A New Algorithm for Land-Cover Classification Using PolSAR and InSAR Data and Its Application to Surface Roughness Mapping Along the Gulf Coast

During a flooding event, the ability of the terrain to dissipate water flow energy depends on its land-cover type and the associated surface roughness. In this study, we developed a new land-cover classification algorithm using repeat-pass polarimetric synthetic aperture radar (PolSAR) and interferometric synthetic aperture radar (InSAR) data. Through a two-level hierarchical approach, we classified nine land-cover types with distinct surface roughness coefficients (Manning's n). We demonstrated the performance of this algorithm using available L-band ALOS PALSAR scenes acquired between April 2007 and April 2011 over the Houston area. The radar-based surface roughness estimates show a good agreement with those independently derived from NOAA's 22-class Coastal Change Analysis Program (C-CAP) 2010 land-cover classification data. Our algorithm is robust, and the randomly selected training sets only account for 0.3% of the total multilooked radar pixels (30-m spacing). Furthermore, we were able to accurately map surface roughness over the New Orleans area using available ALOS PALSAR scenes without selecting any new training sets. We note that NOAA's C-CAP data are currently used for estimating surface roughness in the operational storm-surge models, and a new version is typically released every five to six years. With the launch of the L-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission in the near future, our algorithm can be used to fill the temporal gaps of the existing C-CAP-based surface roughness database and improve the accuracy of near real-time hydrodynamic modeling.

Automated summary

A new land-cover classification algorithm utilizing radar data successfully classified nine land-cover types, demonstrating good performance and validation with NOAA data. The algorithm is robust and showed promising results in surface roughness mapping in the New Orleans area, providing potential to fill temporal gaps in existing databases.