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
O 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
O fluxes using a data-driven approach.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.