Identifying landuse management practices is important for detecting landuse change and impacts on the surrounding landscape. The Ontario Ministry of Agriculture and Rural A airs has established a database product called the Agricultural Resource Inventory (AgRI), which is used for the storage and analysis of agricultural land management practices. This thesis explores the opportunity to populate the AgRI. A comparison of two supervised classi fications using optical satellite imagery with multiple single-date classifi cations and a subsequent multi-date, multi-sensor classi fication were used to gauge the best image timing for crop classi fication. In this study optical satellite images (Landsat-5 and SPOT-4/5) were inputted into a decision tree classifi er and Maximum Likelihood Classifi er (MLC) where the decision tree performed better than the MLC in overall and class accuracies. Classifi cation experienced complications from visual diff erences in vegetation. The multi-date classifi cation performed had an accuracy of 66.52%. The lack of imagery available at crop ripening stages reduced the accuracies greatly.