## 6 Conclusions  
The focus of this study was to evaluate classification accuracy using different sets of input variables derived from either Landsat ARD or HLS time series, including phenometrics generated from two land surface phenology models (CxQ and HPLM), spectral variables, and the combined set of phenometrics and spectral variables. Between the two phenometrically-based classifications, HPLM RFC models exhibited slightly better accuracy but absolute differences in OA were minor (\(<\)1%), mostly due to more precise pixel allocation of land cover. Compared to the phenometrically-based RFC models, the spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the spectrally-based RFC models could not classify wheat accurately. As hypothesized, the most accurate RFC models were retrieved when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classifications (low accuracies for non-vegetated covers) and spectrally-based classifications (low accuracies for wheat). The analysis of important variables indicated that classifications of the study area were strongly driven by variables related to the initial green-up phase of seasonal growth and highest EVIZ over the growing season. We explored land use/land cover classification under different sample pool and sample size scenarios. First, to improve the land cover accuracy of the sample data, both spatial and temporal filters were applied to compensate classification errors of the CDL and offsets between input datasets. The results indicated that a sample pool with a minimum correction of land cover information yielded the most accurate predicted map. Next, land cover classification was also tested with different sample sizes. Although previous findings suggested that a sample size should cover at least 0.25% of the study area to achieve an accurate (OA \(\geq\) 0.90) land cover map, smaller datasets would be acceptable for classification, but should not smaller than 0.05% of the study area, since classification accuracy would decrease rapidly below that threshold. Land surface phenology modeling requires a substantial number of good quality observations over a year [49]; thus, it may be less suitable for areas with persistent cloud cover if only optical data are available to characterize the LSP. However, the prospect of using phenometrics to enhance land use/land cover classification is very promising. First, our results proved that the use of phenometrics and spectral variables together yielded the most accurate classification and overcame limitations of both phenometrically-based and spectrally-based classifications. Second, seasonality information from all spectral band and ratio time series could be extracted to enhance classification accuracy (e.g., [50]). Finally, the temporal resolution of satellite data can be improved by using comparable sensor datastreams, e.g., Landsat and Sentinel-2, but substantial pre-processing is required to achieve compatibility. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2072-4292/11/14/1677/s1](http://www.mdpi.com/2072-4292/11/14/1677/s1)]([http://www.mdpi.com/2072-4292/11/14/1677/s1](http://www.mdpi.com/2072-4292/11/14/1677/s1)). **Author Contributions:** Conceptualization: [PERSON] and [PERSON]; data curation and analysis: [PERSON]; writing--draft: [PERSON] and [PERSON]; writing--review and editing: [PERSON] and [PERSON]. **Funding:** This research was supported, in part, by NASA Land Cover Land Use Change program project NNX14 AJ32G, the Geospatial Sciences Center of Excellence at South Dakota State University, and the Center for Global Change and Earth Observations at Michigan State University. **Acknowledgments:** We would like to thank [PERSON] and [PERSON], Geospatial Science Center of Excellence, South Dakota State University, for assistance with the HPLM fitting. **Conflicts of Interest:** The authors declare no conflict of interest.