## 4 Results  
### Accuracy Assessment of RFC Models  
Overall accuracy and kappa indices for the location and quantity of the 2016 RFC models are summarized by sample pools and sizes in Table 5. The pairwise comparison of accuracy metrics using the Mann-Whitney U and the TOST equivalence tests appear in Tables S3 and S4, respectively. Generally, RFC models using C2 sample pools (2 pixels away from the evaluated pixel; C2S, C2M) had significantly higher accuracy metrics than those using C1 sample pools (C1S, C1M) for all combinations of year and data source. Sample pools that matched 2016 and 2017 land covers (M) yielded more accurate RFC models than those based on land cover from only a single year (S). For all combinations of year and data source, RFC models using larger sample size had significantly higher accuracy metrics. We observed the largest improvements in accuracy metrics from P01 to P05 RFC models, with relative increases of 4.1%, 1.7%, and 6.4% for OA, k_L, and k_Q, respectively. Larger increases in k_Q compared to k_L indicated that improvement in model accuracy was mostly due to better quantity agreement of P05 compared to P01 RFC models. In other words, proportional distributions of land cover classes in P05 RFC models were generally closer to the CDL than those of P01 RFC models. P05 samples were five times larger than P01 samples, which enabled better description for all classes, especially minor cover types. Accuracy improvement from P05 to P15 RFC models was moderate, with relative increases of 1.7%, 1.1%, and 1.8% for OA, k_L, and k_Q, respectively. Relative differences in accuracies of P15 and P25 RFC models were minor, less than 0.6% for all three metrics. Among RFC models using different input datasets, models using phenometrics (CxQ and HPLM) had the lowest accuracy metrics. There was no obvious choice between the 2016 RFC models using CxQ versus HPLM; the HPLM RFC models performed better on ARD data and the CxQ RFC models better on HLS data. For 2017 data, the HPLM RFC models slightly edged CxQ RFC models with less than 1% higher OA (Table S2). Although differences in 2017 OA between CxQ and HPLM RFC models were statistically significant in the Mann-Whitney U tests, the TOST equivalence tests indicated that the differences were within a user-defined indifference zone (i.e., the two models were equivalent). Spectrally-based RFC models (SPL) were more accurate than the phenometrically-based RFC models, with approximately 3% higher OA, relatively. Unlike the sample size scenarios, improvement in SPL RFC models mostly came from better location of pixels (small change in proportional distribution) shown by a higher relative increase in k_L (approximately 5%) compared to k_Q (approximately 1.7% and -3.2% in 2016 and 2017, respectively), indicating that locations of land covers were described more accurately using spectral information. More importantly, RFC models with combined variables (CMB) consistently outperformed RFC models using solely spectral variables (SPL) or phenometrics (CxQ or HPLM). Similar results were found for the 2017 data (Table S2). Table 6 and Table S5 show producer's and user's accuracies for the RFC models using C1S and C2M sample pools (the worst and the best sample pool scenarios based on the results in Table 5 and Table S2). Between C1S and C2M RFC models, relative differences in both producer's and user's accuracies were less than 2.5% for corn, soybean, and water classes. Those three classes were also higher accuracy classes. C2M RFC models had relatively higher producer's and user's accuracies than C1S RFC models in all other classes, including wheat (4.6-12.5%), alfalfa (11.4-16.3%), barren/developed (6.7-18.4%), wetland (16.6-20.4%), and other crops (90-356%). Compared to corn, soybean, and water, the other cover types have more complicated aggregates of phenological and spectral characteristics that make mapping more difficult. For example, barren/developed includes both vegetated (lawn, garden) and non-vegetated (barren, impervious surface) land covers.