Mapping Snow Depth From Ka-Band Interferometry: Proof Of Concept And Comparison With Scanning Lidar Retrievals

Snow cover and its melt dominate sources in many of the world's mountainous regions, and in adjacent areas dependent on river flows originating from mountain basins. However, snow water equivalent (SWE) across Earth is very poorly known. Our inability to measure and track distribution of SWE severely hampers our skill in modeling snow cover for climate and hydrology. In 2013, NASA/JPL began an ambitious program to solve the need for distributed SWE and coincident snow albedo, developing the Airborne Snow Observatory (ASO). The SWE component of the ASO comes from the scanning lidar, which is used to map distributed topography for snow-free and snow-on conditions and in turn snow depth with an unbiased uncertainty of 8 cm. SWE is generated then with modeling of snow density, constrained by available in situ measurements. ASO has provided full basin and distributed mapping of SWE leading to unique discoveries for water cycle science. While these measurements provide critical measurements, an identical path to space with lidar is not presently available and suffers from cloud cover. We investigate the capacity of a Ka-band single pass interferometric synthetic aperture radar (InSAR) GLISTIN (Glacier and Ice Surface Topography Interferometer) to map snow topography/snow depth in a complex mountain basin independent of cloud cover. As a proof-of-concept, GLISTIN overflew a portion of the ASO site in the Sierra Nevada in August 2012 (snow-off) and again in April 2013 (snow-on). Despite it being a "low-snow year"whereby the snow-depth is largely decimetric in a region of high topographic complexity, the quantitative and qualitative comparisons of GLISTIN and ASO are encouraging. Our methodology includes: 1) classification of tree-contaminated regions using the InSAR correlation data at high spatial resolution; 2) data calibration of the InSAR height-data; and 3) generation of bare-Earth digital elevation models at coarsened resolution. Our initial GLISTIN/ASO intercomparison is encouraging, but expanded to identify sources of spatially correlated topographically-induced higher error regions. We also discuss implications of the interferometric penetration of the radar data into the snow and vegetated surface on the measurement . Plans to further this initial evaluation in a future campaign are discussed.