## 4 Discussion
There is clearly some scope for overlap in the open water and marsh classes as both can be seasonal or semi-permanent in nature, and this validation discrepancy highlights a subset of features (temporary, seasonal, or semi-permanent shallow open water wetlands are captured quite differently on the basis of a hydroperiod rather than a single image date. A possible solution could be to distinguish shallow open water from graminoid marsh using optical vegetation productivity measures (e.g., Normalized Difference Vegetation Index (NDVI)). This approach requires further study. We were not able to validate the water permanence classes as this would require consistent field monitoring of wetlands. However, visual assessment (Figure 5) shows that class permanence matches with what we would expect ecologically. Temporary wetlands are small and barely visible in summer/fall high-resolution imagery (often plowed over in agricultural areas). Seasonal wetlands are visible vegetated depressions in the landscape and semi-permanent wetlands that are larger and usually have open water. The major finding of this work is that classifying water frequently and through time can generate accurate and reliable wetland data in the PPR and, potentially, other wetland landscapes with open vegetation structures (maybe wetland landscapes seen in studies like [PERSON] et al. [44]). Here, we have illustrated the advantages of multi-seasonal S2 optical imagery, aligning well with recommendations provided by [PERSON] et al. [10] in their review. However, it is likely that at least four years of time-series data are needed because \"peak wetland flooding\" does not always line up with cloud-free S2 image dates. GEE or other cloud computing platforms are probably needed to make this workflow efficient as processing thousands of S2 images and the classification of water in these images is a computationally intensive process. Spring 2018 images were very important for these areas as most of the temporary wetlands were detected with this imagery. Without these images, the majority of temporary wetlands would be missed. These \"peak wetland flooding\" images are key to this workflow because these images essentially act as a high resolution, hydrologically corrected DEM. After snow melt, all topographic lows (potholes) will naturally fill with water and thus mapping pothole depressions is essentially a water classification exercise, which is well established in the remote sensing literature [45]. It is important to note, however, that while these peak flooding spring images are integral to capturing temporary wetlands, it is the long time-series of multiple images from different periods that enable us to distinguish temporary, seasonal, semi-permanent, and permanent wetlands from one another (Table 2; Figure 5). The combination of images provides us with information on hydroperiod or frequency of flooding over time. In this workflow, there is no need for complicated convolutional neural networks [15], multi-class multi-sensor models, or high-density LiDAR point clouds. These methods are valuable and will likely produce comparable results but they will be much more time- and labor-intensive and, in our experience, without careful calibration of training data, they can often produce odd predictions. Another major finding of this work is that field or photo-interpretation may not be needed to generate reliable wetland data in the PPR. With open-access EO data and open processing platforms (e.g., GEE), the most expensive part of large-scale landcover mapping is often field data or high-resolution photo-interpretation data needed for training and validation of models. This method proposes a quick self-labeling of flooded and non-flooded areas from S2 images in three distinct seasons as necessary training data. Some independent validation data will always be required but this can be at a smaller scale than a normal dataset for training and validation. This method also proves to be highly scalable.