Estimating Alpine Snow Depth by Combining Multi-Frequency Passive Radiance Observations with Ensemble Snowpack Modeling

• Published in: Remote sensing of environment 2019-06, Vol.226
• Main Authors: Kim, Rhae Sung, Durand, Michael, Li, Dongyue, Baldo, Elisabeth, Margulis, Steven, Dumont, Marie, Morin, Samuel
• Format: Article
• Language: English
• Subjects: Earth Resources And Remote Sensing
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2019.03.016

This paper presents a physically-based snow depth retrieval algorithm adapted for deep mountainous snowpack and airborne multifrequency (10.7, 18.7, 37.0 and 89.0 GHz) passive microwave (PM) radiance observations from a single flight. The algorithm employs a single forecast-analysis cycle of a traditional sequential assimilation scheme. It uses an ensemble of multi-layer snowpack model runs to resolve snow microstructure and melt-refreeze crusts, andmicrowave radiative transfer models to relate snow properties to microwave measurements.Snow depth was retrieved at a 120 m spatial resolution over three 1 km2 Intensive Study Area (ISA) within the Rabbit Ears Meso-Cell Study Area (MSA) from the NASA Cold Land Processes Experiment (CLPX) in Colorado (United States) for one date in February 2003. When evaluated against in situ observations, root mean square error (RMSE) of the snow depth from the assimilation was 13.3 cm for areas with low (