With changes in the climate system, obtaining information on crop growth in order to derive early estimates of yields is necessary. Remote sensing allows collecting frequent crop development indications such as the Leaf Area Index (LAI). Although the interaction between linear polarized microwaves and agricultural targets has been studied widely, the implementation of a method that takes benefits of all linear polarizations to optimize the LAI estimation is necessary. Simultaneous to the acquisition of 11 RADARSAT-2 in 2013, LAI over winter wheat fields was measured. The Water Cloud Model (WCM) was implemented to derive LAI values from each polarization. A combination of the retrieved LAI and their associated errors for each polarization was then computed to improve LAI estimation. To investigate the potentiality of applying the model to the agricultural region, a crop classification model to identify winter wheat field was developed using multiyear parcel's crop sequence. This model automatically extracts a training sample. The classification yielded an accuracy of 89%. Index Terms— LAI