## 4 Results
### Cross-Comparison between Predicted Land Cover Maps and the CDL  
Table 11 and Tables S12-S15 show results of pixel-based comparisons between predicted land cover maps and the CDL for different sample pools and sizes. The map OA are much lower than the model OA (~80% versus ~90%). This result makes sense because, in the model AA, each RFC model was optimized to a specific sample dataset (that likely does not fully describe characteristics of all land cover classes). Then, each of these models was used to predict land cover for the much larger area (the entire study area was about 800-20,000 times larger than the training data). Unlike model AA, the pixel-based comparison between predicted land cover maps and the CDL (map AA) revealed that the C1S RFC map agreed more with the CDL than the C2M RFC map. The cross-comparison indicated that, even if we built excellent models from the sample data, we could expect considerable \"differences\" in predicted land cover map compared to the \"reference\" cover map. RFC models trained with larger sample sizes did yield better land cover prediction. Although improvement of OA in the map AA (about 2% to 3% for ARD and HLS data, respectively) were not as large as those in the model AA (about 6%), this result was expected because many more predictions were made to create the land cover map (the entire study area) than in model AA (up to 0.25% of study area). Even a small increase of the map OA (e.g., 0.1%) translates into a large area. A higher k_Q compared to k_L in all scenarios indicated that the proportional distribution of predicted maps was quite close to the CDL and the majority of classification errors came from misallocation of pixels. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**SPL RFC Models**} & \multicolumn{4}{c}{**CMB RFC Models**} \\ \cline{2-7} \# & \multicolumn{2}{c}{**2016**} & \multicolumn{2}{c}{**2017**} & \multicolumn{2}{c}{**2016**} & \multicolumn{2}{c}{**2017**} \\ \cline{2-7}  & **ARD** & **HLS** & **ARD** & **HLS** & **ARD** & **HLS** & **ARD** & **HLS** \\ \hline
1 & P20\_S1 & P20\_B & P20\_S1 & P20\_B & P20\_S1 & \(\alpha\) & P20\_S1 & \(\alpha\) \\
2 & P20\_S2 & P80\_S2 & P80\_S2 & P20\_S2N & giMD & P20\_B & \(\alpha\) & giMD \\
3 & P80\_S2 & P80\_N & P20\_S2 & P80\_S2 & \(\alpha\) & \(\beta\) & P20\_S2 & HTV \\
4 & P20\_S2R & P80\_S2G & P80\_S2G & P20\_R & **gri** & **minx** & HTV & minx \\
5 & P80\_S2G & P20\_G & P20\_N & P80\_S2G & P20\_S2 & \(\gamma\) & minx & gre \\
6 & P80\_N & P20\_R & P50\_S2N & P20\_N & \(\beta\) & o\_fit & giMD & \(\beta\) \\
7 & P20\_N & P50\_NR & P20\_R & P20\_G & **minx** & HTV & gre & P20\_B \\
8 & P20\_S2G & P50\_NB & P20\_S2N & P80\_S2R & **HTV** & **gri** & \(\beta\) & gri \\
9 & P80\_S2R & P80\_S2R & P50\_S2 & P20\_S2R & \(\gamma\) & ymax & \(\gamma\) & P20\_R \\
10 & P50\_NB & P20\_S2R & P20\_S2G & P50\_NR & P20\_S2R & P20\_G & gri & \(\gamma\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Top 10 important variables of SPL and CMB RFC models. In the SPL RFC models, spectral variables in the top 5 that involved SWIR-2 bands are highlighted in light yellow. In the CMB RFC models, spectral variables are highlighted in light blue  and phenometrics from the CxQ and HPLM are highlighted in light orange  and light green, respectively. **Bolded** items are variables related to the initial green-up phase of the seasonal growth.