## 2 Data and Methods
### Hydrogenoid  
Flooded/non-flooded training data were created by labelling segments in three S2 image dates (spring, summer, and fall). First, a Simple Non-Iterative Clustering (SNIC) [40] algorithm was applied to 10 m resolution bands of the S2 imagery in GEE. In GEE, the size of segmented objects varies dynamically as a user zooms in and out to smaller or larger map viewing scales and different pyramiding levels are applied to the imagery; to fix the segments to a set size, a scale is specified in the export operation. This is not to be confused with the spatial resolution of the input data. For the prairie pothle wetlands, a relatively fine export scale was desired to capture as many small features as possible. By trial and experimentation, it was determined that a nominal scale (zoom level) of 2 m/pixel, in combination with an initial seed location spacing of 36 pixels and a compactness value of 0.1, produced segments that best represented the size and shape of wetland features in this area. These segments varied in size from a theoretical minimum of 2 m\({}^{2}\) to over 30,000 m\({}^{2}\) with a mean around 2000 m\({}^{2}\). Segment size is determined by the heterogeneity of pixel values in a given area. The initial SNIC segments were further refined by merging spectrally similar neighboring segments based on bands 4 (red), 8 (NIR), 11, and 12 (SWIR) at a weighting ratio of 1:3:2:3, and then exported as polygons.  
With reference to the representative seasonal S2 images and ESRI basemap imagery, a set of the merged segment polygons were labeled as flooded or non-flooded by a photo-interpreter. Within a given section, each seasonal training datum had a total of roughly 400 labelled polygons, of which approximately 50% were flooded and 50% were non-flooded.  
Flooded/non-flooded classification was performed on a date-by-date basis for selected S2 images of each processing section using the merged segments in a Random Forest (RF) classifier [41]. Each S2 image was segmented via the merged SNIC algorithm in GEE using the 10 m resolution bands: 2 (blue), 3 (green), 4 (red), and 8 (NIR). For each segment, the mean and standard deviation were calculated for the S2 10 m bands and the 20 m bands (5-7 (vegetation red edge), 11, and 12 (SWIR)), as well as the perimeter, width, height, and area of the segments. These segment statistics, along with the flooded/non-flooded labels, were inputs for the RF classifier, which was trained on 5000 pixels using 50 decision trees.  
Figure 2: S2 RGB mosaics for spring (2018), summer (2020), and fall (2020) with a zoomed map inset showcasing seasonal flood dynamics of prairie potholes.  
Finally, the results (i.e., flooded/non-flooded binary rasters) were exported from GEE again at a 2 m scale to achieve the smallest MMU possible, which typically produced 400-800 m\({}^{2}\) wetland segments.  
Exported flooded/non-flooded binary rasters were quality controlled by applying TWI [12] and human footprint [37] masks, as well as manually removing some falsely flooded pixels. To calculate hydroperiod for each section, all binary flooded/non-flooded rasters were added together, divided by the total number of images per section, and multiplied by 100 to derive the frequency of flooding as a percentage.