In this study, the applicability of using C- and L-band Synthetic Aperture Radar (SAR) for estimating Leaf Area Index (LAI) was assessed for two of Canada'smajor crops—corn and soybeans.
A new LAI estimation approach was developed by coupling two existing models, the Water Cloud Model (WCM) and the Ulaby soil moisture model. Multipolarization RADARSAT-2 (C-band) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) (L-band) data, along with ground LAI measurements collected during the SMAPVEX12 field campaign were used to calibrate and validate themodel. Themodelwas calibrated for HH, VV and HV polarizations. These calibrated models were then used to estimate LAI. The root mean square error (RMSE), mean absolute error (MAE) and correlation (R) statistics were used to evaluate
the model's accuracy on an independent dataset.