Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI
The monitoring of Chlorophyll-a (Chl-a) concentration in high northern latitude waters has been receiving increased focus due to the rapid environmental changes in the sub-Arctic, Arctic. Spaceborne optical instruments allow the continuous monitoring of the occurrence, distribution, and amount of Ch...
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Molecular Diversity Preservation International (MDPI)
2019
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Online Access: | https://hdl.handle.net/10037/17025 https://doi.org/10.3390/rs11182076 |
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ftunivtroemsoe:oai:munin.uit.no:10037/17025 2023-05-15T14:51:16+02:00 Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI Blix, Katalin Li, Juan Massicotte, Philippe Matsuoka, Atsushi 2019-09-04 https://hdl.handle.net/10037/17025 https://doi.org/10.3390/rs11182076 eng eng Molecular Diversity Preservation International (MDPI) Remote Sensing Blix, K, Li J, Massicotte, P.; Matsuoka, A. (2019) Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI. Remote Sensing, 11 ,(18), 2076. FRIDAID 1722515 doi:10.3390/rs11182076 2072-4292 https://hdl.handle.net/10037/17025 openAccess Copyright 2019 The Author(s) VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2019 ftunivtroemsoe https://doi.org/10.3390/rs11182076 2021-06-25T17:57:04Z The monitoring of Chlorophyll-a (Chl-a) concentration in high northern latitude waters has been receiving increased focus due to the rapid environmental changes in the sub-Arctic, Arctic. Spaceborne optical instruments allow the continuous monitoring of the occurrence, distribution, and amount of Chl-a. In recent years, the Ocean and Land Color Instruments (OLCI) onboard the Sentinel 3 (S3) A and B satellites were launched, which provide data about various aquatic environments on advantageous spatial, spectral, and temporal resolutions with high SNR. Although S3 OLCI could be favorable to monitor high northern latitude waters, there have been several challenges related to Chl-a concentration retrieval in these waters due to their unique optical properties coupled with challenging environments including high sun zenith angle, presence of sea ice, and frequent cloud covers. In this work, we aim to overcome these difficulties by developing a machine-learning (ML) approach designed to estimate Chl-a concentration from S3 OLCI data in high northern latitude optically complex waters. The ML model is optimized and requires only three S3 OLCI bands, reflecting the physical characteristic of Chl-a as input in the regression process to estimate Chl-a concentration with improved accuracy in terms of the bias (five times improvements.) The ML model was optimized on data from Arctic, coastal, and open waters, and showed promising performance. Finally, we present the performance of the optimized ML approach by computing Chl-a maps and corresponding certainty maps in highly complex sub-Arctic and Arctic waters. We show how these certainty maps can be used as a support to understand possible radiometric calibration issues in the retrieval of Level 2 reflectance over these waters. This can be a useful tool in identifying erroneous Level 2 Remote sensing reflectance due to possible failure of the atmospheric correction algorithm. Article in Journal/Newspaper Arctic Sea ice University of Tromsø: Munin Open Research Archive Arctic The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) Remote Sensing 11 18 2076 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 |
spellingShingle |
VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 Blix, Katalin Li, Juan Massicotte, Philippe Matsuoka, Atsushi Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI |
topic_facet |
VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 |
description |
The monitoring of Chlorophyll-a (Chl-a) concentration in high northern latitude waters has been receiving increased focus due to the rapid environmental changes in the sub-Arctic, Arctic. Spaceborne optical instruments allow the continuous monitoring of the occurrence, distribution, and amount of Chl-a. In recent years, the Ocean and Land Color Instruments (OLCI) onboard the Sentinel 3 (S3) A and B satellites were launched, which provide data about various aquatic environments on advantageous spatial, spectral, and temporal resolutions with high SNR. Although S3 OLCI could be favorable to monitor high northern latitude waters, there have been several challenges related to Chl-a concentration retrieval in these waters due to their unique optical properties coupled with challenging environments including high sun zenith angle, presence of sea ice, and frequent cloud covers. In this work, we aim to overcome these difficulties by developing a machine-learning (ML) approach designed to estimate Chl-a concentration from S3 OLCI data in high northern latitude optically complex waters. The ML model is optimized and requires only three S3 OLCI bands, reflecting the physical characteristic of Chl-a as input in the regression process to estimate Chl-a concentration with improved accuracy in terms of the bias (five times improvements.) The ML model was optimized on data from Arctic, coastal, and open waters, and showed promising performance. Finally, we present the performance of the optimized ML approach by computing Chl-a maps and corresponding certainty maps in highly complex sub-Arctic and Arctic waters. We show how these certainty maps can be used as a support to understand possible radiometric calibration issues in the retrieval of Level 2 reflectance over these waters. This can be a useful tool in identifying erroneous Level 2 Remote sensing reflectance due to possible failure of the atmospheric correction algorithm. |
format |
Article in Journal/Newspaper |
author |
Blix, Katalin Li, Juan Massicotte, Philippe Matsuoka, Atsushi |
author_facet |
Blix, Katalin Li, Juan Massicotte, Philippe Matsuoka, Atsushi |
author_sort |
Blix, Katalin |
title |
Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI |
title_short |
Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI |
title_full |
Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI |
title_fullStr |
Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI |
title_full_unstemmed |
Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI |
title_sort |
developing a new machine-learning algorithm for estimating chlorophyll-a concentration in optically complex waters: a case study for high northern latitude waters by using sentinel 3 olci |
publisher |
Molecular Diversity Preservation International (MDPI) |
publishDate |
2019 |
url |
https://hdl.handle.net/10037/17025 https://doi.org/10.3390/rs11182076 |
long_lat |
ENVELOPE(73.317,73.317,-52.983,-52.983) |
geographic |
Arctic The Sentinel |
geographic_facet |
Arctic The Sentinel |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
Remote Sensing Blix, K, Li J, Massicotte, P.; Matsuoka, A. (2019) Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI. Remote Sensing, 11 ,(18), 2076. FRIDAID 1722515 doi:10.3390/rs11182076 2072-4292 https://hdl.handle.net/10037/17025 |
op_rights |
openAccess Copyright 2019 The Author(s) |
op_doi |
https://doi.org/10.3390/rs11182076 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
18 |
container_start_page |
2076 |
_version_ |
1766322318701756416 |