The current generation of satellite-based sensors produces a wealth of observations. The observations are recorded in different regions of electromagnetic spectrum, such as visual, infra-red, and microwave bands. The observations by themselves provide a snapshot of an area but a more interesting problem, from mining the observations for ecological or agricultural research, is to be able to correlate observations from different time instances. However, the sheer volume of data makes such correlation a daunting task. The task may be simplified in part by correlating geographical coordinates to observation but that may lead to omission of similar conditions in different regions. This paper reports on our work on an image search engine that can efficiently extract matching image segments from a database of satellite images. This engine is based on an adaptation of RISE (Robust Image Search Engine) that has been used successfully in querying large databases of images. Our goal in the current work, in addition to matching different image segments, is to develop an interface that supports hybrid query mechanisms including the ones based on text, geographic, and content.