Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering. O’Leary, D., Brogi, C., Brown, C., Tuohy, P., & Daly, E. Frontiers in Soil Science, 4:1346028, February, 2024. ![Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Introduction The mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties. Methods Multi-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site. Results and discussion The method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.
@article{oleary_linking_2024,
title = {Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering},
volume = {4},
issn = {2673-8619},
url = {https://www.frontiersin.org/articles/10.3389/fsoil.2024.1346028/full},
doi = {10.3389/fsoil.2024.1346028},
abstract = {Introduction
The mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties.
Methods
Multi-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.
Results and discussion
The method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.},
urldate = {2024-11-26},
journal = {Frontiers in Soil Science},
author = {O’Leary, Dave and Brogi, Cosimo and Brown, Colin and Tuohy, Pat and Daly, Eve},
month = feb,
year = {2024},
pages = {1346028},
}
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Methods Multi-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site. Results and discussion The method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.","urldate":"2024-11-26","journal":"Frontiers in Soil Science","author":[{"propositions":[],"lastnames":["O’Leary"],"firstnames":["Dave"],"suffixes":[]},{"propositions":[],"lastnames":["Brogi"],"firstnames":["Cosimo"],"suffixes":[]},{"propositions":[],"lastnames":["Brown"],"firstnames":["Colin"],"suffixes":[]},{"propositions":[],"lastnames":["Tuohy"],"firstnames":["Pat"],"suffixes":[]},{"propositions":[],"lastnames":["Daly"],"firstnames":["Eve"],"suffixes":[]}],"month":"February","year":"2024","pages":"1346028","bibtex":"@article{oleary_linking_2024,\n\ttitle = {Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering},\n\tvolume = {4},\n\tissn = {2673-8619},\n\turl = {https://www.frontiersin.org/articles/10.3389/fsoil.2024.1346028/full},\n\tdoi = {10.3389/fsoil.2024.1346028},\n\tabstract = {Introduction\n The mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties.\n \n \n Methods\n Multi-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.\n \n \n Results and discussion\n The method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.},\n\turldate = {2024-11-26},\n\tjournal = {Frontiers in Soil Science},\n\tauthor = {O’Leary, Dave and Brogi, Cosimo and Brown, Colin and Tuohy, Pat and Daly, Eve},\n\tmonth = feb,\n\tyear = {2024},\n\tpages = {1346028},\n}\n\n\n\n","author_short":["O’Leary, D.","Brogi, C.","Brown, C.","Tuohy, P.","Daly, E."],"key":"oleary_linking_2024","id":"oleary_linking_2024","bibbaseid":"oleary-brogi-brown-tuohy-daly-linkingelectromagneticinductiondatatosoilpropertiesatfieldscaleaidedbyneuralnetworkclustering-2024","role":"author","urls":{"Paper":"https://www.frontiersin.org/articles/10.3389/fsoil.2024.1346028/full"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/tereno","dataSources":["cq3J5xX6zmBvc2TQC"],"keywords":[],"search_terms":["linking","electromagnetic","induction","data","soil","properties","field","scale","aided","neural","network","clustering","o’leary","brogi","brown","tuohy","daly"],"title":"Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering","year":2024}