Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture

Andrea Maino
* ORCID logo ,
Matteo Alberi
ORCID logo ,
Emiliano Anceschi
,
Enrico Chiarelli
,
Luca Cicala
ORCID logo ,
Tommaso Colonna
ORCID logo ,
Mario De Cesare
ORCID logo ,
Enrico Guastaldi
ORCID logo ,
Nicola Lopane
,
Fabio Mantovani
ORCID logo ,
Maurizio Marcialis
,
Nicola Martini
,
Michele Montuschi
,
Silvia Piccioli
,
Kassandra Giulia Cristina Raptis
,
Antonio Russo
,
Filippo Semenza
,
Virginia Strati
ORCID logo
Version 1 : Received: 13 August 2022 / Approved: 15 August 2022 / Online: 15 August 2022 (12:01:44 CEST)

A peer-reviewed article of this Preprint also exists.

Maino, A.; Alberi, M.; Anceschi, E.; Chiarelli, E.; Cicala, L.; Colonna, T.; De Cesare, M.; Guastaldi, E.; Lopane, N.; Mantovani, F.; et al. Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture. Remote Sensing, 2022, 14, 3814. https://doi.org/10.3390/rs14153814. Maino, A.; Alberi, M.; Anceschi, E.; Chiarelli, E.; Cicala, L.; Colonna, T.; De Cesare, M.; Guastaldi, E.; Lopane, N.; Mantovani, F.; et al. Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture. Remote Sensing, 2022, 14, 3814. https://doi.org/10.3390/rs14153814.

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 km2 agricultural plain investigated through a dedicated 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 gamma-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.

Keywords

airborne gamma-ray spectroscopy; non-linear machine learning; potassium; clay; thorium; sand; soil texture; paleo-hydrography

Subject

Environmental and Earth Sciences, Geophysics and Geology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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