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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doaj-73b1fce3edbf436dac57b7ab32dec3e62020-11-25T03:09:59ZengMDPI AGRemote Sensing2072-42922020-06-01121898189810.3390/rs12111898Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine LearningJinku Park0Hyun-Cheol Kim1Dukwon Bae2Young-Heon Jo3Department of Oceanography, Pusan National University, Geumjeong-Gu, Busan 46241, KoreaKorea Polar Research Institute, Incheon 21990, KoreaDepartment of Oceanography, Pusan National University, Geumjeong-Gu, Busan 46241, KoreaDepartment of Oceanography, Pusan National University, Geumjeong-Gu, Busan 46241, KoreaPolar 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.https://www.mdpi.com/2072-4292/12/11/1898data reconstructionchlorophyll-a concentration (CHL)random forest (RF)Ross SeaAntarctica |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jinku Park Hyun-Cheol Kim Dukwon Bae Young-Heon Jo |
spellingShingle |
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 Remote Sensing data reconstruction chlorophyll-a concentration (CHL) random forest (RF) Ross Sea Antarctica |
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 |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-06-01 |
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. |
topic |
data reconstruction chlorophyll-a concentration (CHL) random forest (RF) Ross Sea Antarctica |
url |
https://www.mdpi.com/2072-4292/12/11/1898 |
work_keys_str_mv |
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