scholarly journals Decadal Measurements of the First Geostationary Ocean Color Satellite (GOCI) Compared with MODIS and VIIRS Data

2021 ◽  
Vol 14 (1) ◽  
pp. 72
Author(s):  
Myung-Sook Park ◽  
Seonju Lee ◽  
Jae-Hyun Ahn ◽  
Sun-Ju Lee ◽  
Jong-Kuk Choi ◽  
...  
Keyword(s):  
Yellow Sea ◽  
North Pacific ◽  
Western North Pacific ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
East Sea ◽  
The Yellow Sea ◽  
Chl A ◽  
Correction Technique ◽  
Band Ratios

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.

scholarly journals Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

2019 ◽  
Vol 11 (14) ◽  
pp. 1631 ◽  
Author(s):  
Xiaocan Huang ◽  
Jianhua Zhu ◽  
Bing Han ◽  
Cédric Jamet ◽  
Zhen Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Near Infrared ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
The Yellow Sea ◽  
Optical Parameters ◽  
Band Ratio ◽  
Percentage Difference

Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

scholarly journals Improved Detection of Tiny Macroalgae Patches in Korea Bay and Gyeonggi Bay by Modification of Floating Algae Index

2018 ◽  
Vol 10 (9) ◽  
pp. 1478
Author(s):  
Ahmed Harun-Al-Rashid ◽  
Chan-Su Yang
Keyword(s):  
High Resolution ◽  
Yellow Sea ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ocean Color ◽  
The Yellow Sea ◽  
Small Scale ◽  
Landsat 8 ◽  
Modified Method ◽  
Macroalgal Bloom

This work focuses on the detection of tiny macroalgae patches in the eastern parts of the Yellow Sea (YS) using high-resolution Landsat-8 images from 2014 to 2017. In the comparison between floating algae index (FAI) and normalized difference vegetation index (NDVI) better detection by FAI was observed, but many tiny patches still remained undetected. By applying a modification on the FAI around 12% to 27% increased and correct detection of macroalgae is achieved from 35 images compared to the original. Through this method many scattered tiny patches were detected in June or July in Korea Bay and Gyeonggi Bay. Though it was a small-scale phenomenon they occurred in the similar period of macroalgal bloom occurrence in the YS. Thus, by using this modified method we could detect macroalgae in the study areas around one month earlier than the previously used Geostationary Ocean Color Imager NDVI-based detection. Later, more macroalgae patches including smaller ones occupying increased areas were detected. Thus, it seems that those macroalgae started growing locally from tiny patches rather than being transported from the western parts of the YS. Therefore, this modified FAI could be used for the precise detection of macroalgae.

Lack of Genetic Stock Discretion in Pacific Cod (Gadus macrocephalus)

1987 ◽  
Vol 44 (3) ◽  
pp. 490-498 ◽  
Author(s):  
W. Stewart Grant ◽  
Chang Ik Zhang ◽  
Tokimasa Kobayashi ◽  
Gunnar Ståhl
Keyword(s):  
Pacific Ocean ◽  
Bering Sea ◽  
Yellow Sea ◽  
North Pacific ◽  
Gene Diversity ◽  
North Pacific Ocean ◽  
The Yellow Sea ◽  
Pacific Cod ◽  
Pacific Cod Gadus Macrocephalus ◽  
Gadus Macrocephalus

We examined the ocean-wide genetic population structure of Pacific cod (Gadus macrocephalus) using electrophoretically detectable population markers at 41 protein loci. Samples were collected at 11 locations extending over most of the species's range from the Yellow Sea, Korea, to Puget Sound, Washington. Seven loci (17%) were polymorphic using the 0.05 criterion of polymorphism. Sample heterozygosities ranged from 0.018 to 0.041 and averaged 0.025 (±0.013). Two major genetic groups were detected: a western North Pacific Ocean (Asian) group and an eastern North Pacific group (including Bering Sea stocks). The UPGMA Nei genetic distance, D, (based on 41 loci) between samples from these two groups was 0.025, and this subdivision accounted for 18.9% of the total gene diversity. Genetic differentiation between these two groups appears to reflect the barrier effects of coastal Pleistocene glaciation. Morphological and tagging data from other studies suggest that Pacific cod are subdivided into several independent stocks. In this study, significant allele-frequency differences were detected between samples within the eastern North Pacific Ocean, the Bering Sea, and the western North Pacific Ocean, but not between stocks on a larger geographic scale. The average Nei genetic distance (based on 41 loci) between samples was only 0.0007, and a gene diversity analysis indicated that within-region differences represented only 3.1% of the total gene diversity. There was a slightly greater amount of differentiation between the Yellow Sea and the Sea of Japan (D = 0.0041), which reflects geographic isolation of the Yellow Sea stock not found in other areas. From theoretical considerations, little genetic divergence between stocks of Pacific Cod is expected because random genetic drift in large population sizes is insignificant and because migration between areas prevents genetic differentiation.

Relocation of Offshore Earthquakes around the Korean Peninsula using Multiple Seismic Arrays: Case Examples for East Sea and Yellow Sea Regions

2020 ◽  
Author(s):  
Shu-Chioung Chiu ◽  
Jer-Ming Chiu ◽  
Kwanghee Kim ◽  
Suyoung Kang
Keyword(s):  
Yellow Sea ◽  
East Sea ◽  
The Yellow Sea ◽  
Plate Boundaries ◽  
Apparent Velocity ◽  
Seismic Stations ◽  
Velocity Models ◽  
Spatial Coverage ◽  
Earthquake Hazard Assessment ◽  
Regional Earthquake

&lt;p&gt;Yellow Sea and East Sea regions near Korea are two of the most seismically active marginal seas in the Far East.&amp;#160; While offshore earthquakes in the Yellow Sea may be attributed to potential micro-plate boundaries, East Sea earthquakes may be associated to the seaward extension of many active faults on land or the deformation boundary between oceanic and continental crust.&amp;#160; However, offshore earthquake locations using local seismic network are always subjecting to large uncertainties due to poor spatial coverage of seismic stations, discrepancies on velocity models, and limitations on traditional location technologies. &amp;#160;For instance, it is not uncommon that the same earthquake within Yellow Sea may be reported independently more than tens to hundreds of km apart in Chinese and Korean catalogs while there is no mechanism for earthquake data exchange between the two countries. &amp;#160;&amp;#160;Multiple seismic array method can be applied to improve epicenter location of offshore earthquakes.&amp;#160; Seismic stations in Korea can be integrated into three arrays based on their latitude. Apparent azimuths and apparent velocities of the incoming seismic waves (mainly Pn) from a regional earthquake to each array can be reliably determined.&amp;#160; Epicenter of a regional earthquake can thus be located by tracing seismic rays following the back azimuths derived from multiple arrays. &amp;#160;Offshore earthquakes in the East Sea and Yellow Sea regions are located at shallow depth within crust that Pn waves are expected to be the first arrival phase at many Korean stations.&amp;#160; Thus, offshore earthquakes can be reasonably located using Pn arrivals.&amp;#160; In the Yellow Sea case, the apparent velocity ~8.0 km/sec is observed for all arrays suggesting a typical continental Pn waves propagating across the continent-continent transition region into Korea.&amp;#160; In the East Sea case, the apparent velocity of ~6.8 km/sec or lower is observed for all arrays suggesting a typical oceanic Pn wave propagating across the oceanic-continental margin into Korea.&amp;#160; A better relocated earthquake location in the offshore region is essential for our understanding of regional tectonics and earthquake hazard assessment.&lt;/p&gt;

Bacillus hwajinpoensis sp. nov. and an unnamed Bacillus genomospecies, novel members of Bacillus rRNA group 6 isolated from sea water of the East Sea and the Yellow Sea in Korea

2004 ◽  
Vol 54 (3) ◽  
pp. 803-808 ◽  
Author(s):  
Jung-Hoon Yoon ◽  
In-Gi Kim ◽  
Kook Hee Kang ◽  
Tae-Kwang Oh ◽  
Yong-Ha Park
Keyword(s):  
16S Rdna ◽  
Yellow Sea ◽  
Sea Water ◽  
Diaminopimelic Acid ◽  
East Sea ◽  
The Yellow Sea ◽  
Phenotypic Properties ◽  
Rdna Sequences ◽  
16S Rdna Sequences ◽  
Group 6

Two Gram-positive or -variable, endospore-forming, slightly halophilic strains (SW-72T and SW-93) were isolated from sea water of the East Sea and the Yellow Sea in Korea, respectively, and subjected to polyphasic taxonomic study. Both strains had cell-wall peptidoglycan that was based on meso-diaminopimelic acid and MK-7 as the predominant menaquinone. The two strains contained large amounts of saturated and branched fatty acids, with anteiso-C15 : 0 as the major fatty acid. The DNA G+C contents of strains SW-72T and SW-93 were 40·9 and 41·0 mol%, respectively. Phylogenetic analysis based on 16S rDNA sequences showed that strains SW-72T and SW-93 fall within the radiation of the cluster that comprises members of the genus Bacillus, particularly Bacillus rRNA group 6. There were five nucleotide differences between the 16S rDNA sequences of strains SW-72T and SW-93. The mean level of DNA–DNA relatedness between strains SW-72T and SW-93 was 21·5 %. Strains SW-72T and SW-93 showed 93·1–95·2 % 16S rDNA sequence similarity to the type strains of Bacillus species that are assigned to rRNA group 6. Strains SW-72T and SW-93 could not be differentiated clearly by using their phenotypic properties. On the basis of phenotypic properties, phylogeny and genomic data, it is proposed that strain SW-72T (=KCCM 41641T=JCM 11807T) should be placed in the genus Bacillus as the type strain of a novel species, Bacillus hwajinpoensis sp. nov., and that strain SW-93 (=KCCM 41640=JCM 11806) should be placed in the genus Bacillus as an unnamed Bacillus genomospecies.

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