic phenomenon

Dr. JongKuk Choi
Prof. Dr. Wonkook 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.

Keywords

  • Satellite-based ocean monitoring
  • Geostationary satellite
  • Diurnal variation
  • High temporal frequency

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Published Papers (3 papers)

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Research

19 pages, 12036 KiB  
Article
Improvement of GOCI-II Water Vapor Absorption Correction through Fusion with GK-2A/AMI Data
by Kyeong-Sang Lee, Myung-Sook Park, Jong-Kuk Choi and Jae-Hyun Ahn
Remote Sens. 2023, 15(8), 2124; https://doi.org/10.3390/rs15082124 - 17 Apr 2023
Cited by 4 | Viewed by 1941
Abstract
In remote sensing of the ocean color, in particular, in coarse-resolution global model simulations, atmospheric trace gases including water vapor are generally treated as auxiliary data, which create uncertainties in atmospheric correction. The second Korean geostationary satellite mission, Geo-Kompsat 2 (GK-2), is unique [...] Read more.
In remote sensing of the ocean color, in particular, in coarse-resolution global model simulations, atmospheric trace gases including water vapor are generally treated as auxiliary data, which create uncertainties in atmospheric correction. The second Korean geostationary satellite mission, Geo-Kompsat 2 (GK-2), is unique in combining visible and infrared observations from the second geostationary ocean color imager (GOCI-II) and the advanced meteorological imager (AMI) over Asia and the Pacific Ocean. In this study, we demonstrate that AMI total precipitable water (TPW) data to allow realistic water vapor absorption correction of GOCI-II color retrievals for the ocean. We assessed the uncertainties of two candidate TPW products for GOCI-II atmospheric correction using atmospheric sounding data, and then analyzed the sensitivity of four ocean-color products (remote sensing reflectance [Rrs], chlorophyll-a concentration [CHL], colored dissolved organic matter [CDOM], and total suspended sediment [TSS]) for GOCI-II water vapor transmittance correction using AMI and global model data. Differences between the TPW sources increased the mean absolute percentage error (MAPE) of Rrs from 2.97% to 6.43% in the blue to green bands, higher than the global climate observing system requirements (<5%) at 412 nm. By contrast, MAPE values of 3.53%, 6.18%, and 7.71% were increased to 6.63%, 13.53%, and 16.14% at high sun and sensor zenith angles for CHL, CDOM, and TSS, respectively. Uncertainty analysis provided similar results, indicating that AMI TPW produced approximately 3-fold lower error rates in ocean-color products than obtained using TPW values from the National Centers for Environmental Prediction. These results imply that AMI TPW can improve the accuracy and ability of GOCI-II ocean-color products to capture diurnal variability. Full article
(This article belongs to the Special Issue Ocean Monitoring from Geostationary Platform)
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12 pages, 2993 KiB  
Communication
On the Reconstruction of Missing Sea Surface Temperature Data from Himawari-8 in Adjacent Waters of Taiwan Using DINEOF Conducted with 25-h Data
by Yi-Chung Yang, Ching-Yuan Lu, Shih-Jen Huang, Thwong-Zong Yang, Yu-Cheng Chang and Chung-Ru Ho
Remote Sens. 2022, 14(12), 2818; https://doi.org/10.3390/rs14122818 - 12 Jun 2022
Cited by 8 | Viewed by 2098
Abstract
Satellite remote sensing sea surface temperature (SST) data are lost due to cloud cover. Missing data often cause inconvenience in subsequent applications and thus need to be reconstructed. In this study, the Data Interpolating Empirical Orthogonal Function (DINEOF) method was used to reconstruct [...] Read more.
Satellite remote sensing sea surface temperature (SST) data are lost due to cloud cover. Missing data often cause inconvenience in subsequent applications and thus need to be reconstructed. In this study, the Data Interpolating Empirical Orthogonal Function (DINEOF) method was used to reconstruct the hourly SST data missing from the Himawari-8 satellite in the waters near Taiwan. The SST characteristics in the waters around Taiwan are quite complex, with high SST at Kuroshio in the east of Taiwan and great variation in the SST west of Taiwan due to the influence of tides. Therefore, the analysis with DINEOF was conducted using 25-h data to match the tidal cycle. The influence of SST characteristics on the accuracy of SST reconstruction is also discussed. The results show that in the western sea area where the variation of SST is large, the average root-mean-square error of SST between the original SST and the reconstructed SST is the lowest and the average coefficient of determination is the highest. The accuracy of the reconstructed SST is positively correlated with the SST variation. Furthermore, the statistical results also show that the DINEOF method can effectively reconstruct the SST regardless of the missing data rate. Full article
(This article belongs to the Special Issue Ocean Monitoring from Geostationary Platform)
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25 pages, 11642 KiB  
Article
Decadal Measurements of the First Geostationary Ocean Color Satellite (GOCI) Compared with MODIS and VIIRS Data
by Myung-Sook Park, Seonju Lee, Jae-Hyun Ahn, Sun-Ju Lee, Jong-Kuk Choi and Joo-Hyung Ryu
Remote Sens. 2022, 14(1), 72; https://doi.org/10.3390/rs14010072 - 24 Dec 2021
Cited by 16 | Viewed by 3626
Abstract
The first geostationary ocean color data from the Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) have been accumulating for more than ten years from 2010. This study performs a multi-year quality assessment of GOCI chlorophyll-a (Chl-a) and [...] Read more.
The first geostationary ocean color data from the Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) have been accumulating for more than ten years from 2010. This study performs a multi-year quality assessment of GOCI chlorophyll-a (Chl-a) and radiometric data for 2012–2021 with an advanced atmospheric correction technique and a regionally specialized Chl-a algorithm. We examine the consistency and stability of GOCI, Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) level 2 products in terms of annual and seasonal climatology, two-dimensional frequency distribution, and multi-year time series. Overall, the GOCI agrees well with MODIS and VIIRS on annual and seasonal variability in Chl-a, as the central biological pattern of the most transparent waters over the western North Pacific, productive waters over the East Sea, and turbid waters over the Yellow Sea are reasonably represented. Overall, an excellent agreement is remarkable for western North Pacific oligotrophic waters (with a correlation higher than 0.91 for Chl-a and 0.96 for band-ratio). However, the sporadic springtime overestimation of MODIS Chl-a values compared with others is notable over the Yellow Sea and East Sea due to the underestimation of MODIS blue-green band ratios for moderate-high aerosol optical depth. The persistent underestimation of VIIRS Chl-a values compared with GOCI and MODIS occurs due to inherent sensor calibration differences. In addition, the artificially increasing trends in GOCI Chl-a (+0.48 mg m−3 per 9 years) arise by the decreasing trends in the band ratios. However, decreasing Chl-a trends in MODIS and VIIRS (−0.09 and −0.08 mg m−3, respectively) are reasonable in response to increasing sea surface temperature. The results indicate GOCI sensor degradation in the late mission period. The long-term application of the GOCI data should be done with a caveat, however; planned adjustments to GOCI calibration (2022) in the following GOCI-II satellite will essentially eliminate the bias in Chl-a trends. Full article
(This article belongs to the Special Issue Ocean Monitoring from Geostationary Platform)
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