Remote sensing instruments in Earth orbit provide a rich source of information about current agricultural conditions. Observed over time, patterns emerge that can assist in the prediction of future conditions, such as the yield expected for a given crop at the end of the growing season. It is suspected that these predictions can be made more accurate by incorporting other sources of information, such as weather conditions from ground stations, soil properties, etc. The tools required to access and combine large amounts of data from multiple sources, at different spatial resolutions, are not readily available. The HARVIST (Hetereogeneous Agricultural Research Via Interactive, Scalable Technology) project seeks to address this lack by demonstrating the technology required to perform largescale studies of the interactions between agriculture and climate. Previously, we have developed successful software tools for multispectral pixel classification using support vector machines, and multispectral image pixel clustering using constrained kmeans, which we are leveraging in this effort. To date, we have developed a graphical interface that allows users to interactively run automatic classification and clustering algorithms on multispectral remote-sensing data. We have incorporated technical advances that exploit the spatial nature of the data to greatly increase classification efficiency. Our next goal is to incorporate a predictive component to support applications such as crop yield prediction.