Wildfires and prescribed fires are frequent but under-mapped across wetlands of the southeastern United States . High annual precipitation supports rapid post-fire recovery of wetland vegetation, while associated cloud cover limits clear-sky observations. In addition, the low burn severity of prescribed fires and spectral confusion between fluctuating water levels and burned areas have resulted in wetland burned area being chronically under-estimated across the region. In this analysis, we first quantify the increase in clear-sky observations by using Sentinel-2 in addition to Landsat 8. We then present an approach using the Sentinel-2 archive (2016-2019) to train a wetland burned area algorithm at 20 m resolution. We coupled a Python-derived random forest model with Google Earth Engine to apply the algorithm across the southeastern United States (>290,000 km2). The burned area extent was validated (burned, unburned) using points derived from 27 WorldView-2 and WorldView-3 images. The burned area extent was compared to 555 wetland fire perimeters compiled from state and federal agencies. On an annual timestep, combining the Sentinel-2 and Landsat 8 data increased the mean observation count from 17 to 46 in 2016 and from 16 to 78 in 2019. When validating single-scene burned area extent, the Sentinel-2 output had 29% and 30% omission and commission error rates, respectively. We compared this to the U.S. Geological Survey’s Landsat 8 Burned Area Product (L8 BA), which had 47% and 8% omission and commission error rates, respectively. Across the four-year period, by count the Sentinel-2 burned area detected 78% of the wetland fire perimeters, compared to the L8 BA which detected 60% of the wetland fire perimeters. By area, Sentinel-2 burned area mapped 48% of the perimeter area as burned, compared to the L8 BA which mapped 32% of the perimeter area as burned. This analysis demonstrated the potential of Sentinel-2 to support efforts to track burned area extent even across challenging ecosystem types, such as wetlands.