## 1 Introduction
Addressing these mapping challenges using remote sensing is frequently done, given the advantages of these approaches (e.g., higher cost effectiveness and broader coverage than field methods). A recent, updated review of remote sensing approaches to PPR mapping is provided by [PERSON] et al. [10], wherein the authors offer a comprehensive perspective on the topic. Good inventory data of PPR wetlands can be obtained through high-resolution photo-interpretation, such as the data provided in Duck Unlimited Canada's (DUC) high-resolution mapping program [11]. However, generating these data over large areas (e.g., whole provinces or states) is typically expensive and time-consuming. Other wetland large-scale classification methods using Earth observation (EO) data, such as those acquired by Landsat, Sentinel-1/2, or SPOT [12; 13; 14; 15; 16], have produced promising results in less dynamic wetland landscapes (i.e., boreal or forested ecosystems). However, these methods tend to miss many of the temporary/seasonal wetlands in the PPR due to the static nature of most landcover classifications. Additionally, open-access EO data (e.g., Landsat, Sentinel-2) typically do not provide high enough resolution to capture these very small wetland features [17]. The training of a wetland classification model in the PPR is also troublesome. Datasets that are normally considered to be high-quality training for landcover classifications, such as the Alberta Biodiversity Monitoring Institute's (ABMI) photoplots [18] used in wetland/landcover studies such as in [PERSON] et al. [12], [PERSON] et al. [15], and others [19; 20; 21; 22; 23], do not appear to be very useful in the PPR. These data contain over 100,000 spatially explicit, photo-interpreted, attributed wetland polygons which are normally ideal for wetland class, form, and type classifications, but ultimately, these data are derived from a static interpretation of the landscape during late summer or fall. Therefore, the attribution of the water permanence of the interpreted wetland polygon typically will not match the permanence derived from EO data. Additionally, these wetland landscapes can change year-to-year and, therefore, the wetland landscape during the year of photo-interpretation may not resemble the one during the required year of classification. Dense, high-resolution, time-series photo-interpretation matching the years and seasons of the required EO data is the ideal training and validation data but, again, this is expensive and time-consuming. Time-series field observations of pothole wetlands over multiple years and seasons are also an option, but achieving a sufficient number of field sites to train modern machine learning algorithms is very challenging. Despite these challenges, other studies have managed to map wetland potholes at smaller scales. For example, [PERSON] et al. [24] managed to capture the temporal signature of pothole wetlands with multi-temporal National Agriculture Imagery Program (NAIP) imagery and a Light Detection and Ranging (LiDAR) Digital Elevation Model (DEM) using Google Earth Engine (GEE) [25] in North Dakota, United States. This proposed workflow is likely ideal for pothole mapping as it captured the natural seasonality and could map some of the smallest potholes with the 1 m resolution NAIP imagery. Unfortunately, NAIP is not available in Canada and, thus, another optical data source is needed. [PERSON] et al. [17] have also followed an optical multi-temporal approach for pothole mapping/monitoring in Saskatchewan, Canada using Landsat data. This method is promising for Canadian areas, but the authors acknowledged that future Sentinel-2 (S2) data will provide even better spatial and temporal resolutions for mapping small wetlands.