OC4-SO: A New Chlorophyll- a Algorithm for the Western Antarctic Peninsula Using Multi-Sensor Satellite Data
Chlorophyll- a (Chl- a ) underestimation by global satellite algorithms in the Southern Ocean has long been reported, reducing their accuracy, and limiting the potential for evaluating phytoplankton biomass. As a result, several regional Chl- a algorithms have been proposed. The present work aims at...
Published in: | Remote Sensing |
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Main Authors: | , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
MDPI AG
2022
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Subjects: | |
Online Access: | https://doi.org/10.3390/rs14051052 https://doaj.org/article/c0b0e8d2b3ac49f48cb03c5c4b028ddd |
Summary: | Chlorophyll- a (Chl- a ) underestimation by global satellite algorithms in the Southern Ocean has long been reported, reducing their accuracy, and limiting the potential for evaluating phytoplankton biomass. As a result, several regional Chl- a algorithms have been proposed. The present work aims at assessing the performance of both global and regional satellite algorithms that are currently available for the Western Antarctic Peninsula (WAP) and investigate which factors are contributing to the underestimation of Chl- a . Our study indicates that a global algorithm, on average, underestimates in-situ Chl- a by ~59%, although underestimation was only observed for waters with Chl- a > 0.5 mg m −3 . In high Chl- a waters (>1 mg m −3 ), Chl- a underestimation rose to nearly 80%. Contrary to previous studies, no clear link was found between Chl- a underestimation and the pigment packaging effect, nor with the phytoplankton community composition and sea ice contamination. Based on multi-sensor satellite data and the most comprehensive in-situ dataset ever collected from the WAP, a new, more accurate satellite Chl- a algorithm is proposed: the OC4-SO. The OC4-SO has great potential to become an important tool not only for the ocean colour community, but also for an effective monitoring of the phytoplankton communities in a climatically sensitive region where in-situ data are scarce. |
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