THE ADVANCED MICROWAVE RADIOMETER CONTRIBUTION TO THE CRISTAL MISSION SCIENCE: SNOW DEPTH ON SEA ICE

F. Polverari¹, S. Kacimi¹, M. G. Morris¹, A. Hossan¹, S. T. Brown¹

¹Jet Propulsion Laboratory, California Institute of Technology

The highly anticipated Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) mission, developed by the European Space Agency (ESA) is planned for launch in ~2027. The payload includes a dual-frequency (Ka-/Ku-band) Synthetic Aperture Radar altimeter and a nadir-looking microwave radiometer. The Advanced Microwave Radiometer (AMR-CR) is composed of two systems: the Low-Frequency Microwave Radiometer and the High-Resolution Microwave Radiometer (HRMR), both developed by NASA-Jet Propulsion Laboratory (JPL). Together, they will provide radiometric measurements over six-frequency channels ranging from 18.7 to 168 GHz, similar to the radiometer currently flying on Sentinel-6. This unprecedented combination of active and passive measurements over higher latitudes offers novel opportunities to enhance the cryosphere science at the core of the CRISTAL mission. In this work, we present our strategy for retrieving snow depth estimates from AMR-CR observations.

The highly reflective and insulative properties of snow modulate the thickness of the underlying ice cover, making it a key element of the sea ice systems. In addition, knowledge of snow depth and density is required to estimate sea ice thickness from satellite altimetry. To leverage the range of frequencies from the AMR-CR, we develop a Bayesian retrieval approach based on simulations from the Snow Microwave Radiative Transfer (SMRT) model. SMRT is a versatile tool which computes brightness temperature (TB) at different frequencies from multi-layered media, such as snow, overlying a reflective surface, including ice and water. It allows selecting among various electromagnetic and microstructure formulations. The use of SMRT allows us to investigate the expected sensitivity of the AMR-CR TB to various ocean-ice-snow layering configurations and snow-ice conditions.

For our Bayesian retrieval method, we start by building a SMRT-based inversion database. Here we focus on the Antarctic region. In this presentation, we describe the physical properties of snow and ice collected during field experiments that informed the creation of the database. We further show initial results from our Bayesian-SMRT approach and their assessment through a comparison with estimates from the heritage Marcus and Cavalieri (1998) gradient-ratio algorithm.

The work described in this abstract was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration. © 2025 California Institute of Technology. Government sponsorship acknowledged.