Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements

Abstract

A broad class of neural network (NN) applications dealing with the remote measurements of geophysical (physical, chemical, and biological) parameters of the oceans, atmosphere, and land surface is presented. In order to infer these parameters from remote sensing (RS) measurements, standard retrieval and variational techniques are applied. Both techniques require a data converter (transfer function or forward model) to convert satellite measurements into geophysical parameters or vice versa. In many cases, the transfer function and the forward model can be represented as a continuous nonlinear mapping. Because the NN technique is a generic technique for nonlinear mapping, it can be used beneficially for modeling transfer functions and forward models. These applications are introduced in a broader framework of solving forward and inverse problems in RS. In this broader context, we show that NN is an appropriate and efficient tool for solving forward and inverse problems in RS and for developing fast and accurate forward models and accurate and robust retrieval algorithms. Theoretical considerations are illustrated by several real-life examples—operational NN applications developed by the authors for SSM/I and medium resolution imaging spectrometer sensors.