Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance
Ocean color remote sensing significantly contributes to our understanding of phytoplankton distribution and abundance and primary productivity in the Southern Ocean (SO). However, the current SO in situ optical database is still insufficient and unevenly distributed. This limits the ability to produ...
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ftpubmed:oai:pubmedcentral.nih.gov:5191056 2023-05-15T13:31:05+02:00 Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance Zeng, Chen Xu, Huiping Fischer, Andrew M. 2016-12-07 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191056/ http://www.ncbi.nlm.nih.gov/pubmed/27941596 https://doi.org/10.3390/s16122075 en eng MDPI http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191056/ http://www.ncbi.nlm.nih.gov/pubmed/27941596 http://dx.doi.org/10.3390/s16122075 © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). CC-BY Article Text 2016 ftpubmed https://doi.org/10.3390/s16122075 2017-01-08T01:29:04Z Ocean color remote sensing significantly contributes to our understanding of phytoplankton distribution and abundance and primary productivity in the Southern Ocean (SO). However, the current SO in situ optical database is still insufficient and unevenly distributed. This limits the ability to produce robust and accurate measurements of satellite-based chlorophyll. Based on data collected on cruises around the Antarctica Peninsula (AP) on January 2014 and 2016, this research intends to enhance our knowledge of SO water and atmospheric optical characteristics and address satellite algorithm deficiency of ocean color products. We collected high resolution in situ water leaving reflectance (±1 nm band resolution), simultaneous in situ chlorophyll-a concentrations and satellite (MODIS and VIIRS) water leaving reflectance. Field samples show that clouds have a great impact on the visible green bands and are difficult to detect because NASA protocols apply the NIR band as a cloud contamination threshold. When compared to global case I water, water around the AP has lower water leaving reflectance and a narrower blue-green band ratio, which explains chlorophyll-a underestimation in high chlorophyll-a regions and overestimation in low chlorophyll-a regions. VIIRS shows higher spatial coverage and detection accuracy than MODIS. After coefficient improvement, VIIRS is able to predict chlorophyll a with 53% accuracy. Text Antarc* Antarctica Southern Ocean PubMed Central (PMC) Southern Ocean Sensors 16 12 2075 |
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Article Zeng, Chen Xu, Huiping Fischer, Andrew M. Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance |
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Article |
description |
Ocean color remote sensing significantly contributes to our understanding of phytoplankton distribution and abundance and primary productivity in the Southern Ocean (SO). However, the current SO in situ optical database is still insufficient and unevenly distributed. This limits the ability to produce robust and accurate measurements of satellite-based chlorophyll. Based on data collected on cruises around the Antarctica Peninsula (AP) on January 2014 and 2016, this research intends to enhance our knowledge of SO water and atmospheric optical characteristics and address satellite algorithm deficiency of ocean color products. We collected high resolution in situ water leaving reflectance (±1 nm band resolution), simultaneous in situ chlorophyll-a concentrations and satellite (MODIS and VIIRS) water leaving reflectance. Field samples show that clouds have a great impact on the visible green bands and are difficult to detect because NASA protocols apply the NIR band as a cloud contamination threshold. When compared to global case I water, water around the AP has lower water leaving reflectance and a narrower blue-green band ratio, which explains chlorophyll-a underestimation in high chlorophyll-a regions and overestimation in low chlorophyll-a regions. VIIRS shows higher spatial coverage and detection accuracy than MODIS. After coefficient improvement, VIIRS is able to predict chlorophyll a with 53% accuracy. |
format |
Text |
author |
Zeng, Chen Xu, Huiping Fischer, Andrew M. |
author_facet |
Zeng, Chen Xu, Huiping Fischer, Andrew M. |
author_sort |
Zeng, Chen |
title |
Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance |
title_short |
Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance |
title_full |
Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance |
title_fullStr |
Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance |
title_full_unstemmed |
Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance |
title_sort |
chlorophyll-a estimation around the antarctica peninsula using satellite algorithms: hints from field water leaving reflectance |
publisher |
MDPI |
publishDate |
2016 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191056/ http://www.ncbi.nlm.nih.gov/pubmed/27941596 https://doi.org/10.3390/s16122075 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Antarc* Antarctica Southern Ocean |
genre_facet |
Antarc* Antarctica Southern Ocean |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191056/ http://www.ncbi.nlm.nih.gov/pubmed/27941596 http://dx.doi.org/10.3390/s16122075 |
op_rights |
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
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CC-BY |
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https://doi.org/10.3390/s16122075 |
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Sensors |
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2075 |
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1766015803451244544 |