## 5 Conclusions  
The PPR is a unique ecological region with wetland features that are both ecologically and economically important. These wetland potholes are difficult to map/inventory for conservation purposes because they are very dynamic seasonally and annually. It is also hard to collect proper field training and validation data for classification models. Past studies have found success using large satellite image stacks to capture the temporal signature of these wetland potholes. This study recreated some of these methods with 2017-2020 S2 data processed in GEE. By classifying water/flooding through time with a fine 2 m scale segmentation in GEE, we captured the hydroperiod (flood frequency) of pothole wetlands down to a MMU of 400 m\({}^{2}\). We used this hydroperiod data along with the GEDI CHM data to generate a seven-class wetland inventory. Wetland permanence was defined with the hydroperiod data, and swamp versus marsh classes were defined by the GEDI CHM. This relatively simple classification showed an overall accuracy of 95%, which is driven mainly by the ratio of upland to wetland areas, while wetland user and producer accuracies were approximately 70%. Shallow open water had a producer accuracy of only 9% due to commission errors with marshes, suggesting that some shallow open water overlapped with marshland in water permanence and is captured differently using our hydroperiod approach versus the photo-interpretation used in the validation data. The hydroperiod method does still have limitations as it is optimized for regions with open vegetation structures, reliable seasonal flooding, and non-complex wetland types. When compared to other existing remote sensing-derived inventories in the area, this method classifies many more pothole wetland features, indicating the power of dense time-series data to capture temporary pothole wetlands. Overall, we found that classifying water/flooding across four or more years with S2 data can generate a reliable wetland inventory in the PPR. We also found that a minimum of 8-10 satellite image dates are likely needed to accurately capture all wetland areas in the PPR and that early spring images (post snow melt) are probably the most important for identifying temporary pothole wetlands. Finally, we suggest that normal landcover or wetland training data may not be needed to generate wetland inventories in the PPR. We observed that simple, self-labelling of flooding and not flooded segments in S2 images is all that is needed to classify water across time. Figure 6: Comparison between (**a**) the time-series-based inventory for this study, and (**b**) the SPOT-derived Alberta Merged Wetland Inventory for an area with significant amounts of seasonal and temporary pothole wetlands. Background is the Esri World Imagery baselayer [43] (Source: Forty Mile County No. 8, Village of Warner, Maxar). We hope that methods similar to this can be scaled up to large areas of the PPR to produce a reliable inventory of the small but valuable wetland features. Monitoring of these features can then simply be done through the tracking of their seasonal flooding dynamics. Moving forward, EO data will only improve for tracking wetland flooding through time with better SAR sensors and higher-resolution optical sensors. An accurate baseline inventory of these PPR wetland features will help with the conservation of pothole wetlands, thus preserving their ecosystem functions and the valuable habitat for various species. Conceptualization, E.R.D. ; methodology, E.R.D., A.C., L.B., F.G., M.A. and B.B. ; software, E.R.D. and A.C.; formal analysis, E.R.D. and A.C.; writing--original draft preparation, E.R.D. and A.C.; writing--review and editing, L.B., F.G., M.A., B.B., J.K. and J.N.H. ; visualization, E.R.D. and A.C.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript. This research was funded by Alberta Environment and Parks, Government of Alberta, Canada, grant number 20 GRPOL10.