Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band

Flood events have become intense and more frequent due to heavy rainfall hurricanes caused by global warming. Accurate floodwater extent maps are essential information sources for emergency management agencies flood relief programs direct their resources the most affected areas. Synthetic Aperture Radar (SAR) data superior optical mapping, especially in vegetated areas forests that adjacent urban critical infrastructures. Investigating mapping with various available SAR sensors comparing performance allows identification of suitable can be used map inundated different land covers, such as In this study, we investigated polarization configurations boundary delineation open derived from Sentinel1b, C-band, Uninhabited Aerial Vehicle (UAVSAR) L-band collected during resulting Hurricane Florence eastern area North Carolina. The datasets flooding event on same day study were processed classified five landcover classes using a machine learning method—the Random Forest classification algorithm. We compared results linear, dual, full polarizations datasets. fully polarized achieved highest accuracy decomposition cover features identified based scattering mechanisms.