Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture

Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture

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...

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Bibliographic Details
Main Authors: Andrea Maino, Matteo Alberi, Emiliano Anceschi, Enrico Chiarelli, Luca Cicala, Tommaso Colonna, Mario De Cesare, Enrico Guastaldi, Nicola Lopane, Fabio Mantovani, Maurizio Marcialis, Nicola Martini, Michele Montuschi, Silvia Piccioli, Kassandra Giulia Cristina Raptis, Antonio Russo, Filippo Semenza, Virginia Strati
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Remote Sensing
Subjects:
airborne gamma-ray spectroscopy
non-linear machine learning
potassium
clay
thorium
sand
Online Access:https://www.mdpi.com/2072-4292/14/15/3814

Internet

https://www.mdpi.com/2072-4292/14/15/3814

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