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Hadoop is a distributed filesystem and MapReduce framework originally developed for search applications by Google and subsequently adopted by the Apache foundation as an open source system. We propose that this parallel computing framework is well suited for a variety of service oriented science applications and, in particular, for satellite data processing of remote sensing systems. We show that, by installing Hadoop on a cluster of IBM PowerPC blade clusters, we can efficiently process multiyear remote sensing data, expect to see speed performance improvements over conventional multi-processor methodologies, and have more memory efficient implementation allowing for finer grid resolutions. Moreover, these improvements can be met without significant changes in coding structure.