Annual New Production of Phytoplankton Estimated from MODIS-Derived Nitrate Concentration in the East/Japan Sea

Joo, HuiTae; Lee, Dabin; Son, Seung Hyun; Lee, Sang‐Heon

doi:10.3390/rs10050806

Cited by 7 publications

(6 citation statements)

References 50 publications

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“…A high contribution from diatoms was suggested as a potential cause of the high f-ratio in the UB (Kwak et al, 2013b). Joo et al (2018) estimated annual new production in the EJS based on the MODISderived sea surface nitrate concentration and it was 7.1 molC m −2 yr −1 in the northern EJS and 5.5 molC m −2 yr −1 in the south (Table 3). Hahm and Kim (2001) estimated export production based on the upward flux of 3 He and nitrate into the euphotic zone (Jenkins, 1988) and reported 5.3 molC m −2 yr −1 in the southern EJS including the UB and YB.…”

Section: Primary Productionmentioning

confidence: 99%

Carbon cycling in the East Sea (Japan Sea): A review

Kim

Hwang²,

Kim³

et al. 2022

Front. Mar. Sci.

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The East Sea (also known as the Japan Sea; hereafter, EJS) is a semi-enclosed marginal sea surrounded by the Korean Peninsula, Russia, and the Japanese Islands. The EJS is connected to the Pacific through shallow straits. Thus, the EJS has its own thermohaline circulation and the characteristic biogeochemistry. The deep overturning circulation plays a critical role in carbon cycling including absorption of atmospheric CO2 and its sequestration into the interior of the sea. The turnover time of the deep EJS (>1000 m) is ~ hundred years and probably varies depending on physical climate forcing. Thus, the effect of climate change on oceanic processes may be more easily detected in the EJS. In this paper, we summarize the current understanding of carbon cycling in the EJS. We focus especially on the Ulleung Basin in the southwestern EJS, from which more extensive data are available. Notable features of carbon cycling in the EJS include the following: primary productivity and the export/production ratio are higher than in the adjacent Pacific; the EJS is a net sink of atmospheric CO2 and anthropogenic CO2 content is ~1% of the dissolved inorganic carbon inventory; dissolved inorganic carbon in the sea interior is mostly supplied by organic matter decomposition rather than CaCO3 dissolution and thus, the deep waters are vulnerable to acidification; N:P molar ratio of the deep waters is ~13, lower than the Redfield ratio; concentration of dissolved organic carbon is significantly higher than in the oceans; and sediment resuspension and lateral transport is an important component of sinking particulate organic carbon (POC) flux. Another important feature is the temporal trends observed for the last few decades. For example, pH, calcium carbonate saturation status, and dissolved oxygen concentration in the sea interior have decreased, whereas dissolved inorganic carbon and likely, the inventory of anthropogenic CO2 have increased. These temporal trends have an implication on better understanding of the processes occurring more slowly in the oceans. Brief suggestions for future research that will improve our understanding of carbon cycling and its variability are provided at the end of the paper.

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Section: Primary Productionmentioning

confidence: 99%

Carbon cycling in the East Sea (Japan Sea): A review

Kim

Hwang²,

Kim³

et al. 2022

Front. Mar. Sci.

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“…We used the monthly level-3 datasets including Chl-a and SST covering the EJS from January 2003 to December 2019 at 4 km of spatial resolution. A regional Sea Surface Nitrate (SSN) was calculated based on Chl-a and SST from ocean color data with the regional SSN algorithm [30]. Mixed layer depths of the EJS were obtained from ECCO (Estimating the Circulation and Climate of the Ocean; https://www.ecco-group.org/).…”

Section: Satellite Ocean Color Datamentioning

confidence: 99%

“…Moreover, multilinear regression is based on the assumption of independence between variables; supplementary variables for POC that were not independent among the variables by the PCA were excluded in our algorithm analysis. The satellite-derived SSN concentration is calculated by SST and Chl-a, according to Joo et al [30] algorithm. which is primarily controlled by SST.…”

Section: Derivation Of Protein Concentration Of Phytoplankton Algorithm In the Ejsmentioning

confidence: 99%

Satellite-Derived Protein Concentration of Phytoplankton in the Southwestern East/Japan Sea

Bae

Lee

Kang

et al. 2021

JMSE

Self Cite

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The cellular macromolecular contents and energy value of phytoplankton as primary food source determine the growth of higher trophic levels, affecting the balance and sustainability of oceanic food webs. Especially, proteins are more directly linked with basic functions of phytoplankton biosynthesis and cell division and transferred through the food chains. In recent years, the East/Japan Sea (EJS) has been changed dramatically in environmental conditions, such as physical and chemical characteristics, as well as biological properties. Therefore, developing an algorithm to estimate the protein concentration of phytoplankton and monitor their spatiotemporal variations on a broad scale would be invaluable. To derive the protein concentration of phytoplankton in EJS, the new regional algorithm was developed by using multiple linear regression analyses based on field-measured data which were obtained from 2012 to 2018 in the southwestern EJS. The major factors for the protein concentration were identified as chlorophyll-a (Chl-a) and sea surface nitrate (SSN) in the southwestern EJS. The coefficient of determination (r2) between field-measured and algorithm-derived protein concentrations was 0.55, which is rather low but reliable. The satellite-derived estimation generally follows the 1:1 line with the field-measured data, with Pearson’s correlation coefficient, which was 0.40 (p-value < 0.01, n = 135). No remarkable trend in the long-term annual protein concentration of phytoplankton was found in the study area during our observation period. However, some seasonal difference was observed in winter protein concentration between the 2003–2005 and 2017–2019 periods. The algorithm is developed for the regional East/Japan Sea (EJS) and could contribute to long-term monitoring for climate-associated ecosystem changes. For a better understanding of spatiotemporal variation in the protein concentration of phytoplankton in the EJS, this algorithm should be further improved with continuous field surveys.

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“…Di mana dengan teknologi ini dapat dideteksi cakupan area yang sangat luas dalam satu waktu perekaman yang sangat singkat. Dengan kemampuan sensor yang dapat merekam data citra secara multispektral atau multi kanal (Baiocchi, Brigante, Dominici, & Radicioni, 2012;Wang et al, 2011), dapat dikatakan bahwa dengan bantuan teknologi penginderaan jauh, pemantauan dari fenomena alam seperti pendeteksian populasi dari phytoplakton dapat dilakukan secara serentak dan dengan biaya yang relatif murah (Jin et al, 2017;Joo, Lee, Son, & Lee, 2018;Martinez, Raapoto, Maes, & Maamaatuaihutapu, 2018).…”

Section: Pendahuluanunclassified

Studi Perbandingan Konsentrasi Klorofil-a Di Semenanjung Blambangan Kabupaten Banyuwangi Menggunakan Data Citra Satelit Aqua Modis

Zainab

Wibisana

Casita

2019

JIG

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<p>Pemetaan kandungan klorofil-a di pesisir pantai banyak dilakukan dengan tujuan untuk mendapatkan gambaran tingkat kesuburan dari lingkungan perairan yang dimaksud. Tingkat kesuburan yang diharapkan dapat memberikan gambaran populasi dari perikanan tangkap yang dibutuhkan oleh masyarakat pesisir dalam meningkatkan kesejahteraan hidupnya. Penginderaan jauh sebagai salah satu teknologi yang baru telah memberikan banyak manfaat untuk pendeteksian dan pemetaan konsentrasi klorofil-a skala global. Tujuan dari penelitian ini untuk mendapatkan gambaran kandungan klorofil-a pada dua area yang berbeda yaitu di daerah Semenanjung Blambangan dan daerah Teluk Blambangan. Metode yang dikembangkan dari penelitian ini adalah penggunaan algoritma penginderaan jauh dan teknik regresi guna mendapatkan model matematis yang optimal untuk digunakan dalam peramalan kandungan klorofil-a di masa yang akan datang. Dari penelitian ini diperoleh model matematis yang paling sesuai untuk kondisi Teluk Blambangan yaitu pada panjang gelombang sinar hijau dengan nilai 531 nanometer (nm), di mana korelasi yang terbesar adalah Chlor-a = 21228*(Rrs_531)-42,371 dengan nilai R2 sebesar 0,7951 yang didapatkan dari panjang gelombang sinar hijau yaitu Rrs_531 dari citra satelit Aqua Modis Level-2. Fenomena ini dapat disimpulkan bahwa kandungan klorofil-a di semenanjung Blambangan pada bulan Agustus 2017 cukup signifikan di mana diperoleh rata-rata konsentrasi dari klorofil-a sebesar 18,45 ppt(mg/ m³) sehingga dapat diprediksikan bahwa populasi ikan yang ada di perairan tersebut juga meningkat pada bulan tersebut. Hasil akhir yang didapatkan berupa peta tematis kandungan klorofil-a untuk algoritma model linier, di samping itu persamaan matematis yang dihasilkan diharapkan dapat digunakan oleh para peneliti lainnya untuk mendapatkan model yang lebih akurat yang dapat menggambarkan kondisi riil di lapangan.</p><p> </p>

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Annual New Production of Phytoplankton Estimated from MODIS-Derived Nitrate Concentration in the East/Japan Sea

Cited by 7 publications

References 50 publications

Carbon cycling in the East Sea (Japan Sea): A review

Carbon cycling in the East Sea (Japan Sea): A review

Satellite-Derived Protein Concentration of Phytoplankton in the Southwestern East/Japan Sea

Studi Perbandingan Konsentrasi Klorofil-a Di Semenanjung Blambangan Kabupaten Banyuwangi Menggunakan Data Citra Satelit Aqua Modis

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