Application of Neural Networks to Soil Moisture Retrievals from L-Band Radiometric Data

Many algorithms for retrieving geophysical variables are based on optimal estimation approaches, which can be time consuming specially if a large amount of data is to be processed. On its part, neural networks provide results almost in real time, but their use is still not generalised for remote sensing applications. In this work, a set of neural networks was trained with simulations using numerical land emission models and tested using L-band radiometric data of bare soils acquired during the T-REX and MOUSE field experiments. Soil moisture retrieved by the neural networks was then compared to ground-truth data.