Airborne observations of arctic-boreal water surface elevations from AirSWOT Ka-Band InSAR and LVIS LiDAR

• Main Authors: Fayne, J.V., Smith, L.C., Pitcher, L.H., Kyzivat, E.D., Cooley, S.W., Cooper, M.G., Denbina, M.W., Chen, A.C., Chen, C.W., Pavelsky, T.M.
• Other Authors: College of Arts and Sciences, Department of Geological Sciences
• Format: Article in Journal/Newspaper
• Language: English
• Published: IOP Publishing Ltd 2020
• Subjects: Arctic lakes; LVIS; InSAR; ABoVE; LiDAR; AirSWOT; Water surface elevation; Arctic; Canada; Alaska
• Online Access: https://doi.org/10.17615/x5dc-5m49; https://cdr.lib.unc.edu/downloads/3t9461349?file=thumbnail; https://cdr.lib.unc.edu/downloads/3t9461349

AirSWOT is an experimental airborne Ka-band radar interferometer developed by NASA-JPL as a validation instrument for the forthcoming NASA Surface Water and Ocean Topography (SWOT) satellite mission. In 2017, AirSWOT was deployed as part of the NASA Arctic Boreal Vulnerability Experiment (ABoVE) to map surface water elevations across Alaska and western Canada. The result is the most extensive known collection of near-nadir airborne Ka-band interferometric synthetic aperture radar (InSAR) data and derivative high-resolution (3.6 m pixel) digital elevation models to produce water surface elevation (WSE) maps. This research provides a synoptic assessment of the 2017 AirSWOT ABoVE dataset to quantify regional WSE errors relative to coincident in situ field surveys and LiDAR data acquired from the NASA Land, Vegetation, and Ice Sensor (LVIS) airborne platform. Results show that AirSWOT WSE data can penetrate cloud cover and have nearly twice the swath-width of LVIS as flown for ABoVE (3.2 km vs. 1.8 km nominal swath-width). Despite noise and biases, spatially averaged AirSWOT WSEs can be used to estimate sub-seasonal hydrologic variability, as confirmed with field GPS surveys and in situ pressure transducers. This analysis informs AirSWOT ABoVE data users of known sources of measurement error in the WSEs as influenced by radar parameters including incidence angle, magnitude, coherence, and elevation uncertainty. The analysis also provides recommended best practices for extracting information from the dataset by using filters for these four parameters. Improvements to data handing would significantly increase the accuracy and spatial coverage of future AirSWOT WSE data collections, aiding scientific surface water studies, and improving the platform’s capability as an airborne validation instrument for SWOT.