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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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Title
Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning
Other Titles
머신러닝을 이용한 로스해 해색원격탐사 엽록소 농도 복원 연구
Authors
Park, Jinku
Kim, Hyun-cheol
Bae, Dukwon
Jo, Young-Heon
Subject
Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology
Keywords
data reconstruction; chlorophyll-a concentration (CHL); random forest (RF); Ross Sea; Antarctica
Issue Date
2020-06
Citation
Park, Jinku, et al. 2020. "Data Reconstruction for Remotely Sensed Chlorophyll-a Concentration in the Ross Sea Using Ensemble-Based Machine Learning". REMOTE SENSING, 12(11): 1898-1919.
Abstract
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
URI
https://repository.kopri.re.kr/handle/201206/12059
DOI
http://dx.doi.org/10.3390/rs12111898
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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