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Coherent synthetic aperture radar (SAR) based terrain monitoring relies on effective image focusing, compensation of interferometric phase biases and mitigation of decorrelation effects. The observation of dry-snow-covered terrain is affected by microwave interaction with the snow and this poses a challenge for terrain mapping applications in terms of the effect of the snow on target focusing, signal phase bias and decorrelation. However, these effects also provide an opportunity to map snow properties. As such, dry-snow presents a dual problem for coherent SAR applications: the joint mitigation of the effects of snow to allow unbiased observation of the terrain under the snow and measurement of the snow layer itself. This thesis introduces novel methods to address three aspects of this dual problem: (i) image formation – the defocusing and phase biasing effects of dry-snow are considered including how these can be corrected and exploited to estimate snow water equivalent (SWE) from a single SAR channel; (ii) interferometric phase bias – the limitations of SAR interferometry (InSAR) based SWE change mapping are addressed by exploiting the effect that terrain slope has on the dry-snow InSAR phase contribution; and (iii) temporal decorrelation – an adaptation of phase-linking methods is introduced to better enable multi-temporal InSAR for the case of seasonal snow covered terrain which suffers sever cross-season decorrelation effects. In each case, an analytical model for the respective method is presented, the use of the method is demonstrated with simulated data while method performance is validated with real SAR datasets, either from the SFU Airborne SAR System or the RADARSAT-2 satellite. Together, these contributions represent a significant advancement in enabling wide-scale and persistent coherent SAR monitoring of snow-covered terrains.

The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX- and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt.

We discuss enhanced processing methods for high resolution Synthetic Aperture Radar (SAR) interferometry (InSAR) to monitor small landslides with difficult spatial characteristics, such as very steep and rugged terrain, strong spatially heterogeneous surface motion, and coherence-compromising factors, including vegetation and seasonal snow cover. The enhanced methods mitigate phase bias induced by atmospheric effects, as well as topographic phase errors in coherent regions of layover, and due to inaccurate blending of high resolution discontinuous with lower resolution background Digital Surface Models (DSM). We demonstrate the proposed methods using TerraSAR-X (TSX) Staring Spotlight InSAR data for three test sites reflecting diverse challenging landslide-prone mountain terrains in British Columbia, Canada. Comparisons with corresponding standard processing methods show significant improvements with resulting displacement residuals that reveal additional movement hotspots and unprecedented spatial detail for active landslides/rockfalls at the investigated sites.