Crop classification of homogeneous landscapes and phenology is a common requirement to estimate land cover mapping, monitoring, and land use categories accurately. In recent missions, classification methods using medium or high spatial resolution data, which are multi-temporal with multiple frequencies, have become more attractive. A new mode of incorporating spatial and temporal dependence in a homogeneous region was tried using the Random Forest (RF) classifier for crop classification. A time-series of medium spatial resolution enhanced vegetation index (EVI) and its summary statistics obtained from Landsat 7 Enhanced Thematic Mapper Plus (Landsat 7 ETM+) were used to develop a new technique for crop type classification. Eight classes were studied: alfalfa, asparagus, avocado, cotton, grape, maize, mango, and tomato. Evaluation was based on several criteria: sensitivity to training dataset size, the number of variables, and mapping accuracy. Results showed that the training dataset size strongly affects the classifier accuracy, but if the training data increase, the rate of improvement decreases. The RF algorithm yielded overall accuracy of 81% and a Kappa statistic of 0.70, indicating high model performance. Additionally, the variable importance measures demonstrated that the mode and sum of EVI had extremely important variables for crop class separability. RF had computationally good performance. They can be enhanced by choosing an appropriate classifier for multiple statistics and time-series of Landsat imagery. It might be more economical to use no-cost imaging for crop classification using open-source software.