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...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/15/3814 |
| _version_ | 1797412372320616448 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Andrea Maino |
| collection | DOAJ |
| description | 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<sup>2</sup> 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. |
| first_indexed | 2024-03-09T05:02:04Z |
| format | Article |
| id | doaj.art-6cc1994c954d4b29aac2531480b5cbca |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:02:04Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6cc1994c954d4b29aac2531480b5cbca2023-12-03T12:59:14ZengMDPI AGRemote Sensing2072-42922022-08-011415381410.3390/rs14153814Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil TextureAndrea Maino0Matteo Alberi1Emiliano Anceschi2Enrico Chiarelli3Luca Cicala4Tommaso Colonna5Mario De Cesare6Enrico Guastaldi7Nicola Lopane8Fabio Mantovani9Maurizio Marcialis10Nicola Martini11Michele Montuschi12Silvia Piccioli13Kassandra Giulia Cristina Raptis14Antonio Russo15Filippo Semenza16Virginia Strati17Department of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalyDepartment of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalyGruppo Filippetti Sede Falconara Marittima, 60015 Falconara Marittima, Ancona, ItalyDepartment of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalyCIRA, Italian Aerospace Research Centre, 81043 Capua, Caserta, ItalyGeoExplorer Impresa Sociale s.r.l., 52100 Arezzo, ItalyCIRA, Italian Aerospace Research Centre, 81043 Capua, Caserta, ItalyGeoExplorer Impresa Sociale s.r.l., 52100 Arezzo, ItalyDepartment of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalyDepartment of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalyCorso Giuseppe Garibaldi 119, Comacchio, 44022 Ferrara, ItalyLe Due Valli s.r.l., Ostellato, 44020 Ferrara, ItalyDepartment of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalyLe Due Valli s.r.l., Ostellato, 44020 Ferrara, ItalyDepartment of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalyGruppo Filippetti Sede Falconara Marittima, 60015 Falconara Marittima, Ancona, ItalyDepartment of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalyDepartment of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalySoil 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<sup>2</sup> 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.https://www.mdpi.com/2072-4292/14/15/3814airborne gamma-ray spectroscopynon-linear machine learningpotassiumclaythoriumsand |
| spellingShingle | 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 Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture Remote Sensing airborne gamma-ray spectroscopy non-linear machine learning potassium clay thorium sand |
| title | Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture |
| title_full | Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture |
| title_fullStr | Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture |
| title_full_unstemmed | Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture |
| title_short | Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture |
| title_sort | airborne radiometric surveys and machine learning algorithms for revealing soil texture |
| topic | airborne gamma-ray spectroscopy non-linear machine learning potassium clay thorium sand |
| url | https://www.mdpi.com/2072-4292/14/15/3814 |
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