Li, J.; Matsuoka, A.; Pang, X.; Massicotte, P.; Babin, M. Performance of Algorithms for Retrieving Chlorophyll a Concentrations in the Arctic Ocean: Impact on Primary Production Estimates. Remote Sens.2024, 16, 892.
Li, J.; Matsuoka, A.; Pang, X.; Massicotte, P.; Babin, M. Performance of Algorithms for Retrieving Chlorophyll a Concentrations in the Arctic Ocean: Impact on Primary Production Estimates. Remote Sens. 2024, 16, 892.
Li, J.; Matsuoka, A.; Pang, X.; Massicotte, P.; Babin, M. Performance of Algorithms for Retrieving Chlorophyll a Concentrations in the Arctic Ocean: Impact on Primary Production Estimates. Remote Sens.2024, 16, 892.
Li, J.; Matsuoka, A.; Pang, X.; Massicotte, P.; Babin, M. Performance of Algorithms for Retrieving Chlorophyll a Concentrations in the Arctic Ocean: Impact on Primary Production Estimates. Remote Sens. 2024, 16, 892.
Abstract
Chlorophyll a concentration (Chl) is a key variable to estimate primary production (PP) through ocean color remote sensing (OCRS). Accurate Chl estimate is crucial for better understanding of spatio-temporal trends of PP over recent decades as a consequence of climate change. However, a number of studies have reported that currently operational chlorophyll a algorithms perform poorly in the Arctic Ocean (AO), which is mainly caused by the interference of colored and detrital material (CDM) with the phytoplankton signal in the visible part of the spectrum. To determine how and to what extent that CDM would bias the estimation of Chl, we evaluated the performances of 8 currently available ocean color algorithms: OC4v6, OC3Mv6, OC3V, OC4L, OC4P, AO.emp, GSM01 and AO.GSM. Our results suggest that the empirical AO.emp algorithm performs the best overall, but for waters with high CDM (acdm(443) > 0.067 m-1), which is of much interest in the Arctic, it is the two semi-analytical GSM models that show better performance. Besides, sensitivity analyses using an Arctic spectrally- and vertically-resolved primary production model further show that errors in Chl mostly propagate proportionally to PP estimates with 7% amplification at maximum. We aslo demonstrate that the higher level of CDM relative to Chl in the water column, the larger the bias would occur in both Chl and PP estimates. Although the AO.GSM overall best performs among algorithms tested in the present study, it tends to fail for a significant number of pixels (16.2% observed in the present study) particularly for waters with high CDM. Our results suggest that an algorithm that provides reasonable Chl estimates for a wide range of optically-complex Arctic waters is still required.
Keywords
Arctic Ocean; chlorophyll a algorithm; colored and detrital material; primary production
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
