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Evaluation of sensor characteristics on the retrieval of vegetation surface reflectance in high-latitude ecosystems
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  • Shawn Serbin,
  • Alexey Shiklomanov,
  • Dedi Yang,
  • Philip Brodrick,
  • David Thompson,
  • Benjamin Poulter
Shawn Serbin
Brookhaven National Laboratory, Brookhaven National Laboratory

Corresponding Author:sserbin@bnl.gov

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Alexey Shiklomanov
NASA Goddard Space Flight Center, NASA Goddard Space Flight Center
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Dedi Yang
Stony Brook University, Stony Brook University
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Philip Brodrick
Jet Propulsion Laboratory, Jet Propulsion Laboratory
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David Thompson
Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology
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Benjamin Poulter
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Over the last nearly five decades, optical remote sensing has played a key role in monitoring and quantifying global change, plant diversity, and vegetation functioning across Earth’s terrestrial biomes. As a key tool for researchers, land managers, and policy makers, optical remote sensing facilitates scaling, mapping, and characterizing surface properties over large areas and through time. In addition, steady technological improvements have led to transformational changes in our ability to understand ecosystem state and change, particularly through the expansion of high spectral resolution (i.e. spectroscopic) remote sensing platforms. Point and imaging spectroscopy systems have been used across a range of scales, vegetation types, and biomes to infer plant diversity, leaf traits, and ecosystem functioning. However, despite the acknowledged utility of spectroscopic systems, data availability has been limited to smaller geographic regions given a number of technical challenges, including issues related to data volume and limited spatial coverage by previous Earth Observing (EO) missions (i.e. Hyperion). The NASA Surface Biology and Geology (SBG) mission is designed to fill this gap in ecosystem monitoring. As part of the Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) and Modeling end-to-end traceability (MEET) SBG efforts, we used field, unoccupied aerial system (UAS), and airborne imagery (from NASA’s AVIRIS-NG plafrom) to evaluate the impacts of proposed and theoretical sensor instrument properties on the retrieval of vegetation reflectance across tundra, shrub, and treeline ecosystems in Alaska. Existing observations and open-source tools are used for the simulation of surface reflectance under a range of atmospheric conditions, vegetation types, and different sensor properties. We find that retrieval uncertainty is reduced across all surface types with increasing detector signal-to-noise (SNR) but also key differences across different plant types. Results were also strongly tied to sun-sensor geometry and atmospheric state. Through this exercise we highlight key outcomes to consider for the SBG mission to optimize surface reflectance retrieval in high latitudes that will help to minimize errors in down-stream algorithms, such as functional trait retrievals.