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Satellite remote sensing data has many advantages compared with other data sources, such as field methods and aerial photography, for land cover classification. In particular,it is useful in evaluating temporal and spatial effects. In addition, remote sensing can offer a cost-effective means of providing more frequent crop/land cover data for short and long term planning. This research studied the application of Landsat 5 Thematic Mapper (TM) data to build crop type maps for the Salt River Basin in northeastern Missouri over a two-year period (2005 and 2006) for use in evaluating land-use management. MODIS Normalized Difference Vegetation Indices (NDVI) were used to generate crop phenological behaviors. This method analyzed both the unsupervised class from Landsat images and NDVI values derived from Landsat based on the MODIS NDVI threshold selection. Decision tree analysis, a combination of pixel and object based methods, was applied. In addition, statistical analysis on NDVI was used to build a regression model to predict Landsat NDVI from MODIS NDVI as an aid to define rules in decision tree analysis. Ground truth data in the Goodwater Creek sub-basin and Greenley, MO reference area were used to assess the accuracy of the procedure. Results from this method showed good agreement with limited cloud-free Landsat images during the growing season. The method gave an overall accuracy for the Salt River Basin of 94.1% in 2006 in classifying forest, pasture and crops, including soybean, corn, grain sorghum, winter wheat and double crop, when there was a lack of cloud-free images during the growing season. The overall accuracy results in 2005 for the two reference sites, the Goodwater Creek and the Greenley reference area, were 93.0% and 92.5%, respectively. These results indicate that decision tree can be a useful method for crop separation if a single cloud-free Landsat 5 scene can be taken during the optimum crop discrimination period for a given area with the analysis of weather constraints and crop NDVI phenological behaviors if the decision tree is available from previous training.