Neural network inversion of snow parameters by fusion of snow hydrology prediction and SSM/I microwave satellite measurements

Abstract

Inverse remote sensing problems are generally ill-posed. In this paper, we propose an approach, which integrates the dense media radiative transfer (DMRT) model, snow hydrology model, neural networks and SSM/I microwave measurements, to infer the snow depth. Four multilayer perceptrons (MLPs) were trained using the data from DMRT model. With the provision of an initial guess from snow hydrology prediction, neural networks effectively invert the snow parameters based on SSM/I measurements. In addition, a prediction neural network is used to achieve adaptive learning rates and a good initial estimate of snow depth for inversion. Result shows that our algorithm can effectively and accurately retrieve snow parameters from these highly nonlinear and many-to-one mappings.