EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Mapping soil texture with airborne gamma ray spectroscopy

Andrea Maino1,2, Matteo Alberi1,2, Emiliano Anceschi3, Enrico Chiarelli1,2, Luca Cicala4, Tommaso Colonna5, Mario De Cesare4,6,7, Enrico Guastaldi5, Nicola Lopane1,5, Fabio Mantovani1,2, Nicola Martini8, Michele Montuschi1,2, Silvia Piccioli8, Kassandra Giulia Cristina Raptis1,2, Antonio Russo3, Filippo Semenza1, and Virginia Strati1,2
Andrea Maino et al.
  • 1Department of Physics and Earth Sciences, University of Ferrara, Ferrara, Italy.
  • 2INFN, Ferrara Section, Ferrara, Italy.
  • 3Gruppo Filippetti, Falconara Marittima, Ancona, Italy.
  • 4Italian Aerospace Research Centre CIRA, Capua, Caserta, Italy.
  • 5GeoExplorer Impresa Sociale S.r.l., Arezzo, Italy.
  • 6Department of Mathematics and Physics, University of Campania “Luigi Vanvitelli”, Caserta, Italy.
  • 7INFN, Napoli Section, Complesso Universitario di Monte S. Angelo, Napoli, Italy.
  • 8Le Due Valli S.r.l., Ostellato, Ferrara, Italy.

Soil texture is a key information in precision agriculture for improving soil knowledge and crop performances. A precise mapping of its variability is thereby imperative for rationally planning cultivations and targeting interventions. Unlike direct soil texture measurements that are punctual, destructive, and time-consuming, remote sensing surveys can give widespread, non-invasive, and fast indirect evidence of clay, silt, and sand content. In this study we investigate the performance of Airborne Gamma Ray Spectroscopy (AGRS) for discriminating different texture classes in the ternary diagram of soil texture.

The Mezzano valley (Ferrara, Italy), a 180 km2 rural area reclaimed in the last century, represents an extraordinary benchmark for validating our method. This area, for which a public soil texture map at 1:50000 scale and a spatial resolution of 500 m is available, was scanned by an AGRS system mounted on a dedicated aircraft. The aircraft flew over the study area in a grid-like path of ~500 m spacing, collecting 1469 geolocalized spectra. The K and Th punctual measurements were spatially interpolated by Ordinary Kriging to elaborate K and Th maps with the identical spatial resolution of the soil texture map. Simple and multiple linear correlations, as well as a non‑linear Machine Learning algorithm, were then performed between gamma and soil texture data.

The obtained results by a simple linear regression analysis highlight a moderate positive (negative) correlation between clay (sand) content and K and Th abundances. Multiple linear regressions show a similar trend, with the limitation that the calculated clay, silt, and sand values populate the soil texture ternary diagram in a straight line. Finally, we demonstrate that the most accurate reconstruction of soil texture values is obtained by a non-linear fitting based on the Machine Learning algorithm.

How to cite: Maino, A., Alberi, M., Anceschi, E., Chiarelli, E., Cicala, L., Colonna, T., De Cesare, M., Guastaldi, E., Lopane, N., Mantovani, F., Martini, N., Montuschi, M., Piccioli, S., Raptis, K. G. C., Russo, A., Semenza, F., and Strati, V.: Mapping soil texture with airborne gamma ray spectroscopy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-361,, 2022.