Long-term trend and environmental determinants of phytoplankton biomass in coastal waters of northwestern Bay of Bengal

Miranda, Joereen; Baliarsingh, Sanjiba Kumar; Lotliker, Aneesh A.; Sahoo, S. N.; Sahu, K. C.; Kumar, T. Srinivasa

doi:10.1007/s10661-019-8033-8

Cited by 12 publications

(1 citation statement)

References 41 publications

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“…Phytoplankton play a key role in the marine biogeochemical cycle and global carbon dynamics [1] and largely control the productivity of coastal ecosystems. The chlorophyll a (Chl-a) concentration has been widely used as an index of phytoplankton abundance to reflect the productivity and trophic conditions of estuaries and coastal and oceanic waters [2,3].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Chlorophyll a Concentration Using GOCI Data in Sediment-Laden Turbid Waters of Hangzhou Bay and Adjacent Coastal Waters

Yang

et al. 2023

JMSE

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The Geostationary Ocean Color Imager (GOCI) provided images at hourly intervals up to 8 times per day with a spatial resolution of 500 m from 2011 to 2021. However, in the typical sediment-laden turbid water of Hangzhou Bay, valid ocean color parameters in operational data products have been extensively missing due to failures in atmospheric correction (AC) and bio-optical retrieval procedures. In this study, the seasonal variations in chlorophyll a (Chl-a) concentrations in Hangzhou Bay derived using GOCI data in 2020 were presented. First, valid remote sensing reflectance data were obtained by transferring neighboring aerosol properties of less to more turbid water pixels. Then, we improved a regionally empirical Chl-a retrieval algorithm in extremely turbid waters using GOCI-derived surface reflectance and field Chl-a measurements and proposed a combined Chl-a retrieval scheme for both moderately and extremely turbid water in Hangzhou Bay. Finally, the seasonal variation in Chl-a was obtained by the GOCI, which was better than operational products and in good agreement with the buoy data. The method in this study can be effectively applied to the inversion of Chl-a concentration in Hangzhou Bay and adjacent sea areas. We also presented its seasonal variations, offering insight into the spatial and seasonal variation of Chl-a in Hangzhou Bay using the GOCI.

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Section: Introductionmentioning

confidence: 99%

Retrieval of Chlorophyll a Concentration Using GOCI Data in Sediment-Laden Turbid Waters of Hangzhou Bay and Adjacent Coastal Waters

Yang

et al. 2023

JMSE

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Remote sensing statistical inference: basic theory and forward simulation of water–air statistical radiative transfer

Zhu

2021

Earth Sci Inform

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Satellite-Based Characterization of Phytoplankton Blooms in Coastal Waters of the Northwestern Bay of Bengal

Srichandan

Baliarsingh

Samanta

et al. 2022

J Indian Soc Remote Sens

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Long-term trend and environmental determinants of phytoplankton biomass in coastal waters of northwestern Bay of Bengal

Cited by 12 publications

References 41 publications

Retrieval of Chlorophyll a Concentration Using GOCI Data in Sediment-Laden Turbid Waters of Hangzhou Bay and Adjacent Coastal Waters

Retrieval of Chlorophyll a Concentration Using GOCI Data in Sediment-Laden Turbid Waters of Hangzhou Bay and Adjacent Coastal Waters

Remote sensing statistical inference: basic theory and forward simulation of water–air statistical radiative transfer

Satellite-Based Characterization of Phytoplankton Blooms in Coastal Waters of the Northwestern Bay of Bengal

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