Remote Sensing of Phytoplankton Size Class in Northwest Atlantic from 1998 to 2016: Bio-Optical Algorithms Comparison and Application
Phytoplankton community structure and phytoplankton size class (PSC) are linked to ecological and biogeochemical changes in the oceanic environment. Many models developed to obtain the fraction of PSCs from satellite remote sensing have only been evaluated in open oceans, and very limited effort has...
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ftmdpi:oai:mdpi.com:/2072-4292/10/7/1028/ 2023-08-20T04:08:48+02:00 Remote Sensing of Phytoplankton Size Class in Northwest Atlantic from 1998 to 2016: Bio-Optical Algorithms Comparison and Application Xiaohan Liu Emmanuel Devred Catherine Johnson agris 2018-06-28 application/pdf https://doi.org/10.3390/rs10071028 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs10071028 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 10; Issue 7; Pages: 1028 phytoplankton size ocean color remote sensing Northwest Atlantic Text 2018 ftmdpi https://doi.org/10.3390/rs10071028 2023-07-31T21:36:00Z Phytoplankton community structure and phytoplankton size class (PSC) are linked to ecological and biogeochemical changes in the oceanic environment. Many models developed to obtain the fraction of PSCs from satellite remote sensing have only been evaluated in open oceans, and very limited effort has been carried out to report on the performance of these PSC models in productive continental shelf waters. In this study, we evaluated the performance of nine PSC models in the coastal Northwest Atlantic (NWA) by comparison of in situ phytoplankton pigment measurements with coincidental satellite data from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate-resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). Our results show that no PSC model retrieved all three phytoplankton size classes (pico-, nano-, and micro-phytoplankton) with reliable accuracy in the region of interest. In particular, these PSC models showed poor performance for retrieval of the picophytoplankton fraction of total phytoplankton in our study region, which could be related to the under-representation of pico-dominated samples in the productive waters of the NWA. For the accuracy of retrieved microphytoplankton and combined nano–pico phytoplankton fraction, the regional model developed by Devred et al. (2011) yielded the best result, followed by the model of Brewin et al. (2011). The model of Devred et al. (2011) was applied to satellite-derived chlorophyll-a concentration from the Ocean Color Climate Change Initiative (OC-CCI) archive in the NWA from 1998 to 2016. We report solely on the microphytoplankton biomass and fraction given the inverse relationship that exists with the nano–pico class. The multi-decadal trend along with the deseasonalized trend of microphytoplankton fraction was computed and analyzed for six biogeochemical provinces located in the NWA. Over the 19-year time series, there were significant, positive trends for four of the six provinces, with a slope of ... Text Northwest Atlantic MDPI Open Access Publishing Remote Sensing 10 7 1028 |
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Open Polar |
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MDPI Open Access Publishing |
op_collection_id |
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language |
English |
topic |
phytoplankton size ocean color remote sensing Northwest Atlantic |
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phytoplankton size ocean color remote sensing Northwest Atlantic Xiaohan Liu Emmanuel Devred Catherine Johnson Remote Sensing of Phytoplankton Size Class in Northwest Atlantic from 1998 to 2016: Bio-Optical Algorithms Comparison and Application |
topic_facet |
phytoplankton size ocean color remote sensing Northwest Atlantic |
description |
Phytoplankton community structure and phytoplankton size class (PSC) are linked to ecological and biogeochemical changes in the oceanic environment. Many models developed to obtain the fraction of PSCs from satellite remote sensing have only been evaluated in open oceans, and very limited effort has been carried out to report on the performance of these PSC models in productive continental shelf waters. In this study, we evaluated the performance of nine PSC models in the coastal Northwest Atlantic (NWA) by comparison of in situ phytoplankton pigment measurements with coincidental satellite data from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate-resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). Our results show that no PSC model retrieved all three phytoplankton size classes (pico-, nano-, and micro-phytoplankton) with reliable accuracy in the region of interest. In particular, these PSC models showed poor performance for retrieval of the picophytoplankton fraction of total phytoplankton in our study region, which could be related to the under-representation of pico-dominated samples in the productive waters of the NWA. For the accuracy of retrieved microphytoplankton and combined nano–pico phytoplankton fraction, the regional model developed by Devred et al. (2011) yielded the best result, followed by the model of Brewin et al. (2011). The model of Devred et al. (2011) was applied to satellite-derived chlorophyll-a concentration from the Ocean Color Climate Change Initiative (OC-CCI) archive in the NWA from 1998 to 2016. We report solely on the microphytoplankton biomass and fraction given the inverse relationship that exists with the nano–pico class. The multi-decadal trend along with the deseasonalized trend of microphytoplankton fraction was computed and analyzed for six biogeochemical provinces located in the NWA. Over the 19-year time series, there were significant, positive trends for four of the six provinces, with a slope of ... |
format |
Text |
author |
Xiaohan Liu Emmanuel Devred Catherine Johnson |
author_facet |
Xiaohan Liu Emmanuel Devred Catherine Johnson |
author_sort |
Xiaohan Liu |
title |
Remote Sensing of Phytoplankton Size Class in Northwest Atlantic from 1998 to 2016: Bio-Optical Algorithms Comparison and Application |
title_short |
Remote Sensing of Phytoplankton Size Class in Northwest Atlantic from 1998 to 2016: Bio-Optical Algorithms Comparison and Application |
title_full |
Remote Sensing of Phytoplankton Size Class in Northwest Atlantic from 1998 to 2016: Bio-Optical Algorithms Comparison and Application |
title_fullStr |
Remote Sensing of Phytoplankton Size Class in Northwest Atlantic from 1998 to 2016: Bio-Optical Algorithms Comparison and Application |
title_full_unstemmed |
Remote Sensing of Phytoplankton Size Class in Northwest Atlantic from 1998 to 2016: Bio-Optical Algorithms Comparison and Application |
title_sort |
remote sensing of phytoplankton size class in northwest atlantic from 1998 to 2016: bio-optical algorithms comparison and application |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018 |
url |
https://doi.org/10.3390/rs10071028 |
op_coverage |
agris |
genre |
Northwest Atlantic |
genre_facet |
Northwest Atlantic |
op_source |
Remote Sensing; Volume 10; Issue 7; Pages: 1028 |
op_relation |
Ocean Remote Sensing https://dx.doi.org/10.3390/rs10071028 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs10071028 |
container_title |
Remote Sensing |
container_volume |
10 |
container_issue |
7 |
container_start_page |
1028 |
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1774721296676945920 |