Inversion of Snow Parameters by Neural Network with Iterative Inversion

Abstract

The inversion of snow parameters from passive microwave remote sensing measurements is performed with a neural network trained with a dense media multiple scattering model. A constrained iterative inversion scheme is used. Inversion of four parameters has been performed from five brightness temperatures. The four parameters are: mean-grain size of ice particles in snow, snow density, snow temperature and snow depth. The five brightness temperatures are that of 19 GHz vertical polarization, 19 GHz horizontal polarization, 22 GHz vertical polarization, 37 GHz vertical polarization, and 37 GHz horizontal polarization which are available from SSMI satellites. Based on the neural network constrained iterative inversion algorithm, we have also performed synthetic mapping of the terrain. Retrieval of synthetic mapping has been achieved. The incorporation of ground truth information is also considered.