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's major 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 the model. The model was 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