Simulating skylight polarization patterns using the backward Markov Chain Monte Carlo method

Abstract

Skylight polarization patterns provide valuable information for atmospheric measurements, polarized remote sensing and navigation applications. However, efficiently and accurately modeling polarized radiative transfer in atmospheric scattering remains challenging. We propose a backward Markov chain Monte Carlo (B-MCMC) method to simulate skylight polarization patterns by constructing a Markov chain in parameter space to track photons from the sensor to the top-of-atmosphere (TOA). The results show that the B-MCMC model significantly improves the computational efficiency by a factor of 8-10 while retaining the computational accuracy compared with Monte Carlo simulations. The experiments show that in cloudy skies, the skylight polarization pattern is generally weakened, in the field of skylight polarization detection and application, long wavelengths should be used in clear weather and blue-violet light should be used in cloudy weather, which corresponds to a larger degree of polarization (DOP) and facilitates the acquisition of polarization information. Finally, the aerosol optical depth (AOD) has an important effect on the skylight polarization, as the AOD increases, the DOP decreases, and the decreasing trend will be more and more obvious, when the AOD is above 0.3, the maximum DOP will not exceed 0.5, which is verified by the division of focal plane (DOFP) polarization measurement device.