# New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach
## 5 Conclusions: Gap-Filling Strategies for Long-Period Flux Data Gaps  
Based on the results and discussion, we propose the following gap-filling strategies for long-period flux data. * In situ measurement data should be preferentially used as the input for machine learning, if available. * Data covering as long a period as possible should be used to train the machine-learning-based model. * If there has been a significant ecosystem state change over the study period and the primary objective of gap-filling is to quantify interannual variability rather than seasonality, multiple models should be established for each ecosystem state. If in situ measurements are not available, there is no choice but to use remote-sensing or modeling data. In such a case, we recommend the temporal resolution of the machine-learning-based model be increased (e.g., one-day to eight-day). In a follow-up study, we will develop methodologies to establish a machine-learning-based model trained using all data, giving weight to the data close to the gaps and considering a time lag between fluxes and drivers (e.g., the relationship between vegetation greenness and GPP [41]). Our findings in this paper emphasized the validity of the long-term database for both gap-filling and the estimation of terrestrial CO2/H2O fluxes using a data-driven approach. Particularly, they support the idea that the relationships between fluxes and their drivers can change in the long term, implying the necessity for continuous measurements to understand such changes. Figure 10: Time series of the GPP (**a**), RE (**b**), NEE (**c**), and ET (**d**) at the study site in 2007 and 2014 from the observational data only (Obs only) and the gap-filled data using the SVR-based model (Obs + SVR). **Supplementary Materials:** The following are available online at [[http://www.mdpic.com/2073-4433/10/10/568/s1](http://www.mdpic.com/2073-4433/10/10/568/s1)]([http://www.mdpic.com/2073-4433/10/10/568/s1](http://www.mdpic.com/2073-4433/10/10/568/s1)), Figure S1: Time series of daily downward shortwave radiation (\(R_{\text{sdn}}\), **a**), daytime air temperature (\(T_{\text{air}}\), **b**), daytime vapor pressure deficit (VPD, **c**) and precipitation (\(P\), **d**) at the flux tower, Figure S2: Time series of daily leaf area index (LAI, **a**), enhanced vegetation index (EVI, **b**), and land-surface water index (LSWI, **c**) from the eight-day moderate resolution imaging spectroradiometer (MODIS) data products (MOD15A2 and MCD43A4 version 6) for the study site, Figure S3: Relationships between the length of the training dataset and the root-mean-square error (RMSE) for error assessment of Experiments 3-1, 3-2, 3-3, 3-4, 3-5, and 3-6 (**a**: gross primary production, **b**: ecosystem respiration, **c**: net ecosystem exchange, **d**: evapotranspiration), Figure S4: Same as Figure S3 (**a**: gross primary production, **b**: ecosystem respiration, **c**: net ecosystem exchange, **d**: evapotranspiration), but for \(r^{2}\), Table S1: Annual data retrieval rates, lengths of the first and second longest gaps, and total lengths of the long gaps for CO\({}_{2}\) flux (\(F_{\text{CO}_{2}}\)) and evapotranspiration (ET) from 2006-2015 for the Gwangneung deciduous forest in the Korea National Arboretum (GDK) site. AVG denotes average; STD denotes standard deviation. **Author Contributions:** Conceptualization, M.K., K.I., and J.K.; methodology, M.K. and K.I. ; measurement data production, M.K., J.K., Y.M.I, M.M., J.-H.L., and J.-H.C.; software, M.K., K.I, and J.P.; writing--original draft preparation, M.K. ; writing--review and editing, K.I., J.P., and M.M. ; supervision, J.K.; funding acquisition, M.K. and J.K.  
**Funding:** This work was supported by the Weather Information Service Engine Program of the Korea Meteorological Administration under grant KMIPA-2012-0001-2, by the Development Program on See-At Technology for Meteorology and Earthquake of the Korea Meteorological Administration under Grant KMI2018-05810, and by the R&D Program for Forest Science Technology (project no.