Mapping Arctic Sea Ice Surface Roughness with Multi-angle Imaging SpectroRadiometry

Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extended over multi-decadal time-scales.  The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery at 275m (red channel) and 1.1 km (all channels) resolutions from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident probability distribution functions of lidar derived elevations from the Airborne Topographic Mapper (ATM) is generated. Surface roughness, defined as the standard deviation of the within-pixel elevations to a best-fit plane, is modelled using Support Vector Regression with a Radial Basis Function kernel, hyperparameters are tuned using GridSearchCV, and performance is assessed using nested cross-validation. We present derived instantaneous and monthly averaged sea ice roughness products at 1.1km and 17.6km resolution over the timespan of IceBridge campaigns (March and April for 2009-2018) on an EASE-2 (Equal-Area Scalable Earth) grid. These show considerable promise in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.