Spaceborne microwave sensors are capable of estimating various properties of many geophysical phenomena, including the age and extent of Arctic sea ice and the relative soil moisture over land. The measurement and classification of such geophysical phenomena are used to refine climate models, localize and predict drought, and better understand the water cycle. Data from the active Ku-band scatterometers, the Quick Scatterometer (QuikSCAT), and the Oceansat-2 Scatterometer (OSCAT), are here used to classify areas of first-year and multiyear Arctic sea ice using a temporally adaptive threshold on reported radar backscatter values. The result is a 15-year data record of daily ice classification images. An additional ice age data record is produced using the C-band Advanced Scatterometer (ASCAT) and the Special Sensor Microwave Imager Sounder (SSMIS) with an alternate classification methodology based on Bayesian decision theory. The ASCAT/SSMIS classification methodology results in a record which is generally consistent with the QuikSCAT and OSCAT classifications, which conclude in 2014. With multiple ASCAT and SSMIS sensors still operational, the ASCAT/SSMIS ice classifications can continue to be produced into the future. In addition to ice classification, ASCAT is used to estimate the relative surface soil moisture at high-resolution (4.45 — 4.45 km per pixel). The soil moisture estimates are obtained using enhanced resolution image reconstruction techniques and an altered version of the Water Retrieval Package (WARP) algorithm. The high-resolution soil moisture estimates are shown to agree well with the existing lower resolution WARP products while also revealing finer details.

Long term trends in Arctic sea ice are of particular interest with regard to global temperature, climate change, and industry. This thesis uses microwave scatterometer data from QuikSCAT and radiometer data to analyze intra- and interannual trends in first-year and multi-year Arctic sea ice. It develops a sea ice type classification method. The backscatter of first-year and multi-year sea ice are clearly identifiable and are observed to vary seasonally. Using an average of the annual backscatter trends obtained from QuikSCAT, a classification of multi-year ice is obtained which is dependent on the day of the year (DOY). Validation of the classification method is done using regional ice charts from the Canadian Ice Service. Differences in ice classification are found to be less than 6% during the winters of 06-07, 07-08, and the end of 2008. Anomalies in the distribution of sea ice backscatter from year to year suggest a reduction in multi-year ice cover between 2003 and 2009 and an approximately equivalent increase in first-year ice cover.

Because the earth's cryosphere influences global weather patterns and climate, the scientific community has had great interest in monitoring this important region. Microwave remote sensing has proven to be a useful tool in estimating sea and glacial ice surface characteristics with both scatterometers and radiometers exhibiting high sensitivity to important ice properties. This dissertation presents an array of studies focused on extracting key surface features from multisensor microwave data sets. First, several enhanced resolution image reconstruction issues are addressed. Among these are the optimization of the scatterometer image reconstruction (SIR) algorithm for NASA scatterometer (NSCAT) data, an analysis of Ku-band azimuthal modulation in Antarctica, and inter-sensor European Remote Sensing Satellite (ERS) calibration. Next, various methods for the removal of atmospheric distortions in image reconstruction of passive radiometer observations are considered. An automated algorithm is proposed which determines the spatial extent of sea ice in the Arctic and Antarctic regions from NSCAT data. A multisensor iterative sea ice statistical classification method which adapts to the temporally varying signatures of ice types is developed. The sea ice extent and classification algorithms are adopted for current SeaWinds scatterometer data sets. Finally, the automated inversion of large-scale forward electromagnetic scattering of models is considered and used to study the temporal evolution of the scattering properties of polar sea ice.