Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning
Polar regions are too harsh to be continuously observed using ocean color (OC) sensors because of various limitations due to low solar elevations, ice effects, peculiar phytoplankton photosynthetic parameters, optical complexity of seawater and persistence of clouds and fog. Therefore, the OC data u...
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ftdoajarticles:oai:doaj.org/article:73b1fce3edbf436dac57b7ab32dec3e6 2023-05-15T13:43:05+02:00 Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning Jinku Park Hyun-Cheol Kim Dukwon Bae Young-Heon Jo 2020-06-01T00:00:00Z https://doi.org/10.3390/rs12111898 https://doaj.org/article/73b1fce3edbf436dac57b7ab32dec3e6 EN eng MDPI AG https://www.mdpi.com/2072-4292/12/11/1898 https://doaj.org/toc/2072-4292 doi:10.3390/rs12111898 2072-4292 https://doaj.org/article/73b1fce3edbf436dac57b7ab32dec3e6 Remote Sensing, Vol 12, Iss 1898, p 1898 (2020) data reconstruction chlorophyll-a concentration (CHL) random forest (RF) Ross Sea Antarctica Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12111898 2022-12-31T16:12:03Z Polar regions are too harsh to be continuously observed using ocean color (OC) sensors because of various limitations due to low solar elevations, ice effects, peculiar phytoplankton photosynthetic parameters, optical complexity of seawater and persistence of clouds and fog. Therefore, the OC data undergo a quality-control process, eventually accompanied by considerable data loss. We attempted to reconstruct these missing values for chlorophyll-a concentration (CHL) data using a machine-learning technique based on multiple datasets (satellite and reanalysis datasets) in the Ross Sea, Antarctica. This technique—based on an ensemble tree called random forest (RF)—was used for the reconstruction. The performance of the RF model was robust, and the reconstructed CHL data were consistent with satellite measurements. The reconstructed CHL data allowed a high intrinsic resolution of OC to be used without specific techniques (e.g., spatial average). Therefore, we believe that it is possible to study multiple characteristics of phytoplankton dynamics more quantitatively, such as bloom initiation/termination timings and peaks, as well as the variability in time scales of phytoplankton growth. In addition, because the reconstructed CHL showed relatively higher accuracy than satellite observations compared with the in situ data, our product may enable more accurate planktonic research. Article in Journal/Newspaper Antarc* Antarctica Ross Sea Directory of Open Access Journals: DOAJ Articles Ross Sea Remote Sensing 12 11 1898 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
data reconstruction chlorophyll-a concentration (CHL) random forest (RF) Ross Sea Antarctica Science Q |
spellingShingle |
data reconstruction chlorophyll-a concentration (CHL) random forest (RF) Ross Sea Antarctica Science Q Jinku Park Hyun-Cheol Kim Dukwon Bae Young-Heon Jo Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning |
topic_facet |
data reconstruction chlorophyll-a concentration (CHL) random forest (RF) Ross Sea Antarctica Science Q |
description |
Polar regions are too harsh to be continuously observed using ocean color (OC) sensors because of various limitations due to low solar elevations, ice effects, peculiar phytoplankton photosynthetic parameters, optical complexity of seawater and persistence of clouds and fog. Therefore, the OC data undergo a quality-control process, eventually accompanied by considerable data loss. We attempted to reconstruct these missing values for chlorophyll-a concentration (CHL) data using a machine-learning technique based on multiple datasets (satellite and reanalysis datasets) in the Ross Sea, Antarctica. This technique—based on an ensemble tree called random forest (RF)—was used for the reconstruction. The performance of the RF model was robust, and the reconstructed CHL data were consistent with satellite measurements. The reconstructed CHL data allowed a high intrinsic resolution of OC to be used without specific techniques (e.g., spatial average). Therefore, we believe that it is possible to study multiple characteristics of phytoplankton dynamics more quantitatively, such as bloom initiation/termination timings and peaks, as well as the variability in time scales of phytoplankton growth. In addition, because the reconstructed CHL showed relatively higher accuracy than satellite observations compared with the in situ data, our product may enable more accurate planktonic research. |
format |
Article in Journal/Newspaper |
author |
Jinku Park Hyun-Cheol Kim Dukwon Bae Young-Heon Jo |
author_facet |
Jinku Park Hyun-Cheol Kim Dukwon Bae Young-Heon Jo |
author_sort |
Jinku Park |
title |
Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning |
title_short |
Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning |
title_full |
Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning |
title_fullStr |
Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning |
title_full_unstemmed |
Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning |
title_sort |
data reconstruction for remotely sensed chlorophyll-a concentration in the ross sea using ensemble-based machine learning |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12111898 https://doaj.org/article/73b1fce3edbf436dac57b7ab32dec3e6 |
geographic |
Ross Sea |
geographic_facet |
Ross Sea |
genre |
Antarc* Antarctica Ross Sea |
genre_facet |
Antarc* Antarctica Ross Sea |
op_source |
Remote Sensing, Vol 12, Iss 1898, p 1898 (2020) |
op_relation |
https://www.mdpi.com/2072-4292/12/11/1898 https://doaj.org/toc/2072-4292 doi:10.3390/rs12111898 2072-4292 https://doaj.org/article/73b1fce3edbf436dac57b7ab32dec3e6 |
op_doi |
https://doi.org/10.3390/rs12111898 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
11 |
container_start_page |
1898 |
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1766184231606681600 |