A different situation is extant in a trade between multiple small satellites versus a larger multisensor satellite to accommodate a given sensor payload. Here the higher specific costs for small satellites and small launch vehicles will generally result in a higher cost to field the system initially (but not necessarily to maintain it) than using a larger multisensor satellite and a matching launch vehicle. This is true irrespective of sensor size or cost.
Much of the interest in small satellites stems from a desire to "do more with less" and an assumption that small satellite missions result in lower costs. A more pragmatic objective reflecting recent budget realities might be to "spend less and do as much as possible." Small satellites clearly provide a vehicle for accomplishing the latter. Here, the committee distinguishes between "small satellites'' (100 to 500 kg), "small missions" (low cost), and larger (higher cost) missions that are performed with multiple small satellites. Low-cost small satellites help enable low-cost small missions. This benefit is derived as much from the relative simplicity of many small missions (or the preexistence of mission elements) as from the size of the satellite. Small missions generally consist of only one or a few sensors and may have less stringent requirements as measured by performance, calibration, or longevity. Less complex missions require shorter development time, which goes to the heart of lower costs. Because simpler missions may also be less capable, the science and operational needs must be carefully evaluated to ensure that they are adequately addressed.
When considering small satellites to perform a larger mission involving a number of sensors, a mission architecture trade-off study is required. Alternative architectures include accommodating all sensors on a single larger platform, on multiple small satellites, or on a mixed fleet. Trade-off criteria may include programmatic flexibility, preferred measurement sampling strategies, risk tolerance, system robustness, schedule, and—of course—life cycle cost. The lowest cost architecture for such missions is not evident a priori, but depends on mission-specific parameters.
One of the emerging benefits of small satellite missions is a reduction in the "time to science." Large, complicated missions often take many years to develop, during which time both scientific understanding (and hence requirements) and technology may evolve substantially. In addition, an increasingly cost-constrained fiscal environment makes large missions especially vulnerable to budget instabilities. When a large mission can be accomplished with multiple small satellites, this approach may lead to faster science return—but this is not guaranteed. The overall schedule and cost must be examined to determine if the need for multiple satellites and launches increases or reduces the time interval to establish full capability. The potential for obtaining some (perhaps the most important) data sooner can be a compelling driver.
Small satellites offer the potential for new mission architectures, such as clusters or constellations. Such architectures may permit development of new observing strategies that alter the relative balance between observation error (as quantified by parameters such as signal-to-noise ratio) and sampling error. Employing constellations of small satellites to acquire large amounts of observational data, albeit of perhaps lower quality,1 may provide a more robust estimate of the overall statistics of the data field. This aspect of the scientific mission has not been examined in detail in this report and would have to be considered on a case-by-case basis.
Although there are differences between the operational measurement requirements of missions such as NPOESS and the science requirements of research-oriented missions such as NASA's EOS, there is clearly overlap as well. Moreover, many operational measurements are useful for research, especially for long-term climate studies. The separation of instrument variability from the often subtle, long-term variations in climate-related processes requires careful calibration and validation of the sensor and its derived data products. As sensors are replaced over time, it is essential to maintain "dynamic continuity" of the data product