On-Demand Geospatial Spectroscopy Processing Environment on the Cloud (GeoSPEC)

The 2017-2027 Decadal Survey prioritizes a spaceborne imaging spectrometer to advance the study of Surface Biology and Geology (SBG) globally. It would generate large volumes (~20 TB/day) of high dimensional (>224 bands) data, with a wide range of measurement objectives spanning vegetation, hydrology, geology, and aquatic studies. Many of the observables identified for SBG have been demonstrated using airborne imaging spectroscopy, but there is a potentially very large set of products that may be asked of a mission like SBG, and the algorithms, L1-to-L3+ processing flows, and intermediate products may vary considerably by application. Further, multiple well-vetted algorithms in the processing chain may exist for the same observables (and are ever-improving), and users may wish to tune the retrievals based on parameterizations particular to an application or using constraints from field measurements to improve algorithm performance in localized settings. Because of the demand for products from imaging spectroscopy from a wide range of users as well as the dynamic nature of the algorithms, our basic premise is that in the future science data systems for imaging spectrometer data will differ dramatically from current approaches. We propose an On-Demand Geospatial Spectroscopy Processing Environment on the Cloud (GeoSPEC) as a necessary information technology innovation required to meet the needs of both users and distributors of the products of imaging spectroscopy in the forthcoming era of widespread and possibly global hyperspectral data availability. We will develop the technology using terrestrial vegetation use cases for mapping vegetation foliar traits and fractional cover. We will provide users with options for new atmospheric correction protocols, other corrections (such as BRDF and topography), and options for algorithm selection. GeoSPEC will also include off- and on-ramps in the processing workflow for users to implement their own code or commercial programs on their own systems, thus facilitating flexibility in the application of vetted algorithms for product distribution. The proposed project will leverage existing NASA-funded technologies:EcoSIS for spectral libraries, EcoSML for spectral models, the SWOT/NISAR cloud-based science data system (HySDS), as well as data analysis services (Apache SDAP), interactive visualization and analytics (Common Mapping Client, CMC) and open-source python packages HyTools for hyperspectral processing and ISOFIT for atmospheric correction.The platform will be developed using Level 1 calibrated radiance from two imaging spectrometers with large and diverse data records: NASA AVIRIS-Classic and AVIRIS-NG. Ultimately, development of flexible, on-demand cloud-based processing of hyperspectral imagery- which does not currently exist -will reduce barriers to usage of complex imaging spectroscopy data and its products, such as NASA's vast airborne AVIRIS archives at present, and data from EMIT and HISUI in the near future, and eventually SBG. Finally, there is a large potential user base for the L3+ outputs of SBG-like measurements, which for our use case includes ecologists and ecosystem scientists interested in using the high-level products to predict processes related to carbon uptake and nutrient processing, or to characterize biodiversity based on spatial variation in functional traits. These potential users represent a new constituency for imaging spectroscopy data products who previously would not have had access to such measurements across broad spatial and temporal extents. GeoSPEC is a necessary Analytic Center Framework development both for a scientific community wishing to use the high-level products of imaging spectroscopy (we have three letters of endorsement representing Map of Life, distribution modeling, and evolutionary biology) as well as two mission communities (letters from SBG mission study leadership and NEON). GeoSPEC enters at TRL 3 and exits at TRL 5.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

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