Instrument Data Metrics Evaluator for Tradespace Analysis of Earth Observing Constellations

There is currently a trend towards developing and commissioning satellite constellation missions, which has necessitated tradespace studies to design high-performance, low cost and low risk constellations. An open-source software tool called Tradespace Analysis Tool for Constellations (TAT-C) has been developed at the NASA Goddard Space Flight Center, which aims to facilitate pre-phase A mission studies by generating and optimizing the tradespace of the constellations, involving a multitude of possibly coupled parameters such as orbits, satellites, launchers, ground-stations, and instruments. The performance attributes investigated by TAT-C are instrument data metrics, coverage metrics, cost and risk. While previous research has explored optimization of the constellation satellite orbits using metrics associated with coverage (such as maximizing access duration, maximizing the number of revisits over a region), there is relatively less work on exploring the tradespace of instrument parameters and associated data metrics. Previous research has also used relatively rudimentary data-metrics such as imaged pixel resolutions, range, and angles at which observations are made. This paper describes the instrument data-metrics evaluator of TAT-C which generates more sophisticated data metrics characteristic of the instrument type. The basic concept and the architecture of the evaluator have been developed to accommodate instruments without assuming specifics about the underlying technology. In this paper, we describe the modeling of the two most common types of Earth Observing instruments, namely passive optical sensors and synthetic aperture radars (SARs). The models allow for evaluation of commonly used data-metrics such as the signal to noise ratio, noise equivalent delta temperature, ground-pixel resolutions, dynamic range, noise-equivalent sigma0, etc. The challenges in making the models generic enough for wide usage, while being able to appropriately mimic characteristics of complex real-world instruments, are described using examples such as the Landsat-8 Thermal Infrared Sensor and Operational Land Imager. Lastly, we present results from the instrument data metrics evaluator for three important use cases of mission design with passive-optical sensors and SARs. The first use case explores the tradespace of Sun-Synchronous Orbits from a perspective of data-metrics as opposed to commonly considered coverage metrics. The second use case explores the tradespace of SAR parameters and highlights quantitative trade-offs between instrument parameters influencing the performance, size, and complexity. Such tradespace analysis allows the user to appreciate and consider a fundamental constraint on the SAR antenna size depending on radar frequency. In the last use case, we explore the tradespace of pushbroom vs whiskbroom scanners, and evaluate the conditions under which their performance match.