  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.
