Passive Microwave Remote Sensing of Snow Layers Using Novel Wideband Radiometer Systems and RFI Mitigation
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Climate change can reduce the availability of water resources in many regions, and affect the energy supplies. Snowpacks are important in water resource management as well as flood and avalanche protection. The rapid melting process due to global warming changes the snowpacks’ annual statistics, including extent, timing, and snow water equivalent (SWE) of seasonal snowpack, which results in non-stationary annual statistics that should be monitored in nearly daily intervals.
The development of advanced radiometric sensors capable of accurately measuring the snowpack thickness and SWE is needed for the study of the snowpack parameters’ statistical changes. A Wideband Autocorrelation Radiometer (WiBAR) that measures at long wavelengths, can reveal the microwave travel time through the snow, and thus the snow depth.
There are challenges that need to be addressed to advance the future airborne and spaceborne operation of WiBAR. Some of these challenges and the solutions are presented in this dissertation. We built a novel frequency tunable microwave comb filter for RFI mitigation. We demonstrated time-domain WiBAR with a new time-domain calibration results in faster data acquisition. The effects of an above snow vegetation layer on brightness temperature are evaluated and a new multiple scattering model of brightness temperature is proposed for the forest.
Chairs: Professor Kamal Sarabandi & Dr. Roger De Roo