Mapping Arctic sea ice surface roughness with Multi-angle Imaging Spectro-radiometer.

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 and thickness. At a local scale, roughness in the form of ridges, hummocks, rafted ice can slow down and hinder safe transport on the ice as well as be a hazard for ice strengthened vessels and structures. 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 extend over multi-decadal time-scales. The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery 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 roughness from coincident operation IceBridge (OIB) airborne laser data is generated. Surface roughness, defined as the standard deviation of the within-pixel lidar elevations to a best-fit plane, is modelled using several techniques and Support Vector Regression with a Radial Basis Function kernel selected. Hyperparameters are tuned using grid optimisation, model performance is assessed using blocked k-fold cross-validation. We present a derived sea ice roughness product at 1.1km resolution over the period of operation (April 2000 – 2020) and a corresponding time series analysis. To demonstrate the validity of the derived product, we first evaluate our roughness product against independent LiDAR characterisations of surface roughness consistent with our training data. We also evaluate our derived roughness product with known proxies of surface roughness on a pan-Arctic basis (AWISMOS CS2-SMOS sea ice thickness.) Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable capacity in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.