Remote sensing of surface water provides a powerful tool to inform the management of waterfowl habitat, but there is little information available to directly assess the relative accuracy of different remote sensing datasets. Our objective was to understand how the characteristics of remotely sensed inundation datasets inform dataset accuracy, the reliable detection of small waterbodies, the distribution of surface water, observation frequency and completeness of data: all attributes relevant to the management of waterfowl habitat. We compared surface water composites from 10 remotely sensed surface water datasets from 2016 to 2021 to in-situ surface water data for three complexes of small waterbodies in the U.S. Prairie Pothole Region and evaluated their accuracy at the pixel, waterbody and local landscape scales. While all products had high per-pixel balanced accuracies (>0.75), we found distinct differences in waterbody area and landscape distribution estimates among datasets. Sentinel-1-based datasets provided a more complete set of observations over time and were more sensitive in detecting water presence in smaller waterbodies but were less accurate at identifying waterbody area than other datasets evaluated. Landsat datasets, alternatively, produced simpler landscape distributions that largely omitted the smallest waterbodies. While the datasets that either fused Sentinel-1 and −2 data collections or utilized local training data had the highest performances (e.g. balanced accuracy = 0.92), all datasets had use-case scenarios for which they may be informative. Our comparisons revealed differences that were not evident in traditional pixel-scale accuracy assessment, such as an 18-fold difference in the number of inundated waterbodies identified across remote sensing datasets. These findings provide novel insights for waterfowl conservation management on howremote sensing datasets may differ in their ability to monitor annual spring surface water presence within landscapes dominated by small waterbodies, such as the Prairie Pothole Region.