EAGER: Collaborative Research: Autonomous retrieval of impurity-laden Arctic sea ice and hyperspectral surface properties through innovative robotics

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Snow and glacier ice are often laden with light absorbing particles. By comparison to land ice, sea ice surface biogeochemistry has been largely ignored. One reason is due to the relative inaccessibility of impurity-laden sea ice. The presence of these particles, such as black carbon and dust, lowers the surface albedo, resulting in increased solar absorption that quickly thins the impurity-laden ice. Deposition of black carbon onto Arctic sea ice is likely growing due to the increasing frequency and severity of fires in the Arctic. Additionally, thawing permafrost may be increasing dust deposition onto nearby sea ice. The increased absorption of solar radiation by the light absorbing particles also increases meltwater generation and melt-pond formation. As a result, physical access to sampling impurities on sea ice is limited due to unsafe physical ice conditions. This award supports development of an integrated unmanned aerial system (UAS) capable of taking off from a ship-based platform, imaging the surface of the sea ice to locate ideal sampling locations, and autonomously retrieving snow samples that would otherwise be unreachable. Students are engaged throughout this project via the Colorado Space Grant Consortium. The scale of impurity-laden sea ice is currently unknown and is not accounted for in global climate models. Sediment-laden ice and other impurities have a profound impact on sea ice biota and can delay or inhibit the timing of the spring phytoplankton and ice algae bloom, thereby impacting the entire marine ecosystem. Given that these localized albedo responses and feedbacks lead to regional impacts on the Arctic surface energy balance, climate, and ocean primary productivity, it will have repercussions for the global climate as well. The impurity-ice albedo feedbacks also thin the sea ice, limiting physical sampling due to unsafe ice conditions. Therefore, field sampling of snow and ice surface biogeochemistry will be transformed by developing a robotic sampling device affixed to an UAS, together with a hyperspectral remote sensing imager. The rapid collection of useful spectral information will help document the impact impurity-laden ice has on sea ice albedo and the robotic arm will increase the spatial and temporal frequency of ground-based observations from normally inaccessible floes. This new integrated UAS will be tested in the Pacific Northwest and on sea ice in the Arctic Ocean and will have broad applications for use in other regions of the cryosphere, such as heavily crevassed glaciers and ice sheets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.