A deep neural network for predicting soil texture using airborne radiometric data

The ternary nature of soil texture, defined by its proportions of clay, silt, and sand, makes it challenging to predict through linear regression models from other soil attributes and auxiliary variables. The most promising results in this field have been recently achieved by Machine Learning methods which are able to derive nonlinear, non-site-specific models to predict soil texture. In this paper we present a method of constructing a pair of Deep Neural Network (DNN) algorithms that can predict clay and sand soil contents from Airborne Gamma Ray Spectrometry data of K and Th ground abundances. We tested the algorithm’s hyperparameters through various configurations to optimize the DNNs’ performance, effectively avoiding underfitting and overfitting of the models. This led to the creation of a high resolution 20 m × 20 m soil texture map from dense AGRS data, significantly refining the previous map’s granularity. The application of the obtained DNN models to unseen sites can be supported by future training on additional textural classes.