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...
Published in: | ISPRS Journal of Photogrammetry and Remote Sensing |
---|---|
Main Authors: | , , , , , |
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 |
id |
ftunivgreenwich:oai:gala.gre.ac.uk:47156 |
---|---|
record_format |
openpolar |
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 |
_version_ |
1801382545894408192 |