ANN technique for the evaluation of soil moisture over bare and vegetated fields from microwave radiometer data

Abstract

Retrieving information from remotely sensed data is an important task. In the present work, data of L band microwave radiometer has been used to collect the brightness temperature over bare and vegetated fields in two polarizations at different moisture levels. Artificial neural network (ANN) trained with Levenberg-Marquardt algorithm has been used to determine soil moisture from brightness temperatures measured by microwave radiometry. ANN are trained to evaluate the moisture content in the range 0 - 36% from different sets of data of bare and vegetated fields. Properly trained feed-forward neural network with Levenberg-Marquardt algorithm predicted soil moisture content with less mean absolute error