## 4 Results
### Accuracy Assessment of RFC Models
In addition, minor crops and non-agriculture classes are likely to have lower accuracy in the CDL compared to corn and soybean (commodity crops) or to open water (distinct spectral characteristics), resulting in lower accuracy in the training and validating data. Nevertheless, all improvements from C1S and C2M RFC models were statistically significant (Table S6). However, differences in PA/UA of corn, soybean, water and barren/developed were generally within the indifference zones (or no obvious improvements were observed for those classes). \begin{table}
\begin{tabular}{c l c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Scenario**} & \multicolumn{6}{c}{**ARD**} & \multicolumn{6}{c}{**HLS**} \\ \cline{3-11}  & & **OA** & **k\_L** & **k\_Q** & **OA** & **k\_L** & **k\_Q** & \\ \hline \multirow{3}{*}{**Sample Pool**} & C1S & 88.8 & 0.904 & & 0.917 & & 86.8 & & 0.884 & & 0.906 & \\  & C1M & 90.7 & *** & 0.923 & *** & 0.926 & *** & 88.7 & *** & 0.904 & *** & 0.914 & *** \\  & C2S & 90.4 & NS & 0.921 & NS & 0.924 & NS & 89.4 & *** & 0.909 & *** & 0.921 & *** \\  & C2M & 91.8 & *** & 0.935 & *** & 0.932 & *** & 90.7 & *** & 0.922 & *** & 0.927 & *** \\ \hline \multirow{3}{*}{**Sample Size**} & P01 & 87.1 & 0.906 & & 0.877 & & 84.8 & & 0.883 & & 0.862 & \\  & P05 & 90.5 & *** & 0.919 & *** & 0.929 & *** & 89.0 & *** & 0.903 & *** & 0.922 & *** \\  & P15 & 91.8 & *** & 0.927 & *** & 0.944 & *** & 90.6 & *** & 0.914 & *** & 0.940 & *** \\  & P25 & 92.3 & *** & 0.931 & * & 0.949 & *** & 91.2 & *** & 0.919 & *** & 0.945 & *** \\ \hline \multirow{3}{*}{**Input Set**} & CxQ & 86.8 & & 0.879 & & 0.914 & & 86.0 & & 0.871 & & 0.911 & \\  & HPLM & 88.6 & *** & 0.900 & *** & 0.919 & NS & 85.2 & NS & 0.867 & NS & 0.898 & NS \\  & SPL & 92.2 & *** & 0.943 & *** & 0.927 & *** & 91.0 & *** & 0.928 & *** & 0.925 & *** \\  & CMB & 94.1 & *** & 0.961 & *** & 0.938 & *** & 93.4 & *** & 0.953 & *** & 0.936 & *** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall accuracy (in percent) and kappa indices for location and quantity of 2016 RFC models summarized by sample pools, sample sizes, and input variables. A particular scenario (current row) was compared to a scenario right above it (above row) using the nonparametric Mann-Whitney U test and the TOST equivalence test. The null hypothesis of the U test is that a random accuracy metric of the first scenario (above row) will be less than a random accuracy metric of the second scenario (current row). Significance levels of the U test are indicated by ***, **, and * for p-values of less than 0.001, 0.01, and 0.05, respectively. NS stands for “not significant”. Results of the TOST equivalence test are highlighted in \(\underline{\text{light blue}}\) for “not equivalent” and \(\underline{\text{light yellow}}\) for “equivalent”. Full pairwise comparisons are provided in Tables S3 and S4. Between the least accurate and the most accurate sample size scenarios (P01 versus P25 RFC models), relative differences in water were less than 2.3% (Table 7, Table S7), likely due to very distinct spectral responses of water compared to other covers. Relative improvements in PA/UA of major classes (corn, soybean, and grassland) were also minor (less than 5%), as there were already many training pixels in each class even with the smallest sample size. However, increases in both PA and UA of those classes were statistically significant (Table S6). Both producer's and user's accuracies improved significantly for minor crops and non-agricultural cover types (10-70% relative higher PA/UA), including wheat, alfalfa, other crops, wetland, and barren/developed. Considering that minor cover types have mixed spectral and phenological characteristics, larger sample sizes would allow those classes to be described more thoroughly in the training, thereby improving accuracies. Table 8 and Table S8 show the PA/UA of RFC models using different sets of input variables.