Remote sensing has long been used as a method for crop harvest monitoring and harvest classification. Harvest monitoring is necessary for the planning of and prompting of effective agricultural practices. Traditionally sugarcane harvest monitoring and classification within the realm of remote sensing is performed with the use of optical data. However, when monitoring sugarcane, the growth period of the crop requires a complete set of multi-temporal image acquisitions throughout the year. Due to the limitations associated with optical sensors, the use of all weather, daylight independent Synthetic Aperture Radar (SAR) sensors is required. The added polarimetric information associated with fully polarimetric SAR sensors result in complex datasets which are expensive to acquire. It is therefore important to assess the benefits of using a fully polarimetric dataset for sugarcane harvest monitoring as opposed to a dual polarimetric dataset. The dual polarimetric dataset which is less complex in nature and can be acquired at a fee much less than that of the fully polarimetric dataset. This thesis undertakes the task of identifying the value of fully polarimetric data for sugarcane harvest identification and classification. Two main experiments were designed in order to complete the task. The experiments make use of fully polarimetric RADARSAT-2 C-band imagery covering the southern part of Reunion Island. Experiment 1 made use of a multi temporal single feature differencing technique for sugarcane harvest identification. Polarimetric decompositions were extracted from the fully polarimetric data and used along with the inherent SAR features. The accuracy with which each SAR feature was able to predict the sugarcane harvest date for each field was assessed. The polarimetric decompositions were superior in classification accuracy to the inherent SAR features. The Van Zyl volume decomposition component achieved an accuracy of 88.33% whereas the inherent SAR backscatter featur