Passive microwave remote sensing of snow parameters constrained by snow hydrology model and snow grain size growth

To predict the snow parameters using passive microwave remote sensing, such as snow depth or snow water equivalent, is an important issue in the geoscience problems. The retrieval is a complicated task because the remote sensing measurements are affected by multiple snow parameters. There are three major components in the authors' parameter retrieval algorithm-a dense media radiative transfer (DMRT) model which is based on the quasi-crystalline approximation (QCA) and the sticky particle model, a physically based snow hydrology model (SHM) and a neural network (NN) for speedy retrievals. The retrieval algorithm is applied to stations over the Northern Hemisphere and the results compare favorably with the ground truth measurements.