A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data

The ability to infer ocean chlorophyll-a concentrations (Chla) from spaceborne instruments is key to assessments of global ocean productivity and monitoring of water quality. Here, we present a novel parametric algorithm, OCG, trained on a set of global in situ high-performance liquid chromatography...

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Published in:ISPRS Journal of Photogrammetry and Remote Sensing
Main Authors: Merder, Julian, Zhao, Gang, Pahlevan, Nima, Rigby, Robert A., Stasinopoulos, Dimitrios M., Michalak, Anna M.
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 2024
Subjects:
Online Access:http://gala.gre.ac.uk/id/eprint/47156/
http://gala.gre.ac.uk/id/eprint/47156/9/47156%20RIGBY_A_Novel_Algorithm_For_Oceon_Chlorophyll-a_Concentration_%28OA%29_2024.pdf
https://doi.org/10.1016/j.isprsjprs.2024.03.014
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spelling ftunivgreenwich:oai:gala.gre.ac.uk:47156 2024-06-09T07:49:45+00:00 A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data Merder, Julian Zhao, Gang Pahlevan, Nima Rigby, Robert A. Stasinopoulos, Dimitrios M. Michalak, Anna M. 2024-03-25 application/pdf http://gala.gre.ac.uk/id/eprint/47156/ http://gala.gre.ac.uk/id/eprint/47156/9/47156%20RIGBY_A_Novel_Algorithm_For_Oceon_Chlorophyll-a_Concentration_%28OA%29_2024.pdf https://doi.org/10.1016/j.isprsjprs.2024.03.014 en eng Elsevier http://gala.gre.ac.uk/id/eprint/47156/9/47156%20RIGBY_A_Novel_Algorithm_For_Oceon_Chlorophyll-a_Concentration_%28OA%29_2024.pdf Merder, Julian orcid:0000-0002-5958-7016 , Zhao, Gang, Pahlevan, Nima, Rigby, Robert A. orcid:0000-0003-4787-623X , Stasinopoulos, Dimitrios M. and Michalak, Anna M. orcid:0000-0002-6152-7979 (2024) A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data. ISPRS Journal of Photogrammetry and Remote Sensing, 210. pp. 198-211. ISSN 0924-2716 (Print), 1872-8235 (Online) (doi:https://doi.org/10.1016/j.isprsjprs.2024.03.014 <https://doi.org/10.1016/j.isprsjprs.2024.03.014>) cc_by_nc_nd_4 QA75 Electronic computers. Computer science QD Chemistry Article PeerReviewed 2024 ftunivgreenwich https://doi.org/10.1016/j.isprsjprs.2024.03.014 2024-05-14T23:34:38Z The ability to infer ocean chlorophyll-a concentrations (Chla) from spaceborne instruments is key to assessments of global ocean productivity and monitoring of water quality. Here, we present a novel parametric algorithm, OCG, trained on a set of global in situ high-performance liquid chromatography (HPLC) data that leverages Level- 3 remote sensing reflectance (Rrs) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The OCG algorithm leverages more bands than existing algorithms and also provides pixel-wise uncertainty assessments that enable the calculation of the probability of exceeding specific Chla thresholds. This feature has significant implications for water quality management, particularly in monitoring harmful algal blooms. The OCG surpasses existing algorithms in bias and accuracy without overfitting, especially in coastal areas, where it outperforms the current standard product (CI OC3) by 20 % in median symmetric accuracy. Moreover, the OCG reduces the signed symmetric percentage bias (SSPB) in coastal regions from 41 % (CI OC3) to below 5 %. Globally, the OCG algorithm yields lower Chla in coastal regions, the Southern Ocean and the Mediterranean Sea, and higher values in the open ocean, particularly in ocean gyres and polar regions. For the Chesapeake Bay and the Baltic Sea, for example, daily OCG estimates for 2002 to 2021 are, on average, 2.9 g/ L and 3.7 g/L lower than CI OC3 estimates, respectively. The presented approach also shows great potential for other existing and upcoming sensors, enabling widespread application in remote sensing. Article in Journal/Newspaper Southern Ocean University of Greenwich: Greenwich Academic Literature Archive Southern Ocean ISPRS Journal of Photogrammetry and Remote Sensing 210 198 211
institution Open Polar
collection University of Greenwich: Greenwich Academic Literature Archive
op_collection_id ftunivgreenwich
language English
topic QA75 Electronic computers. Computer science
QD Chemistry
spellingShingle QA75 Electronic computers. Computer science
QD Chemistry
Merder, Julian
Zhao, Gang
Pahlevan, Nima
Rigby, Robert A.
Stasinopoulos, Dimitrios M.
Michalak, Anna M.
A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data
topic_facet QA75 Electronic computers. Computer science
QD Chemistry
description The ability to infer ocean chlorophyll-a concentrations (Chla) from spaceborne instruments is key to assessments of global ocean productivity and monitoring of water quality. Here, we present a novel parametric algorithm, OCG, trained on a set of global in situ high-performance liquid chromatography (HPLC) data that leverages Level- 3 remote sensing reflectance (Rrs) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The OCG algorithm leverages more bands than existing algorithms and also provides pixel-wise uncertainty assessments that enable the calculation of the probability of exceeding specific Chla thresholds. This feature has significant implications for water quality management, particularly in monitoring harmful algal blooms. The OCG surpasses existing algorithms in bias and accuracy without overfitting, especially in coastal areas, where it outperforms the current standard product (CI OC3) by 20 % in median symmetric accuracy. Moreover, the OCG reduces the signed symmetric percentage bias (SSPB) in coastal regions from 41 % (CI OC3) to below 5 %. Globally, the OCG algorithm yields lower Chla in coastal regions, the Southern Ocean and the Mediterranean Sea, and higher values in the open ocean, particularly in ocean gyres and polar regions. For the Chesapeake Bay and the Baltic Sea, for example, daily OCG estimates for 2002 to 2021 are, on average, 2.9 g/ L and 3.7 g/L lower than CI OC3 estimates, respectively. The presented approach also shows great potential for other existing and upcoming sensors, enabling widespread application in remote sensing.
format Article in Journal/Newspaper
author Merder, Julian
Zhao, Gang
Pahlevan, Nima
Rigby, Robert A.
Stasinopoulos, Dimitrios M.
Michalak, Anna M.
author_facet Merder, Julian
Zhao, Gang
Pahlevan, Nima
Rigby, Robert A.
Stasinopoulos, Dimitrios M.
Michalak, Anna M.
author_sort Merder, Julian
title A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data
title_short A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data
title_full A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data
title_fullStr A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data
title_full_unstemmed A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data
title_sort novel algorithm for ocean chlorophyll-a concentration using modis aqua data
publisher Elsevier
publishDate 2024
url http://gala.gre.ac.uk/id/eprint/47156/
http://gala.gre.ac.uk/id/eprint/47156/9/47156%20RIGBY_A_Novel_Algorithm_For_Oceon_Chlorophyll-a_Concentration_%28OA%29_2024.pdf
https://doi.org/10.1016/j.isprsjprs.2024.03.014
geographic Southern Ocean
geographic_facet Southern Ocean
genre Southern Ocean
genre_facet Southern Ocean
op_relation http://gala.gre.ac.uk/id/eprint/47156/9/47156%20RIGBY_A_Novel_Algorithm_For_Oceon_Chlorophyll-a_Concentration_%28OA%29_2024.pdf
Merder, Julian orcid:0000-0002-5958-7016 , Zhao, Gang, Pahlevan, Nima, Rigby, Robert A. orcid:0000-0003-4787-623X , Stasinopoulos, Dimitrios M. and Michalak, Anna M. orcid:0000-0002-6152-7979 (2024) A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data. ISPRS Journal of Photogrammetry and Remote Sensing, 210. pp. 198-211. ISSN 0924-2716 (Print), 1872-8235 (Online) (doi:https://doi.org/10.1016/j.isprsjprs.2024.03.014 <https://doi.org/10.1016/j.isprsjprs.2024.03.014>)
op_rights cc_by_nc_nd_4
op_doi https://doi.org/10.1016/j.isprsjprs.2024.03.014
container_title ISPRS Journal of Photogrammetry and Remote Sensing
container_volume 210
container_start_page 198
op_container_end_page 211
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