# New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach
## 3 Results  
Table 4 presents the results of Experiments 1-1 and 1-2, which supported Hypothesis 1, that is, estimation using in situ measurement data as the (meteorological) input to machine learning is more reasonable than using remote-sensing and modeling data. The actual measurement data should be more reliable than the remote-sensing and modeling data. For example, the footprints of EC flux data (size: \(<\)1 km) were matched with those of the in situ measurement data (\(<\)1 km), rather than the remote-sensing grid (LST: 3 km; SWR: 5 km) and modeling data (\(\sim\)250 km). Considering the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \(R_{sdt}\) & \(T_{air}\) & **VPD** & \(P\) & **LAI** & **EVI** & **LSWI** \\  & **(MJ m\({}^{-2}\))** & **(\({}^{\circ}\)C)** & **(hPa)** & **(mm)** & **(m\({}^{2}\) m\({}^{-2}\))** & **(Untilless)** & **(Untilless)** \\ \hline
2003 & 4750 & 15.8 & 6.6 & 1740 & 0.96 & 0.30 & 0.10 \\
2004 & 5190 & 16.5 & 8.6 & 1594 & 0.99 & 0.30 & 0.08 \\
2005 & 5298 & 15.8 & 7.0 & 1272 & 0.92 & 0.29 & 0.08 \\
2006 & 4909 & 15.9 & 7.0 & 1683 & 0.89 & 0.29 & 0.09 \\
2007 & 4833 & 16.2 & 7.3 & 1678 & 0.94 & 0.30 & 0.09 \\
2008 & 5100 & 16.1 & 7.3 & 1098 & 0.91 & 0.28 & 0.08 \\
2009 & 5186 & 15.9 & 7.2 & 1278 & 0.99 & 0.30 & 0.10 \\
2010 & 4897 & 15.2 & 6.3 & 1496 & 0.94 & 0.30 & 0.10 \\
2011 & 5147 & 14.8 & 6.2 & 1499 & 0.85 & 0.29 & 0.08 \\
2012 & 5107 & 14.8 & 6.2 & 1695 & 0.78 & 0.29 & 0.09 \\
2013 & 5319 & 15.4 & 6.1 & 1078 & 0.89 & 0.29 & 0.07 \\
2014 & 5082 & 15.5 & 5.7 & 1173 & 0.93 & 0.31 & 0.10 \\
2015 & 5162 & 15.7 & 5.6 & 1158 & 0.89 & 0.29 & 0.09 \\ \hline AVG & 5075 & 15.7 & 6.7 & 1419 & 0.91 & 0.29 & 0.09 \\ STD & 176 & 0.5 & 0.8 & 250 & 0.06 & 0.01 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Annual values of integrated (or averaged) downward shortwave radiation (\(R_{sdt}\)), daytime air temperature (\(T_{air}\)), daytime vapor pressure deficit (VPD), precipitation (\(P\)), leaf area index (LAI), enhance vegetation index (EVI), and land–surface water index (LSWI) from 2003 to 2015 for the study site. AVG denotes average; STD denotes standard deviation. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Hypotheses**} & **Exp.** & **Target** & \multirow{2}{*}{**Training Year**} & **Meteorological** \\  & **No.** & **Year** & & **Input Source** \\ \hline (1) Estimation using in situ measurement data as the input for machine learning is more reasonable than that using remote-sensing and modeling data. & 1-2 & 2009 & 2008 \& 2010 \\ \hline \multirow{4}{*}{(2) A training dataset for machine learning that is closer to the gaps results in better estimation.} & 2-1 & 2008 \& 2010 \\  & 2-2 & 2006 \& 2011 \\  & 2-3 & 2009 & 2005 \& 2012 \\  & 2-4 & 2004 \& 2013 \\  & 2-5 & 2003 \& 2015 \\ \hline \multirow{4}{*}{(3) A longer training dataset for machine learning results in better estimation.} & 3-1 & 2008 \& 2010 \\  & 3-2 & 2008 \& 2010 \\  & 3-3 & 2009 & 2006, 2008, 2010, \& 2011 \\  & 3-4 & 2009 & 2005, 2006, 2008, 2006, 2008, 2010, 2011, 201, 2012, 2013, 2014, 2012, 2011, 2012, 2013, 2015 & \\ \cline{1-1}  & 3-5 & 2009 & 2010, 2011, 2012, 2012, 2013, 2015 & \\ \cline{1-1}  & 3-6 & 2013 \& 2015 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hypotheses and experimental design. spatial heterogeneity of the study site, we expected the SVR-based model from Experiment 1-1 to outperform the one from Experiment 1-2 e.g., [31]. The estimations using both the in situ measurement data and remote-sensing (and modeling) data agreed reasonably well with the observations (Figure 4, except for the ET from Experiment 1-2 around DOY (day of year) 180). As expected, the day-to-day fluctuations of estimations using remote-sensing and modeling data were significantly smaller than those of the observations, resulting in higher root-mean-square errors (RMSE) and lower coefficients of determination (\(r^{2}\)) than those for the estimations using the in situ measurement data, except for the RE with small actual day-to-day variations.