Abstract
This paper proposes an algorithm to find the smallest satellite constellation satisfying a given set of Earth observation requirements. This methodology is exemplified with the Satellites Observing Lakes and Vegetation Environments (SOLVE) study, which aims at deploying a fleet of small satellites carrying miniaturized hyperspectral spectrometers. A key requirement of this mission is a high temporal resolution through which the ground target can be observed several times a day. Hourly observations are required in this mission in order to capture diurnal changes in water quality and vegetation environments. Given sensor specifications and observation requirements, the proposed algorithm determines orbital parameters of an optimal constellation design via a semi-analytical approach. This approach reveals trade-offs amongst performance metrics and deployment cost, providing better physical intuition for decision-making compared to stochastic optimization.
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