## 4 Discussion
[30] encountered some success in mapping surface water dynamics with Sentinel-1 SAR image time series in South Dakota, prior research in Alberta by [PERSON] et al. [29] has shown Sentinel-1 SAR had little value for differentiating open water and grasslands in southern Alberta due to the lower noise floor of Sentinel-1. Other sensors such as RADARSAT-2 seem to be valuable for water dynamics in the PPR [31] but prove to be much harder to apply operationally over larger areas. SAR data will likely be very important for future efforts at PPR wetland mapping/monitoring but S2 data seem to be the best open-access data, currently, for capturing water dynamics and achieving a relatively small MMU. The resulting wetland inventory product generated from the hydroperiod data plus GEDI-based CHM for marsh versus swamp proved to be accurate at the wetland class level when compared to photo-interpreted data from DUC. Unsurprisingly, upland (i.e., non-wetlands) were very accurate (98%) as this method only classified wetlands if the area, at any time, was flooded/saturated during the S2 image stack. The producer and user accuracies of the marsh class (73% and 60%, respectively) were considered reasonably accurate given the small and dynamic nature of this landcover type. The majority of both the omission and commission errors in the marsh class is the result of confusion with upland, and there does not appear to be a systemic bias in either direction. Some of this discrepancy is probably due to what a photo-interpreter observed and delineated in the high-resolution imagery at a particular point in time and what was recorded and resolved by the time-series S2 data, for our classification. As noted, several physical and ecological components of the PPR landscape can appear very different from year to year and season to season, given that the majority of the wetland features we identified are temporary marshes with hydroperiods of 20% and below. As a result, we argue that accuracy measurements for this area are useful but do not tell the whole story of how the data are capturing ecological patterns. A mismatch in results is influenced by both the scale and the timing of the input data versus validation data. The swamp class could not be reliably assessed in this study due to limited samples in the assessment area. Swamps are more prevalent farther west towards the Rocky Mountains, where accuracies may be higher, but we did not have access to validation data from this area. The 9% producer accuracy of the open water hydroperiod class requires some explanation. Open water as defined by the hydroperiod method included all areas flooded 85% or more of the time. This includes deep water bodies like reservoirs and lakes, which comprises most of the permanent open water area in the hydroperiod-based inventory, as well as shallow open water wetlands. The validation data from DUC focused on shallow open water wetlands only, however, and thus does not include of the obvious large water bodies. This is an important source of misalignment with this validation dataset. Whereas nearly all the open water in the our hydroperiod-based inventory corresponded to shallow open water in the validation dataset, generating almost 100% user accuracy, a significant amount of shallow open water in the validation data was classified as marsh by the hydroperiod method. These shallow open water wetlands likely appeared dry in two or more of the S2 image dates, resulting in less than 85% hydroperiod calculation from the image stack, and subsequent placement in the marsh class using this approach. They were evidently filled with open water at the time of the DUC air photo acquisition, however, and captured as such in the validation data.