EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

A software framework for optimizing the design of spaceborne hyperspectral imager architectures

Adam Erickson1, Benjamin Poulter1, David Thompson2, Gregory Okin3, Shawn Serbin4, Weile Wang5, and David Schimel2
Adam Erickson et al.
  • 1NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, Maryland, USA
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
  • 3Department of Geography, University of California, Los Angeles, California, USA
  • 4Brookhaven National Laboratory, Upton, New York, USA
  • 5NASA Ames Research Center, Moffett Field, California, USA

Quantifying the capacity, and uncertainty, of proposed spaceborne hyperspectral imagers to retrieve atmospheric and surface state information is necessary to optimize future satellite architectures for their science value. Given the vast potential joint trade-and-environment-space, modeling key ‘globally representative’ points in this n-dimensional space is a practical solution for improving computational tractability. Given guidance from policy instruments such as the NASA Decadal Survey and the recommended Designated Target Observables, or DOs, the downselect process can be viewed as a constrained multi-objective optimization. The need to simulate imager architecture performance to achieve downselect goals has motivated the development of new mathematical models for estimating radiometric and retrieval uncertainties provided conditions analogous to real-world environments. The goals can be met with recent advances that integrate mature atmospheric inversion approaches such as Optimal Estimation (OE) that includes joint atmospheric-surface state estimation (Thompson et al. 2018) and the EnMAP end-to-end simulation tool, EeteS (Segl et al. 2012), which utilizes OE for inversions. While surface-reflectance and retrieval simulation models are normally run in isolation on local computing environments, we extend tools to enable uncertainty quantification into new representative environments and thereby increase robustness of the downselect process by providing an advanced simulation model to the broader hyperspectral imaging community in software-as-a-service (SaaS). Here, we describe and demonstrate our instrument modeling web service and corresponding hyperspectral traceability analysis (HyperTrace) library for Python. The modeling service and underlying HyperTrace OE library are deployed on the NASA DISCOVER high-performance computing (HPC) infrastructure. An intermediate HTTP server communicates between FTP and HTTP servers, providing persistent archival of model inputs and outputs for subsequent meta-analyses. To facilitate enhanced community participation, users may simply transfer a folder containing ENVI format hyperspectral imagery and a corresponding JSON metadata file to the FTP server, from which it is pulled to a NASA DISCOVER server for processing, with statistical, graphical, and ENVI-formatted results subsequently returned to the FTP server where it is available for users to download. This activity provides an expanded capability for estimating the various science values of architectures under consideration for NASA’s Surface Biology and Geology Designated Observable.

How to cite: Erickson, A., Poulter, B., Thompson, D., Okin, G., Serbin, S., Wang, W., and Schimel, D.: A software framework for optimizing the design of spaceborne hyperspectral imager architectures, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19665,, 2020