Passive Microwave Remote Sensing of Terrestrial Snow — Preparing for the Copernicus Imaging Microwave Radiometer

Abstract

Seasonal terrestrial snow is a crucial freshwater resource and a fundamental indicator of climate change. Spaceborne passive microwave radiometry is capable of monitoring both snow cover extent (SCE) and snow water equivalent (SWE) on a global scale, by exploiting the distinctive frequency-dependent characteristics of the microwave emission of snow. The main limitation of passive microwave approaches is the coarse spatial resolution which, amongst other reasons, causes systematic underestimation and large uncertainties of both SCE and SWE. The upcoming Copernicus Imaging Microwave Radiometer (CIMR), with its higher spatial resolution and full 360° scan promises substantial improvements—provided current methodologies and the influence of instrument geometry and topography are well understood. In preparation for CIMR, this thesis advances passive microwave snow methodologies through three studies, focused on dry snow detection and its implications for SWE retrieval on hemispheric, continental and regional scales.

The first study presents the first comprehensive long-term, hemispheric evaluation of six dry snow detection algorithms for CIMR-like frequencies of SMMR, SSM/I and SSMIS, which are validated against in situ snow depth measurements and IMS snow maps. All algorithms underestimate SCE, but cumulative snow masks counteract this bias during the accumulation season. Implementing the best algorithms within the GlobSnow framework improves SWE statistics, most notably for shallow autumn snow. This highlights the impact that the algorithm choice for dry snow detection has on the quality of SWE retrieval and therefore on long-term climate data records. The second study is the first to investigate WindSat’s near-instantaneous dual-look brightness temperatures for snow mapping over Eurasia. The scan azimuth angle systematically affects detection: south-looking observations (capturing deeper, drier snow on north-facing slopes) consistently detect more snow. Spatially superimposing both looks increases mapped SCE while maintaining high agreement with reference data. Dual-look snow mapping further highlights the need for water-spillover corrections and renews the interest in relief effects. The third study then quantifies, for the first time, how topography affects brightness temperatures of snow-covered land. WindSat’s fore-aft brightness temperatures generally have a high correlation over Eurasia; however, the correlation drops over mountains and during peak winter. This drop, as demonstrated for the Greater Alpine Region in Europe, is due to subpixel rather than coarse-pixel topography and can be expressed through sensor-independent and sensor-dependent characteristics. This motivates topographic corrections on fine spatial scales as well as regionally varying incidence angles within snow retrievals.

Overall, this thesis demonstrates that algorithm choice, viewing geometry, and subpixel topography each measurably affect passive microwave snow estimates. These effects and related uncertainties will persist for CIMR, despite the higher spatial resolution, making the findings directly transferable to CIMR snow product development and to global snow monitoring for enhanced climate adaptation and water resource management.