Classification of soil texture using remotely, sensed brightness temperature over the southern great plains

This study explores the use of artificial neural networks (ANNs) models and brightness temperature from the Southern Great Plains in the United States to classify soil into different textures. Previous studies using ANN models and brightness temperature in a single drying cycle suggested that they might contain sufficient features to classify soil into three categories. To classify soil into more than three groups and to explore the limits of classification accuracy, this paper suggests the use of multiple-drying-cycle brightness temperature data. We have performed several experiments with feed-forward neural network (FFNN) models, and the results suggest that the maximum achievable classification accuracy through the use of multiple-drying-cycle brightness temperature is about 80%. It appears that the rapidly changing space-time evolution of brightness temperature will restrict the FFNN model performance. Motivated by these observations, we have used a simple prototype-based classifier, known as the 1-NN model, and achieved 86% classification accuracy for six textural groups. A comparison of error regions predicted by both models suggests that for the given input representation maximum achievable accuracy for classification into six soil texture types is about 93%.