Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture

TL;DR

This study combines airborne gamma-ray spectroscopy and machine learning, especially deep neural networks, to improve soil texture mapping accuracy, revealing finer features and historical paleo-channels in the Mezzano Lowland.

Contribution

It introduces a non-linear machine learning approach that significantly enhances soil texture prediction from gamma-ray data compared to traditional methods.

Findings

Non-linear models outperform linear models in soil texture prediction.

Airborne gamma-ray data reveals finer soil features than regional maps.

Predicted clay maps align with historical paleo-channel locations.

Abstract

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 $km^2$ agricultural plain investigated through a ded-icated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-structures were equally present, the airborne gam-ma-ray data permits us shedding light on finer features. Map areas with higher clay content were coincident with paleo-channels crossing the Mezzano Lowland in Etruscan and Roman periods, confirmed by the hydrographic setting of historical maps and by the geo-morphological features of the study area.