Remote sensing has resulted in repositories of data that grow at a pace much faster than can be readily analyzed. One of the obstacles in dealing with remotely sensed data and others is the variable quality of the data. Instrument failures can result in entire missing observation cycles, while cloud cover frequently results in missing or distorted values. We investigated the use of several methods that automatically deal with corruptions in the data. These include robust measures which avoid overfitting, filtering which discards the corrupted instances, and polishing by which the corrupted elements are fitted with more appropriate values. We applied such methods to a data set of vegetation indices and land cover type assembled from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) data collection.