Refractive index and surface roughness estimation using passive multispectral and multiangular polarimetric measurements

This paper presents a method to estimate refractive index and surface roughness simultaneously from multispectral and multiangular passive polarimetric measurements. Such a method has ties to passive remote sensing applications. Within the analysis, we use a previously derived expression for the degree of linear polarization, and a nonlinear least-squares algorithm to estimate the parameters of interest (i.e., refractive index and surface roughness) from the measured data. The results obtained from Monte Carlo simulations show that the estimation accuracy improves as the number of spectral channels and detection angles increase. It does so until the estimation accuracy reaches saturation. To take full advantage of the presented method, we also determine the most reasonable number of spectral channels and detection angles for our laboratory measurements using Monte Carlo simulations. Finally, after analyzing the experimental results for dielectric and metallic samples, we validate the effectiveness and advantages of the presented method to estimate refractive index and surface roughness for passive remote sensing.