GROSS PRIMARY PRODUCTIVITY ESTIMATION USING MULTI-ANGULAR MEASUREMENTS FROM SMALL SATELLITE CLUSTERS

Gross primary productivity is an excellent metric of how much forests act as carbon dioxide sinks but currently have up to 40% uncertainty in their global estimates. A large proportion of the uncertainty has been attributed to artifacts in the sun-sensor geometry of monolithic spacecrafts leading to insufficient sampling of the bi-directional reflectance of vegetation. This paper proposes to use small satellite clusters with spectrometers as a new measurement solution to improve angular sampling locally and scale up measurements globally. Initial observing system simulations with four satellites launched as secondary payloads via the ISS and operating in different imaging modes show error estimates of less than 12% when compared to dense airborne measurements, a 50% improvement to the worst case error produced by corresponding monoliths.