Backpropagation Neural Network for Soil Moisture Retrieval Using Nafe’05 Data : a Comparison of Different Training Algorithms

The backpropagation artificial neural network (ANN) is a well-known and widely applied mathematical model for remote sensing applications for pattern recognition, approximation and mapping of non-linear functions and time-series prediction. The backpropagation ANN algorithm is underpinned by a gradient descent algorithm that is used to modify the network weights to maximise performance, using some criterion function. There are a number of variations from this general algorithm and it is necessary to explore these to find the best method for any particular application. The application considered in this paper is the determination of volumetric soil moisture content given airborne microwave measurements of the Hand V-polarized brightness temperature obtained during the National Airborne Field Experiment 2005 (NAFE’05). In this paper, a number of backpropagation ANN methods are investigated. Some produce the globally acceptable accuracy of less than or equal to 4%v/v of Root Mean Square Error (RMSE). However, the standard deviation among the 11 different variations of backpropagation training algorithms (0.55) is significant compared to the accuracy. Hence, there is a need for a full analysis of the backpropagation ANN and careful selection of the best backpropagation ANN model to be used.