Surface Water Extent Extraction in Prairie Environments Using Sentinel-1 Image-Pair Coherence

Knowledge of surface water extent is critical for ecological and disaster monitoring. However, surface water extraction from optical satellite imagery is challenging due to the impact of weather. Synthetic Aperture Radar (SAR) can penetrate cloud cover and has significant advantages for surface water mapping, but the classification accuracy might be limited by SAR’s inherent properties and land cover, which have similar backscatter to surface water. This study finds that the accuracy of surface water extraction at the Prairie Pothole Region (PPR) can be improved by combining interferometric coherence and backscatter for machine learning classification. This study performs time-series analysis on surface water and land to investigate their discrimination at different seasonal periods. The accuracy improvement of this method on Sentinel-1 images reached 10% during the seasons of fall and winter, where the combination of backscatter and coherence was proven to be efficient for separating water and land. Hence, our approaches of combining backscatter and coherence provide new insights for surface water extraction from SAR images in future studies.