Next generation commercial space-borne systems

Commercial satellite remote sensing is a rapidly growing market, some projections indicating revenues from the sale of data and GIS products totaling over $12B by 2003. Though commercial remote sensing may be promising, there have been some limiters to the growth of the market over the past few years, including the costs per image, the not-so-timely acquisition of data and delivery to the customer, and others. One current inhibitor to the growth of the market is the cost of commercial images. Most commercially provided data range from $1,500 to $4,000 per scene, depending on the resolution and size of the image, with more integrated GIS products costing as high as $10,000 for one image. A second inhibitor is the difficulty in translating spectral signatures into useful and accurate information without weeks of ground research. Another potential inhibitor is the timeliness in acquiring satellite images. At present, it takes anywhere from two days to well over four weeks before customers receive imagery from vendors. This time lapse can be a problem for users of imagery with rigid time constraints (e.g., agricultural or media interests). Due to these inhibitors, the success of a remote sensing system depends not just on the spectrum and resolution of the data, but also on other factors important to customers such as image cost, speedy access to data, service reliability, and other figures of merit relevant to a customers needs. ELOP has examined how these various figures of merit become beneficial to different applications as they vary in performance. Applications such as Agriculture, Forestry, Geology, Coastal & Water and many other, were compared with variations in spatial resolution, spectral resolution, spectral range, revisit rate, data delivery speed, and others. A condensed version for varying figures of merit evaluated with some expected applications will be presented and the various parameters analyzed for variable revisit rate with a conclusive approach for the optimal solution.