Currently, monitoring of agrarian policy actions usually requires ground visits to sample targeted farms, a time-consuming and very expensive procedure. To improve this, we have undertaken a study of the accuracy of five supervised classification methods (Parallelepiped, Minimum Distance, Mahalanobis Classifier Distance, Spectral Angle Mapper and Maximum Likelihood) using multispectral and pan-sharpened QuickBird imagery. We sought to verify whether remote sensing offers the ability to efficiently identify crops and agro-environmental measures in a typical agricultural Mediterranean area characterized by dry conditions. A segmentation of the satellite data was also used to evaluate pixel, object and pixel + object as minimum information units for classification. The results indicated that object- and pixel + object-based analyses clearly outperformed pixel-based analyses, yielding overall accuracies higher than 85% in most of the classifications and exhibiting the Maximum Likelihood of being the most accurate classifier. The accuracy for pan-sharpened image and object-based analysis indicated a 4% improvement in performance relative to multispectral data.