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Generating Reliable Higher Level Hyperspectral Data Products: A Framework for Driving Upstream Processing Decisions

With many new hyperspectral imaging missions coming online, there is a growing need to apply existing science to generate reliable high level data products for end-users. Here we propose using a scenario analysis framework for evaluating the impact of upstream image acquisition processing decisions...

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Bibliographic Details
Main Authors: Roth, Keely L., Houborg, Rasmus, Shivers, Sarah, Haag, Justin, Giuliano, Paul
Format: Conference Proceeding
Language:English
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Summary:With many new hyperspectral imaging missions coming online, there is a growing need to apply existing science to generate reliable high level data products for end-users. Here we propose using a scenario analysis framework for evaluating the impact of upstream image acquisition processing decisions on the quality of derived hyperspectral data products. We demonstrate our framework on an example use case comparing a spectral index-based modeling approach against a partial least squares regression approach for estimating canopy chlorophyll content under both typical and edge image acquisition conditions. The results highlight the potential of scenario analysis as a tool for supporting both coarse and fine-scale processing decisions in an operational hyperspectral data pipeline.
ISSN:2153-7003
DOI:10.1109/IGARSS46834.2022.9883061