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Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning

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dc.contributor.authorPark, Jinku-
dc.contributor.authorKim, Hyun-cheol-
dc.contributor.authorBae, Dukwon-
dc.contributor.authorJo, Young-Heon-
dc.date.accessioned2021-05-18T01:32:57Z-
dc.date.available2021-05-18T01:32:57Z-
dc.date.issued2020-06-
dc.identifier.urihttps://repository.kopri.re.kr/handle/201206/12059-
dc.description.abstractPolar 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 researchen_US
dc.languageEnglishen_US
dc.subjectEnvironmental Sciences & Ecologyen_US
dc.subjectGeologyen_US
dc.subjectRemote Sensingen_US
dc.subjectImaging Science & Photographic Technologyen_US
dc.subject.classification해당사항없음en_US
dc.titleData Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learningen_US
dc.title.alternative머신러닝을 이용한 로스해 해색원격탐사 엽록소 농도 복원 연구en_US
dc.typeArticleen_US
dc.identifier.bibliographicCitationPark, Jinku, et al. 2020. "Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning". <em>REMOTE SENSING</em>, 12(11): 1898-1919.en_US
dc.citation.titleREMOTE SENSINGen_US
dc.citation.volume12en_US
dc.citation.number11en_US
dc.identifier.doi10.3390/rs12111898-
dc.citation.startPage1898en_US
dc.citation.endPage1919en_US
dc.description.articleClassificationSCIE-
dc.description.jcrRateJCR 2018:23.333en_US
dc.subject.keyworddata reconstructionen_US
dc.subject.keywordchlorophyll-a concentration (CHL)en_US
dc.subject.keywordrandom forest (RF)en_US
dc.subject.keywordRoss Seaen_US
dc.subject.keywordAntarcticaen_US
dc.identifier.localId2020-0082-
dc.identifier.scopusid2-s2.0-85086444937-
dc.identifier.wosid000543397000203-
Appears in Collections  
2019-2020, Ecosystem Structure and Function of Marine Protected Area (MPA) in Antarctica (19-20) / Kim, Jeong-Hoon (PM19060)
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