Modeling Biophysical Variables in the Canadian High Arctic Using Synthetic Aperture Radar Data

• Main Author: Collingwood, Adam
• Other Authors: Geography, Treitz, Paul
• Format: Thesis
• Language: English
• Published: 2014
• Subjects: Remote Sensing; SAR; Arctic; Nunavut; Canada; Melville Island
• Online Access: http://hdl.handle.net/1974/8618

Thesis (Ph.D, Geography) -- Queen's University, 2014-02-03 16:52:26.856 The estimation or modeling of biophysical variables such as surface roughness, vegetation phytomass, and soil moisture in the Arctic is an important step towards understanding arctic energy fluxes, effects of changing climate, and hydrological patterns. This research uses Synthetic Aperture Radar (SAR) data, along with ancillary optical and environmental data, to create models that estimate these biophysical variables across different High Arctic landscapes, with the goal of applying the models across even larger areas. Field work was conducted at two High Arctic locations on Melville Island, Nunavut, Canada. At each location, surface roughness values were measured at a number of randomized plot locations using a pin meter. Soil moisture values were measured using a time domain reflectometry (TDR) instrument within six hours of multiple overpasses of the RADARSAT-2 SAR sensor. Surface roughness models were generated with multi-incidence angle and fully polarimetric SAR data, with resulting R2 values ranging between 0.39 and 0.66, and normalized root mean squared error (N_RMSE) values of 14% - 22%. The output from the final surface roughness model was used as an input to the soil moisture models. Vegetation phytomass was modeled with multi-angular SAR data, using a soil adjusted vegetation index (SAVI) derived from optical data across the study area as a measure of verification. The resulting model had a significant (p <0.05) relationship to the SAVI values, with an R2 of 0.60. This model was then compared to field-collected above-ground phytomass values, and a model was derived that related SAR data directly to phytomass. This model again showed a strong relationship, with an R2 value of 0.87. The final biophysical variable that was modeled, soil moisture, showed moderate agreement to field-measured soil moisture values (R2 = 0.46, N_RMSE = 0.15%), but much stronger relationships were found for relative moisture values at fine scales ...