Integrated Analysis of Robustness and Flexibility in Management of Earth Observation Portfolios

Portfolios of Earth observing (EO) missions are complex systems of systems, consisting of multiple missions that provide unique observing capabilities, while also providing additional collective value. To observe large-scale climate trends and changes over time, it is key to maintain the ability to provide sustained data collection from these EO satellites for several years and sometimes decades. However, the complexity associated with formulating and developing EO missions, compounded with operational and funding uncertainty, is such that gaps may occur between missions, impacting the ability to provide these sustained measurements. These EO missions provide data for a wide range of geophysical parameters, which, collectively, inform our understanding of the Earth system. Planning and executing on a balanced portfolio is important to sustain these collective capabilities. Strategic decision-making at the portfolio level is, however, a complex task because missions oftentimes address multiple parameters and interconnections need to be taken into consideration. The likelihood of data gaps is often assessed at the parameter level to inform these strategic decisions. However, uncertain events and decisions can impact multiple parameters at once, adding to decision complexity. The authors propose to model interconnections between missions of a notional Earth Science portfolio to analyze system changeability and better understand the impact of these events and decisions at the portfolio level. Changeability is decomposed into four factors: robustness, an insensitivity to environmental change; flexibility, the ease of changing the system; agility, the speed of applying changes to the system; and adaptability, the ability of a system to adapt on its own. The analysis documented in this paper will focus on the first two factors of robustness and flexibility. In the context of the NASA Earth Science portfolio, robustness is considered to be the portfolio's ability to provide sustained measurements despite future uncertainties. Flexibility is considered to be the availability of decision alternatives that contribute to parameter coverage at little detriment to the rest of the portfolio. These aspects of changeability are evaluated through joint consideration of mission-parameter interconnectivity and redundancy in parameter coverage. Domain mapping matrix (DMM) methods-which involve mapping elements across domains of a system to observe dependencies-are applied to model interconnections between EO missions and geophysical parameters of interest over a period of 20 years. The application of DMM methods allows for the identification of parameter interconnectivity. Analysis of this interconnectivity over time can identify when gap mitigation decisions or uncertain events for one parameter could be detrimental to other parameters. Across a portfolio, this analysis can help inform the identification of threats to multi-parameter clusters and opportunities to minimize gaps in others. This paper describes an approach for modeling portfolio interconnectivity, proposes how portfolio threats and opportunities can be defined and identified through changeability analysis, and presents this analysis for a notional EO portfolio. The proposed approach supports strategic management of EO portfolios. Identifying threats and opportunities allows decision-makers to focus effort on analyzing changes that may have significant impact, rather than considering the full set of changes that could affect the portfolio.