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ORIGINAL RESEARCH article

Front. Mar. Sci.
Sec. Ocean Observation
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1476425
This article is part of the Research Topic Remote Sensing Applications in Oceanography with Deep Learning View all articles

Development of VIIRS-OLCI chlorophyll-a product for the coastal estuaries

Provisionally accepted
Alexander Gilerson Alexander Gilerson 1*Mateusz Malinowski Mateusz Malinowski 1Jacopo Agagliate Jacopo Agagliate 1Eder Herrera Estrella Eder Herrera Estrella 1Maria Tzortziou Maria Tzortziou 1Michelle C. Tomlinson Michelle C. Tomlinson 2Andrew Meredith Andrew Meredith 2,3Richard P. Stumpf Richard P. Stumpf 2Michael Ondrusek Michael Ondrusek 4Lide Jiang Lide Jiang 5Menghua Wang Menghua Wang 4
  • 1 City College of New York (CUNY), New York City, United States
  • 2 National Centers for Coastal Ocean Science (NOAA), Silver Spring, Maryland, United States
  • 3 Consolidated Safety Services (United States), Fairfax, Virginia, United States
  • 4 National Environmental Satellite Data and Information Service (NOAA), Silver Spring, Maryland, United States
  • 5 Cooperative Institute for Research in the Atmosphere (CIRA), Fort Collins, Colorado, United States

The final, formatted version of the article will be published soon.

    Coastal waters require monitoring of chlorophyll-a concentration (Chl-a) in a wide range of Chl-a from a few mg/m 3 to hundreds of mg/m 3 , which is of interest to the fisheries industry, evaluation of climate change effects, ecological modeling and detection of Harmful Algal Blooms (HABs). Monitoring can be carried out from the Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Instrument (OLCI) Ocean Color (OC) satellite sensors, which are currently on orbit and are expected to be the main operational OC sensors at least for the next decade. A Neural Network (NN) algorithm, which uses VIIRS M3-M5 reflectance bands and an I1 imaging band, was developed to estimate Chl-a in the Chesapeake Bay, for the whole range of Chl-a from clear waters in the Lower Bay to extreme bloom conditions in the Upper Bay and the Potomac River, where Chl-a can be used for bloom detection. The NN algorithm demonstrated a significant improvement in the Chl-a retrieval capabilities in comparison with other algorithms, which utilize only reflectance bands. OLCI NIR/red 709/665 nm bands red edge 2010 algorithm denoted as RE10 was also explored with several atmospheric corrections from EUMETSAT, NOAA and NASA. Good consistency between the two types of algorithms is shown for the bloom conditions and the whole range of waters in the Chesapeake Bay (with RE10 switch to OC4 for lower Chl-a) and these algorithms are recommended for the combined VIIRS-OLCI product for the estimation of Chl-a and bloom monitoring. The algorithms were expanded to the waters in Long Island Sound, demonstrating good performance.

    Keywords: chlorophyll-a concentration, Coastal waters, Neural Network, VIIRS, OLCI

    Received: 05 Aug 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Gilerson, Malinowski, Agagliate, Herrera Estrella, Tzortziou, Tomlinson, Meredith, Stumpf, Ondrusek, Jiang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Alexander Gilerson, City College of New York (CUNY), New York City, United States

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