SBG User Needs and Valuation Study Final Report September, 2020

Culver, Tom; Rydeen, Amy E.; Dix, Molly; Camello, Meghan; Gallaher, Michael; Lapidus, Daniel; Brown, Elizabeth; Lee, Christine; Luvall, Jeffrey C.; Stavros, E. Natasha; Uz, Stephanie Schollaert; Yuen, Karen Wing Yee; Glenn, Nancy F.

doi:10.5281/zenodo.6347764

Cited by 3 publications

(11 citation statements)

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“…A survey of user needs and the value of information (Culver et al, 2020) is also being used to inform future community and partner engagement, where value of information is estimated using industry size and evaluating value of notional SBG products to support decision contexts (full reports are available: sbg.jpl.nasa.gov/ doc_links/user-needs-and-valuation-study, https://sbg.jpl.nasa.gov/doc_links/2nd-sbg-user-needs-and-valuationstudy). The synthesis of applications community needs and perspectives has also been conducted in concert with science needs, as a cohesive unit, to identify other key measurement needs for an SBG architecture.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Low latency is largely considered beneficial (Culver et al, 2020;Davies et al, 2017;Moyle et al, 2016;NASA, 2019b) in that data and information needs to be delivered in relevant timescales for decision-makers to find them useful. Culver et al, 2020 found that for agriculture, water quality, and fire-related applications, between 83% and 94% of survey respondents in an SBG applications study indicated that 24 hr latency is optimum to confer benefit. Additional detail is provided for each of the sub-areas, that is, for harmful algal bloom monitoring, decision-makers identify a latency range of 24-48 hr with a temporal revisit of 1-3 days to support their issuance of effective warnings (Culver et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…While the AppsWG efforts were able to secure a low latency target for VSWIR and TIR architectures, another applications need is more frequent temporal revisit and was not possible under the cost/budget boundaries put forward in the Decadal Survey. For many applications, a combination of frequent temporal revisit and low latency would add tremendous value to natural hazards and disaster response applications (Culver et al, 2020). Synergy through international partnerships are in progress between SBG and ESA, Italian Space Agency, and Centre National D'Etudes Spatiales.…”

Section: Sbg Applications and Nasa Mission Applicationsmentioning

confidence: 99%

“…Third, the AppsWG analyzed trades across disciplines and application domains for different capabilities and needs to assess impacts of different mission architecture design decisions. Finally, the Apps WG assessed the applications value as intrinsically important context that can help improve partnerships within the external community as well as internationally for mission planning-more detail on this effort can be found in (Culver et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Systematic Integration of Applications into the Surface Biology and Geology (SBG) Earth Mission Architecture Study

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This is the first instance of integration from the perspective of science applications into mission design, and at the architecture study phase. Mission architectures have traditionally been underpinned by science needs only. This effort demonstrates that not only does the applications community confer unique technical and measurement needs that are feasible to integrate into architecture considerations, but also that the overall process for defining mission requirements is enhanced through science and applications synergy and cohesion. Furthermore, applications feed into other aspects of the mission design process at early stages, such as bolstering discussions with international partners and reinforcing the benefits and need of harmonized data products and mission coordination. LEE ET AL.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Sbg Applications and Nasa Mission Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Maximizing Societal Benefit Across Multiple Hyperspectral Earth Observation Missions: A User Needs Approach

Schiavon,

Taramelli,

Tornato

et al. 2023

JGR Biogeosciences

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Imaging spectroscopy is a powerful tool used to support diverse Earth science and applications objectives, ranging from understanding and mitigating widespread impacts of climate change to management of water at farm‐scale. Community studies, such as those deployed by NASA's Surface Biology and Geology and ESA's Copernicus Hyperspectral Imaging Mission for the Environment, have offered new and tangible insights into user needs that are then incorporated into overall mission planning and design. These technologies and tools will be key to develop and consolidate downstream services for users and resource management, given the current pressures on the environment posed by climate change and population growth. This process has highlighted the degree to which planned mission capabilities are responsive to community needs. In this study, we analyze user requirements belonging to the Italian Copernicus User Forum and to the user pool of NASA's Surface Biology and Geology community for the synergic use of hyperspectral imaging technology, providing a reference for the development of earth observation services and the consolidation of existing ones. In addition, potential cross‐mission coordination is analyzed to highlight key benefits—(a) addressing shared community needs around products requiring more frequent temporal revisit and (b) shared resources and community expertise around algorithm development. This paper discusses the critical role of early engagement with users to establish a community of practice ready to work with high spatial resolution imaging spectroscopy data sets. The main outcome is a guide for the synergetic use of hyperspectral mission and data together with the identification of the main gaps between user needs and satellite capabilities influencing the development of key national and trans‐national downstream services.

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Hypoxia Forecasting for Chesapeake Bay Using Artificial Intelligence

Zheng,

Schollaert Uz,

St-Laurent

et al. 2024

Artificial Intelligence for the Earth Systems

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Seasonal hypoxia is a recurring threat to ecosystems and fisheries in the Chesapeake Bay. Hypoxia forecasting based on coupled hydrodynamic and biogeochemical models has proven useful for many stakeholders, as these models excel in accounting for the effects of physical forcing on oxygen supply, but may fall short in replicating the more complex biogeochemical processes that govern oxygen consumption. Satellite-derived reflectances could be used to indicate the presence of surface organic matter over the Bay. However, teasing apart the contribution of atmospheric and aquatic constituents from the signal received by the satellite is not straightforward. As a result, it is difficult to derive surface concentrations of organic matter from satellite data in a robust fashion. A potential solution to this complexity is to use deep learning to build end-to-end applications that do not require precise accounting of the satellite signal from atmosphere or water, phytoplankton blooms, or sediment plumes. By training a deep neural network with data from a vast suite of variables that could potentially affect oxygen in the water column, improvement of short-term (daily) hypoxia forecast may be possible. Here we predict oxygen concentrations using inputs that account for both physical and biogeochemical factors. The physical inputs include wind velocity reanalysis information, together with 3D outputs from an estuarine hydrodynamic model, including current velocity, water temperature, and salinity. Satellite-derived spectral reflectance data are used as a surrogate for the biogeochemical factors. These input fields are time series of weekly statistics calculated from daily information, starting 8 weeks before each oxygen observation was collected. To accommodate this input data structure, we adopted a model architecture of long short-term memory networks with 8 time steps. At each time step, a set of convolutional neural networks are used to extract information from the inputs. Ablation and cross validation tests suggest that among all input features, the strongest predictor is the 3D temperature field, with which the new model can outperform the state-of-the-art by ∼20% in terms of median absolute error. Our approach represents a novel application of deep learning to address a complex water management challenge.

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SBG User Needs and Valuation Study Final Report September, 2020

Cited by 3 publications

References 0 publications

Systematic Integration of Applications into the Surface Biology and Geology (SBG) Earth Mission Architecture Study

Systematic Integration of Applications into the Surface Biology and Geology (SBG) Earth Mission Architecture Study

Maximizing Societal Benefit Across Multiple Hyperspectral Earth Observation Missions: A User Needs Approach

Hypoxia Forecasting for Chesapeake Bay Using Artificial Intelligence

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