The Aquarius/SAC-D is an Earth Science remote sensing satellite mission to measure global Sea Surface Salinity (SSS) that is sponsored by the NASA and the Argentine Space Agency (CONAE). The prime remote sensor is the Aquarius (AQ) L-band radiometer/scatterometer, which measures the L-band emitted blackbody radiation (brightness temperature) from the ocean. The brightness temperature at L-band is proportional to the ocean salinity as well as a number of physical parameters including ocean surface wind speed. The salinity retrieval algorithm make corrections for all other parameters before retrieving salinity, and the greatest of these is the increased brightness temperature due to roughness caused by surface wind speed. This thesis presents an independent approach for the AQ roughness correction, which is derived using simultaneous measurements from the CONAE Microwave Radiometer (MWR). When the wind blows over the ocean’s surface, the brightness temperature is increased because of the ocean wave surface roughness. The MWR provides a semi-empirical approach by measuring the excess ocean emissivity at 36.5 GHz and then applying radiative transfer theory (improved ocean surface emissivity model) to translate this to the AQ 1.4 GHz frequency (L-band). The theoretical basis of the MWR algorithm is described and empirical results are presented that demonstrate the effectiveness in reducing the salinity measurement error due to surface roughness.

Aquarius/SAC-D is a joint NASA/CONAE (Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using an L-band radiometer that measures ocean brightness temperature (Tb). The application of L-band radiometry to retrieve SSS is a difficult task, and therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that "warms" the ocean Tb. The Aquarius (AQ) instrument (L-band radiometer/scatterometer) baseline approach uses the radar scatterometer to provide this ocean roughness correction, through the correlation of radar backscatter with the excess ocean emissivity. In contrast, this dissertation develops an ocean roughness correction for AQ measurements using the MicroWave Radiometer (MWR) instrument Tb measurements at Ka-band to remove the errors that are caused by ocean wind speed and direction. The new ocean emissivity radiative transfer model was tuned using one year (2012) of on-orbit combined data from the MWR and the AQ instruments that are collocated in space and time. The roughness correction in this paper is a theoretical Radiative Transfer Model (RTM) driven by numerical weather forecast model surface winds, combined with ancillary satellite data from WindSat and SSMIS, and environmental parameters from NCEP. This RTM provides an alternative approach for estimating the scatterometer-derived roughness correction, which is independent. The theoretical basis of the algorithm is described and results are compared with the AQ baseline scatterometer method. Also results are presented for a comparison of AQ SSS retrievals using both roughness corrections.