Earth observation systems, consisting of in-space and air borne platforms and sensors, are providing a growing number of high resolution spatial and temporal services including agricultural crop yield predictions, local weather forecasts, and traffic management. As the complexity of these systems increases with multi-platform elements and sophisticated processing and modeling, there are also increasing avenues for introduction of errors. It is important to characterize and quantify the uncertainties and errors. Here, it shown that a value-chain approach can be used for conceptualizing errors and modeling uncertainties relevant for final decisions. This approach can then be applied for improving system value assessments and obtaining an ‘error-adjusted’ value of the remote sensing system. The error-adjusted value can be used in optimization or trade-studies for system design. This value system is then applied, as an example, to the FLARE real world calibration/validation system to look at potential Return on Investment (ROI) of better calibration to satellite image prices and market penetration
