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Open AccessArticle

New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach

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National Center for Agro Meteorology, Seoul 08826, Korea
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Center for Environmental Remote Sensing, Chiba University, Chiba 2638522, Japan
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Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul 08826, Korea
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Interdisciplinary Program in Agricultural and Forest Meteorology, Seoul National University, Seoul 08826, Korea
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Research Institute for Agriculture and Life, Seoul National University, Seoul 08826, Korea
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Future Earth Program, Asia Center, Seoul National University, Seoul 08826, Korea
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Institute of Green Bio Science and Technology, Seoul National University Pyeongchang Campus, Pyeongchang 25354, Korea
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Department of Earth and Environment, Boston University, Boston, MA 02215, USA
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Forest Ecology & Climate Change Division, National Institute of Forest Science, Seoul 02455, Korea
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Research Planning & Coordination Division, National Institute of Forest Science, Seoul 02455, Korea
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Author to whom correspondence should be addressed.
Atmosphere 2019, 10(10), 568; https://doi.org/10.3390/atmos10100568
Received: 17 August 2019 / Revised: 17 September 2019 / Accepted: 19 September 2019 / Published: 22 September 2019
In the Korea Flux Monitoring Network, Haenam Farmland has the longest record of carbon/water/energy flux measurements produced using the eddy covariance (EC) technique. Unfortunately, there are long gaps (i.e., gaps longer than 30 days), particularly in 2007 and 2014, which hinder attempts to analyze these decade-long time-series data. The open source and standardized gap-filling methods are impractical for such long gaps. The data-driven approach using machine learning and remote-sensing or reanalysis data (i.e., interpolating/extrapolating EC measurements via available networks temporally/spatially) for estimating terrestrial CO2/H2O fluxes at the regional/global scale is applicable after appropriate modifications. In this study, we evaluated the applicability of the data-driven approach for filling long gaps in flux data (i.e., gross primary production, ecosystem respiration, net ecosystem exchange, and evapotranspiration). We found that using a longer training dataset in the machine learning generally produced better model performance, although there was a greater possibility of missing interannual variations caused by ecosystem state changes (e.g., changes in crop variety). Based on the results, we proposed gap-filling strategies for long-period flux data gaps and used them to quantify the annual sums with uncertainties in 2007 and 2014. The results from this study have broad implications for long-period gap-filling at other sites, and for the estimation of regional/global CO2/H2O fluxes using a data-driven approach. View Full-Text
Keywords: eddy covariance; long-term database; gap-filling; long gap; data-driven approach; uncertainty eddy covariance; long-term database; gap-filling; long gap; data-driven approach; uncertainty
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Kang, M.; Ichii, K.; Kim, J.; Indrawati, Y.M.; Park, J.; Moon, M.; Lim, J.-H.; Chun, J.-H. New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach. Atmosphere 2019, 10, 568.

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