Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors
A generalized coccolithophore bloom classifier has been developed for use with ocean color imagery. The bloom classifier was developed using extracted satellite reflectance data from SeaWiFS images screened by default bloom detection mask. In the current application, we extend the optical water type...
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Online Access: | https://publications.jrc.ec.europa.eu/repository/handle/JRC67442 http://www.sciencedirect.com/science/article/pii/S003442571100349X https://doi.org/10.1016/j.rse.2011.10.001 |
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ftjrc:oai:publications.jrc.ec.europa.eu:JRC67442 2023-05-15T17:34:22+02:00 Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors DOWELL Mark MOORE Timothy S. FRANZ Bryan 2012 Online https://publications.jrc.ec.europa.eu/repository/handle/JRC67442 http://www.sciencedirect.com/science/article/pii/S003442571100349X https://doi.org/10.1016/j.rse.2011.10.001 ENG eng ELSEVIER SCIENCE INC JRC67442 2012 ftjrc https://doi.org/10.1016/j.rse.2011.10.001 2022-05-01T08:17:35Z A generalized coccolithophore bloom classifier has been developed for use with ocean color imagery. The bloom classifier was developed using extracted satellite reflectance data from SeaWiFS images screened by default bloom detection mask. In the current application, we extend the optical water type (OWT) classification scheme by adding a new coccolithophore bloom class formed from these extracted reflectances. Based on an in situ coccolithophore data set from the North Atlantic, the detection levels with the new scheme were between 1,500 and 1,800 coccolithophore cells/mL and 43,000 and 78,000 liths/mL. The detected bloom area using the OWT method was an average of 1.75 times greater than the default bloom detector based on a collection of SeaWiFS 1 km imagery. The versatility of the scheme is shown with SeaWiFS, MODIS Aqua, CZCS and MERIS imagery at the 1 km scale. The OWT scheme was applied to the daily global SeaWiFS imagery mission data set (years 1997–2010). Based on our results, average annual coccolithophore bloom area was more than two times greater in the southern hemisphere compared to the northern hemisphere with values of 2.00 × 106 km2 and 0.75 × 106 km2, respectively. The new algorithm detects larger bloom areas in the Southern Ocean compared to the default algorithm, and our revised global annual average of 2.75 × 106 km2 is dominated by contributions from the Southern Ocean. JRC.H.5 - Land Resources Management Other/Unknown Material North Atlantic Southern Ocean Joint Research Centre, European Commission: JRC Publications Repository Southern Ocean Remote Sensing of Environment 117 249 263 |
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Open Polar |
collection |
Joint Research Centre, European Commission: JRC Publications Repository |
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ftjrc |
language |
English |
description |
A generalized coccolithophore bloom classifier has been developed for use with ocean color imagery. The bloom classifier was developed using extracted satellite reflectance data from SeaWiFS images screened by default bloom detection mask. In the current application, we extend the optical water type (OWT) classification scheme by adding a new coccolithophore bloom class formed from these extracted reflectances. Based on an in situ coccolithophore data set from the North Atlantic, the detection levels with the new scheme were between 1,500 and 1,800 coccolithophore cells/mL and 43,000 and 78,000 liths/mL. The detected bloom area using the OWT method was an average of 1.75 times greater than the default bloom detector based on a collection of SeaWiFS 1 km imagery. The versatility of the scheme is shown with SeaWiFS, MODIS Aqua, CZCS and MERIS imagery at the 1 km scale. The OWT scheme was applied to the daily global SeaWiFS imagery mission data set (years 1997–2010). Based on our results, average annual coccolithophore bloom area was more than two times greater in the southern hemisphere compared to the northern hemisphere with values of 2.00 × 106 km2 and 0.75 × 106 km2, respectively. The new algorithm detects larger bloom areas in the Southern Ocean compared to the default algorithm, and our revised global annual average of 2.75 × 106 km2 is dominated by contributions from the Southern Ocean. JRC.H.5 - Land Resources Management |
author |
DOWELL Mark MOORE Timothy S. FRANZ Bryan |
spellingShingle |
DOWELL Mark MOORE Timothy S. FRANZ Bryan Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors |
author_facet |
DOWELL Mark MOORE Timothy S. FRANZ Bryan |
author_sort |
DOWELL Mark |
title |
Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors |
title_short |
Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors |
title_full |
Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors |
title_fullStr |
Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors |
title_full_unstemmed |
Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors |
title_sort |
detection of coccolithophore blooms in ocean color satellite imagery: a generalized approach for use with multiple sensors |
publisher |
ELSEVIER SCIENCE INC |
publishDate |
2012 |
url |
https://publications.jrc.ec.europa.eu/repository/handle/JRC67442 http://www.sciencedirect.com/science/article/pii/S003442571100349X https://doi.org/10.1016/j.rse.2011.10.001 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
North Atlantic Southern Ocean |
genre_facet |
North Atlantic Southern Ocean |
op_relation |
JRC67442 |
op_doi |
https://doi.org/10.1016/j.rse.2011.10.001 |
container_title |
Remote Sensing of Environment |
container_volume |
117 |
container_start_page |
249 |
op_container_end_page |
263 |
_version_ |
1766133188216750080 |