A new unsupervised algorithm, called CLimate and rEmote sensing Association patterns Miner, for mining association patterns on heterogeneous time series from climate and remote sensing data, integrated in a remote sensing information system is developed to improve the monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing images, an image pre-processing module, a time series extraction module, and time series mining methods. The time series mining method transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in other series within a temporal sliding window. The validation process was achieved with agro climatic data and NOAA-AVHRR images of sugar cane fields. Rules generated by the new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden of dealing with many data charts. This new method can be used by agro meteorologists to mine and discover knowledge from their long time series of past and forecasting data, being a valuable tool to support their decision-making process..