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Using brightness temperature Tb measurements from L-band airborne microwave radiometers, with independent sea surface temperature (SST) observations, sea surface salinity (SSS) can be remotely determined with errors of about 1 psu in temperate regions. Nonlinearities in the relationship between Tb, SSS, and SST produce variations in the sensitivity of salinity S to variations in Tb and SST. Despite significant efforts devoted to SSS remote sensing retrieval algorithms, little consideration has been given to deriving density D from remotely sensed SSS and SST. Density is related to S and T through the equation of state. It affects the ocean's static stability and its dynamical response to forcings. By chaining together two empirical relationships (flat-sea emissivity and equation of state) to form an inversion algorithm for sea surface density (SSD) in terms of Tb and SST, we develop a simple L-band SSD retrieval algorithm. We use this to investigate the sensitivity of SSD retrievals to observed Tb and SST and infer errors in D for typical sampling configurations of the airborne Salinity, Temperature, And Roughness Remote Scanner (STARRS) and satellite-borne Soil Moisture and Ocean Salinity (SMOS) and Aquarius radiometers. We then derive D from observations of river plumes obtained using STARRS and demonstrate several oceanographic applications: the observations are used to study variations in T and S effects on D in the Mississippi plume, and the across-shelf density gradient is used to infer surface geostrophic shear and subsurface geostrophic current in the Plata plume. Future basin-scale applications of SSD retrievals from satellite-borne microwave radiometers such as SMOS and Aquarius are anticipated.