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The purpose of this work is to assess the sugarcane yield variation in regional scale through NDVI images from a low resolution spatial satellite. We have used Principal Component Analysis (PCA) and Cluster Analysis to correlate sugarcane cultivated land with multitemporal NDVI images also verifying the influence of climate conditions to them. According to both techniques (PCA and clustering), clusters for different set of variables are distinct only when cultivated land was included in the dataset. On the contrary, climate variables determine the clustering formation. Exploring multitemporal images from high resolution satellites through data mining techniques, such as cluster analysis, is a valuable way to improve crops monitoring specially at a time when it becomes increasingly important to understand the impact of climate change on agriculture.