Estimation of Marine Primary Productivity From Satellite-Derived Phytoplankton Absorption Data

Ma, Shengming; Tao, Zui; Yang, Xiaofeng; Yu, Yang; Zhou, Xuan; Ma, Wentao

doi:10.1109/jstars.2014.2298863

Cited by 30 publications

(16 citation statements)

References 48 publications

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“…The absorption-based model NPP1 captured spatial and temporal dynamics in NPP within waters which represent moderate chlorophyll-low variation and high chlorophyll-high variation conditions, and hydrodynamic features such as counter currents and nutrient upwellings which are common to Australian shelf waters ( Figure 6; Hanson et al, 2005;Jones et al, 2015;van Ruth et al, 2010). Ma et al (2014) and Foukal and Thomas (2014) demonstrated that absorption-based models were sensitive to spatial patterns in NPP and produced reliable seasonal trends in NPP when applied in coastal regions, particularly where upwelling features were present. The NPP seasonal cycle obtained from the NPP1 model at Yongala and Port Hacking National Reference Stations conformed with our expectations of NPP dynamics and physical processes controlling phytoplankton at these locations.…”

Section: Potential For Further Application Of An Absorption-based Satmentioning

confidence: 99%

“…% ) are often estimated from laboratory based studies with algal monocultures (Marra et al, 2007(Marra et al, , 2003. Despite the limited data from mixed natural assemblages, absorption-based NPP models have reliably estimated NPP with low error, using in-situ data in the Equatorial Pacific, Southern Ocean, Western English Channel, California coast, North Atlantic, and North Pacific (Barnes et al, 2014;Ma et al, 2014;Marra et al, 2007). Absorption-based models have also been applied to satellite inverted a PHY coefficient to study the dynamics of NPP in the Southern Ocean and global eastern boundary upwelling regions (Hirata et al, 2009;Shang et al, 2010) and provide global estimates of annual NPP (Ma et al, 2014) To assess the utility of an absorption-based approach to estimate surface NPP in Australian coastal waters, this study compiled bio-optical and NPP data from tropical to temperate Australian locations.…”

Section: Introductionmentioning

confidence: 99%

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Phytoplankton absorption predicts patterns in primary productivity in Australian coastal shelf waters

Robinson

Cherukuru

Hardman-Mountford

et al. 2017

Estuarine, Coastal and Shelf Science

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The phytoplankton absorption coefficient (a PHY) has been suggested as a suitable alternate first order predictor of net primary production (NPP). We compiled a dataset of surface biooptical properties and phytoplankton NPP measurements in coastal waters around Australia to examine the utility of an in-situ absorption model to estimate NPP. The magnitude of surface NPP (0.20 to 19.3 mmol C m-3 d-1) across sites was largely driven by phytoplankton biomass, with higher rates being attributed to the microplankton (> 20 µm) size class. The phytoplankton absorption coefficient a PHY for PAR (photosynthetically active radiation; ā PHY)) ranged from 0.003 to 0.073 m-1 , influenced by changes in phytoplankton community composition, physiology and environmental conditions. The a PHY coefficient also reflected changes in NPP and the absorption model-derived NPP could explain 73 % of the variability in measured surface NPP (n = 41; RMSE = 2.49). The absorption model was applied to two contrasting coastal locations to examine NPP dynamics: a high chlorophyll-high variation (HCHV; Port Hacking National Reference Station) and moderate chlorophyll-low variation (MCLV; Yongala National Reference Station) location in eastern Australia using the GIOP-DC satellite a PHY product. Mean daily NPP rates between 2003 and 2015 were higher at the HCHV site (1.71 ± 0.03 mmol C m-3 d-1) with the annual maximum NPP occurring during the austral winter. In contrast, the MCLV site annual NPP peak occurred during the austral wet season and had lower mean daily NPP (1.43 ± 0.03 mmol C m-3 d-1) across the time-series. An absorption-based model to estimate NPP is a promising approach for exploring the spatiotemporal dynamics in phytoplankton NPP around the Australian continental shelf.

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Section: Potential For Further Application Of An Absorption-based Satmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Phytoplankton absorption predicts patterns in primary productivity in Australian coastal shelf waters

Robinson

Cherukuru

Hardman-Mountford

et al. 2017

Estuarine, Coastal and Shelf Science

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“…Satelit secara rutin telah menyediakan beberapa variabel biofisik seperti variabel konsentrasi klorofil-a dan suhu permukaan laut. Data yang telah didapat oleh sensor satelit, dapat digunakan untuk membuat model estimasi produktivitas primer, sehingga estimasi produktivitas primer lebih cepat dan efisien (Ma et al 2014). Salah satu kendala membuat model produktivitas primer menggunakan penginderaan jauh adalah bahwa sensor satelit hanya dapat mendeteksi konsentrasi klorofil-a permukaan dan bukan produktivitas primer (Susilo 1999).…”

Section: Pendahuluanunclassified

Hubungan Antara Konsentrasi Klorofil-a Dengan Tingkat Produktivitas Primer Menggunakan Citra Satelit Landsat-8

Nuzapril¹,

Susilo

Panjaitan

2017

JTPK

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Chlorophyll-a is a phytoplankton pigment involved in photosynthesis. Chlorophyll-a concentration detection through satellite orbiting can only be infered the concentration of chlorophyll-a at sea surface and could not estimate the sea primary productivity. Sea Primary productivity may last up to a depth of compensation or the depth at which the intensity of light stayed at least 1% of sea surface light intensity. However, the aim of this study is to find out the relationships between the concentration of chlorophyll-a and primary productivity so that the concentration of chlorophyll-a could be used to predict primary productivity. The linear regression equation have been applied to construct model explaing relationship between the chlorophyll-a concentration and sea primary productivity. The equation explaing on chlorophyll-a concentrations with primary productivity is PP = 22.746 + 95.536Keu (R²) = 0.66 where PP is the sea primary productivity, Keu is the average of chlorophyll-a concentration throughout the water column. The results of these equations can be applied to satellite imagery so that it can assist in monitoring water quality conditions. Keyword: Chlorophyll-a concentration, light intensity, primary productivity, satellite imagery ABSTRAK Klorofil-a merupakan pigmen fitoplankton yang berperan dalam proses fotosintesis. Deteksi konsentrasi klorofil-a melalui satelit hanya dapat menduga konsentrasi klorofil-a permukaan dan bukan produktivitas primer. Produktivitas primer dapat berlangsung sampai kedalaman kompensasi atau kedalaman dimana intensitas cahaya tinggal 1% dari intensitas cahaya permukaan. Penelitian ini bertujuan untuk mencari hubungan antara konsentrasi klorofil-a dengan produktivitas primer sehingga konsentrasi klorofil-a dapat digunakan untuk menduga produktivitas primer. Analisis regresi linier dilakukan terhadap model hubungan antara konsentrasi klorofil-a dengan produktivitas primer. Persamaan hubugan antara konsentrasi klorofil dengan produktivitas primer adalah PP = 22.746 + 95.536Keu dengan (R²) = 0.66 dimana PP adalah produktivitas primer dan Keu adalah konsentrasi klorofil-a rata-rata di seluruh kolom perairan. Hasil persamaan tersebut dapat diaplikasikan untuk citra satelit sehingga dapat membantu dalam memonitoring kondisi kualitas perairan.Kata kunci: Konsentrasi klorofil-a, intensitas cahaya, produktivitas primer, citra satelit

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“…A different basis is the third approach, based on phytoplankton absorption coefficient (a phy , PP sat−aphy−based ) (Marra et al, 1993;Lee et al, 1996;Hirawake et al, 2011;Ma et al, 2014). Instead of biological information, this approach uses optical information.…”

Section: Introductionmentioning

confidence: 99%

Regionalization and Dynamic Parameterization of Quantum Yield of Photosynthesis to Improve the Ocean Primary Production Estimates From Remote Sensing

2018

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Estimation of Marine Primary Productivity From Satellite-Derived Phytoplankton Absorption Data

Cited by 30 publications

References 48 publications

Phytoplankton absorption predicts patterns in primary productivity in Australian coastal shelf waters

Phytoplankton absorption predicts patterns in primary productivity in Australian coastal shelf waters

Hubungan Antara Konsentrasi Klorofil-a Dengan Tingkat Produktivitas Primer Menggunakan Citra Satelit Landsat-8

Regionalization and Dynamic Parameterization of Quantum Yield of Photosynthesis to Improve the Ocean Primary Production Estimates From Remote Sensing

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