Value-centric approaches for Earth Observation System (EOS) design and use have long been advocated in principle, but operationally remain rare in practice. Here, an integrative framework for quantifying value is presented that connects architecture and design, data processing, and data use. Examples in constellation design, calibration for data quality, and decision applications from remote sensing data demonstrate how the approach can be applied. Additionally, closed form analytical models are formulated to show how system design, operation, and data use variables connect with value metrics relevant for different stakeholders. The models presented here offer a basis for informing and negotiating choices by designers, operators, and users of emerging EOS.

Since the first spacecraft were deployed for earth observation (EO) in the early 1960s, technological change in EO systems has significantly improved remote sensing capabilities. To fully understand the pace of past change and develop empirical baselines for future projections, it is important to quantitatively characterize performance of key instruments used in EO missions. This study presents trends in performance in EO instruments using empirical data from spacecraft and instruments deployed from 1959-2022. Parameterized models are derived to quantitatively determine temporal progression in key figures-of-merit. Pareto frontiers of mass and resolution of sounders, SAR, and radiometers are also determined. The results show a consistent shift in Pareto frontiers of radiometers across successive 15-year periods spanning 1972 to 2025.

There are few frameworks to quantitatively determine value of remote sensing data to inform decisions related to sustainable development. In most cases, value remains vaguely defined and qualitatively discussed. Here, we develop an approach in which decisions, that can be supported by remote sensing data, are systematically structured, and monetized value of possible outcomes are used as a basis for quantifying value of remote sensing. It is shown that a canonical set of decision structures and application of a Bayesian approach leads to a set of analytic expressions that can be used to build insights of the decision problem and to identify driving factors that affect value of remote sensing. The method is demonstrated for water quality monitoring, forest management, and solar power infrastructure inspection. The specific case studied for water quality showed that remote sensing provides value if likelihood of a HAB is 30% or more (even if data quality is not high). In forest management (with specific restoration costs, mangrove loss costs etc.), remote sensing offers value beyond a threshold of cost of field surveys. For solar farm inspection, remote sensing is most valuable for low anomaly rates and high panel replacement costs.

Earth observation (EO) activities from space have rapidly advanced with a five-fold increase in average annual launches during 2011–2021 as compared to the previous decade. With increasing interest in public and private sectors for charting out strategies and roadmaps for future missions, it is important to track technology trends. Here, a quantitative assessment of key advancement in EO capabilities is presented along with projections for 2030. The analysis shows that, if the trends continue as-is, the number of countries owning (and engaging with) EO spacecraft will expand to 64 by 2030 which will be a 42% increase from 2020 (of 45 countries). Additionally, minimum spatial resolution of imagers on scientific and commercial EO spacecraft will fall below 10cm, and below 30cm for Synthetic Aperture Radars.

High resolution, high frequency, multi-spectral remotely-sensed Earth Observation (EO) data is increasingly available. Challenges of ensuring radiometric data quality and consistency of data products, however, have not been widely recognized. As new products and applications rapidly emerge, and decisions linked to public safety, well-being, and environmental assessments are at stake, the issue of EO data quality and quantification of errors and their impacts has gained increasing salience. This paper presents a generalizable decision-theoretic framework for quantifying the value of data quality (partly through improved calibration), and demonstrates its application for a water quality monitoring case. Results from the particular case of freshwater monitoring show that after 30% or more probability of occurrence of a harmful algal bloom, remote sensing is useful to have regardless of the data quality level. However, factors such as cost of remote sensing data (in this case, comparing internal processing vs. external processing of tasked satellite imagery) will affect the threshold at which remote sensing value is positive regardless of data quality level.

Due to recently growing adoption, residential photovoltaic (PV) installations are becoming a key contribution to renewable energy production. In order to efficiently track the inherently decentralized deployment of these PV systems, this study leverages widely available high-resolution satellite imagery, along with the demonstrated ability of convolutional neural networks (CNNs) in image classification tasks. In particular, this effort presents development of a custom-trained CNN that operates on images of residential areas received as part of a larger image processing pipeline. This custom-trained CNN is shown to achieve comparable accuracy to the state-of-the-art achieved via transfer learning, but with reduced computational burden and required image resolution. Using imagery from two cities in California, this approach achieves PV classification with a precision of 91.9% and recall of 92.4%. This accuracy is sufficient to inform actionable insights from satellite imagery regarding the political, social, and economic factors affecting PV deployment.

Earth observation systems are providing a growing amount of high resolution and multi-spectral data including agricultural crop yield predictions, local weather forecasts, wild fire tracking, and water quality monitoring. With the advent of multiple elements and sophisticated processing and modeling, there are also increasing avenues for introduction of errors. It is important to quantify and characterize these errors in the remotely sensed data as it gets increasingly used to inform vital decisions. Here, a data value-chain approach is presented for conceptualizing introduction and propagation of errors in remote sensing data acquisition, processing, and decisions. A detailed case of calibration errors and their propagation to higher level data products is then discussed. An NDVI analysis is used as an example, and it is shown (for the selected region), that a 3% error in reflectance ratio of red and NIR bands translates into a difference of 60 % for category 5 NDVI classification (representing high vegetation). This amplification of errors along the data value chain is caused by nonlinear operators such as the application of quotients to differences of reflectance values as well as the use of classifiers based on fixed color thresholding.

The Inclusive Wealth Index has been proposed as a suitable measurement of sustainable growth and development of nations. This index consists of natural, produced, and human capital. A systematic and verifiable assessment of natural capital across countries has proven to be challenging. This paper proposes that natural capital be evaluated in a consistent manner through the use of earth observation satellites. 96 and 110 instruments were identified to observe forest and agricultural resources, respectively, thus a strong capability exists for these natural capital categories. However, current remote sensing capabilities can only go so far. Only 20 instruments were identified that can potentially be used for fish stock assessment. Although 72 instruments were identified to have mineral observation capabilities, data post processing is needed to accurately assess mineral stocks. 35 instruments were identified to assist in fossil fuel observation, but given that supply data exists for fossil fuels, this capability gap is not considered critical. By examining these capability gaps, scientists can direct their attention to payloads that will enable remote sensing technologies to assist in the measurement of the Inclusive Wealth Index.

New earth observation missions are being implemented and conceptualized with architectures employing multiple spacecraft and novel observing strategies that combine distributed, multi-platform systems. These new types of missions are enabling earth observation with multi-angular, multi-spectral data acquisition at high resolution and high revisit frequencies. The architectural choices have grown exponentially in such distributed systems, and there is a need for quantitative measures, that go beyond cost estimation, and assess value (or scientific return) over a mission's operational life-cycle. Here, we formulate a novel metric, Net Architecture Value (NAV), that can be used in early stage conceptual mission design and architecture trade studies. We propose that useful data of adequate quality, obtained over regions of interest, acquired by a system over its lifetime can be used as a proxy measure of value. We demonstrate the application of this approach for a distributed space mission.

The availability of high-resolution remote sensing data has created opportunities to gain new information about urban infrastructure. Here, high resolution ortho-imagery with three bands and maximum likelihood classification of images is used to obtain information about roads and pavement (i.e. transportation related) infrastructure. The cities of Phoenix, Arizona (for the years 2004, 2006, 2008, 2012) and Seattle, Washington (for 2002, 2005, 2009) were analyzed. We discuss the implications and utility of this (and other infrastructure) data for constructing metrics for urban growth and environmental sustainability.

Equity and fairness are important socially relevant issues that are rarely considered or quantitatively studied in engineering systems analysis and technical design. Sociotechnical systems that provide important services, such as water supply, energy supply, transportation services, and telecommunication services, are largely designed using measures of technical performance and cost. Given the vital links of these systems to social welfare, it is important that techno-economic criteria (for guiding design) are augmented with socially important considerations such as equity and fairness. Here, an initial conceptual framework and some quantitative measures of equity are presented. An irrigation network is discussed as an application case. Statistical variation of its normalized performance (ratios of actual versus planned service) across spatially separated parts of the system, and spatial variation of reliability are used as measures of equity. The irrigation system exhibited median reliability of 84% over a decade, however, system equity had little to no improvement. The influence of a network's architecture on evolution of degrees of fairness is discussed, and simulations-based analyses show relationship between network structure and variation of fairness norms. The cases show that inclusion of equity measures in design and operations optimization can be formalized, and equity in resource allocation resulting from emergent behavior can be impacted via promoting and steering collective fairness norms.

Distributed Spacecraft Missions (DSMs) have largely garnered interest due to lower cost and new technical capabilities. However, another important attribute in DSMs is the potential of graceful degradation where a failure of one or more spacecraft (or instruments) in the DSM can reduce total performance, but the remaining functional spacecraft can maintain a limited science return. The potential for retaining some mission value in the event of partial failures leads to enhanced lifetime value. Here, an approach is developed to analyze graceful degradation and performance of DSM used for Earth observation. The system performance is defined through metrics of coverage and average revisit time of points of interest. Graceful degradation is quantified through total change and rate of change in performance metrics with increasing sub-system failures over the lifetime of a mission. An Expected Performance measure is formulated for conducting comparison of DSM architectures for early stage conceptual mission design and trade studies.