RETRIEVAL OF SNOW PARAMETERS BY ITERATIVE INVERSION OF A NEURAL-NETWORK

The inversion of snow parameters from passive microwave remote sensing measurements is performed based on an iterative inversion of a neural network (NN) trained with a dense media multiple scattering model. Inversion of four parameters is performed from five brightness temperatures. The four parameters are: mean-grain size of ice particles in snow, snow density, snow temperature, and snow depth. Iterative inversion of a data driven forward NN model is justified on a theoretical and methodological basis. We perform an error analysis, comparing iterative inversion of a forward model with the use of an explicit inverse for the retrieval of independent snow parameters from their corresponding measurements. The NN iterative inversion algorithm is further illustrated by reconstructing a synthetic terrain of snow parameters from their corresponding measurements, inverting all four parameters simultaneously. The reconstructed parameter contours are in good agreement with the original synthetic parameter contours.