Visible near-infrared spectroscopy and pedotransfer function well predict soil sorption coefficient of glyphosate
The soil sorption coefficient (K-d) of glyphosate mainly controls its transport and fate in the environment. Laboratory-based analysis of K-d is laborious and expensive. This study aimed to test the feasibility of visible near-infrared spectroscopy (vis-NIRS) as an alternative method for glyphosate...
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2023
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ftunivgent:oai:archive.ugent.be:01H258CNSST3T993JT07HJPH5M 2024-05-19T07:41:27+00:00 Visible near-infrared spectroscopy and pedotransfer function well predict soil sorption coefficient of glyphosate Akter, Sonia de Jonge, Lis Wollesen Møldrup, Per Greve, Mogens Humlekrog Nørgaard, Trine Weber, Peter Lystbæk Hermansen, Cecilie Mouazen, Abdul Knadel, Maria 2023 application/pdf https://biblio.ugent.be/publication/01H258CNSST3T993JT07HJPH5M http://hdl.handle.net/1854/LU-01H258CNSST3T993JT07HJPH5M https://doi.org/10.3390/rs15061712 https://biblio.ugent.be/publication/01H258CNSST3T993JT07HJPH5M/file/01H258G9E402ZMT2N0QT26K3ZK eng eng https://biblio.ugent.be/publication/01H258CNSST3T993JT07HJPH5M http://hdl.handle.net/1854/LU-01H258CNSST3T993JT07HJPH5M http://doi.org/10.3390/rs15061712 https://biblio.ugent.be/publication/01H258CNSST3T993JT07HJPH5M/file/01H258G9E402ZMT2N0QT26K3ZK Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) info:eu-repo/semantics/openAccess REMOTE SENSING ISSN: 2072-4292 Earth and Environmental Sciences visible near-infrared spectroscopy alternative to wet chemistry analysis modeling multivariate data analysis pesticide sorption soil contaminant DIFFUSE-REFLECTANCE SPECTROSCOPY ORGANIC-MATTER ADSORPTION MOBILITY OXIDES LIMITATIONS DESORPTION PHOSPHATE MINERALS IRON journalArticle info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2023 ftunivgent https://doi.org/10.3390/rs15061712 2024-04-30T23:30:59Z The soil sorption coefficient (K-d) of glyphosate mainly controls its transport and fate in the environment. Laboratory-based analysis of K-d is laborious and expensive. This study aimed to test the feasibility of visible near-infrared spectroscopy (vis-NIRS) as an alternative method for glyphosate K-d estimation at a country scale and compare its accuracy against pedotransfer function (PTF). A total of 439 soils with a wide range of K-d values (37-2409 L kg(-1)) were collected from Denmark (DK) and southwest Greenland (GR). Two modeling scenarios were considered to predict K-d: a combined model developed on DK and GR samples and individual models developed on either DK or GR samples. Partial least squares regression (PLSR) and artificial neural network (ANN) techniques were applied to develop vis-NIRS models. Results from the best technique were validated using a prediction set and compared with PTF for each scenario. The PTFs were built with soil texture, OC, pH, Fe-ox, and P-ox. The ratio of performance to interquartile distance (RPIQ) was 1.88, 1.70, and 1.50 for the combined (ANN), DK (ANN), and GR (PLSR) validation models, respectively. vis-NIRS obtained higher predictive ability for K-d than PTFs for the combined dataset, whereas PTF resulted in slightly better estimations of K-d on the DK and GR samples. However, the differences in prediction accuracy between vis-NIRS and PTF were statistically insignificant. Considering the multiple advantages of vis-NIRS, e.g., being rapid and non-destructive, it can provide a faster and easier alternative to PTF for estimating glyphosate K-d. Article in Journal/Newspaper Greenland Ghent University Academic Bibliography Remote Sensing 15 6 1712 |
institution |
Open Polar |
collection |
Ghent University Academic Bibliography |
op_collection_id |
ftunivgent |
language |
English |
topic |
Earth and Environmental Sciences visible near-infrared spectroscopy alternative to wet chemistry analysis modeling multivariate data analysis pesticide sorption soil contaminant DIFFUSE-REFLECTANCE SPECTROSCOPY ORGANIC-MATTER ADSORPTION MOBILITY OXIDES LIMITATIONS DESORPTION PHOSPHATE MINERALS IRON |
spellingShingle |
Earth and Environmental Sciences visible near-infrared spectroscopy alternative to wet chemistry analysis modeling multivariate data analysis pesticide sorption soil contaminant DIFFUSE-REFLECTANCE SPECTROSCOPY ORGANIC-MATTER ADSORPTION MOBILITY OXIDES LIMITATIONS DESORPTION PHOSPHATE MINERALS IRON Akter, Sonia de Jonge, Lis Wollesen Møldrup, Per Greve, Mogens Humlekrog Nørgaard, Trine Weber, Peter Lystbæk Hermansen, Cecilie Mouazen, Abdul Knadel, Maria Visible near-infrared spectroscopy and pedotransfer function well predict soil sorption coefficient of glyphosate |
topic_facet |
Earth and Environmental Sciences visible near-infrared spectroscopy alternative to wet chemistry analysis modeling multivariate data analysis pesticide sorption soil contaminant DIFFUSE-REFLECTANCE SPECTROSCOPY ORGANIC-MATTER ADSORPTION MOBILITY OXIDES LIMITATIONS DESORPTION PHOSPHATE MINERALS IRON |
description |
The soil sorption coefficient (K-d) of glyphosate mainly controls its transport and fate in the environment. Laboratory-based analysis of K-d is laborious and expensive. This study aimed to test the feasibility of visible near-infrared spectroscopy (vis-NIRS) as an alternative method for glyphosate K-d estimation at a country scale and compare its accuracy against pedotransfer function (PTF). A total of 439 soils with a wide range of K-d values (37-2409 L kg(-1)) were collected from Denmark (DK) and southwest Greenland (GR). Two modeling scenarios were considered to predict K-d: a combined model developed on DK and GR samples and individual models developed on either DK or GR samples. Partial least squares regression (PLSR) and artificial neural network (ANN) techniques were applied to develop vis-NIRS models. Results from the best technique were validated using a prediction set and compared with PTF for each scenario. The PTFs were built with soil texture, OC, pH, Fe-ox, and P-ox. The ratio of performance to interquartile distance (RPIQ) was 1.88, 1.70, and 1.50 for the combined (ANN), DK (ANN), and GR (PLSR) validation models, respectively. vis-NIRS obtained higher predictive ability for K-d than PTFs for the combined dataset, whereas PTF resulted in slightly better estimations of K-d on the DK and GR samples. However, the differences in prediction accuracy between vis-NIRS and PTF were statistically insignificant. Considering the multiple advantages of vis-NIRS, e.g., being rapid and non-destructive, it can provide a faster and easier alternative to PTF for estimating glyphosate K-d. |
format |
Article in Journal/Newspaper |
author |
Akter, Sonia de Jonge, Lis Wollesen Møldrup, Per Greve, Mogens Humlekrog Nørgaard, Trine Weber, Peter Lystbæk Hermansen, Cecilie Mouazen, Abdul Knadel, Maria |
author_facet |
Akter, Sonia de Jonge, Lis Wollesen Møldrup, Per Greve, Mogens Humlekrog Nørgaard, Trine Weber, Peter Lystbæk Hermansen, Cecilie Mouazen, Abdul Knadel, Maria |
author_sort |
Akter, Sonia |
title |
Visible near-infrared spectroscopy and pedotransfer function well predict soil sorption coefficient of glyphosate |
title_short |
Visible near-infrared spectroscopy and pedotransfer function well predict soil sorption coefficient of glyphosate |
title_full |
Visible near-infrared spectroscopy and pedotransfer function well predict soil sorption coefficient of glyphosate |
title_fullStr |
Visible near-infrared spectroscopy and pedotransfer function well predict soil sorption coefficient of glyphosate |
title_full_unstemmed |
Visible near-infrared spectroscopy and pedotransfer function well predict soil sorption coefficient of glyphosate |
title_sort |
visible near-infrared spectroscopy and pedotransfer function well predict soil sorption coefficient of glyphosate |
publishDate |
2023 |
url |
https://biblio.ugent.be/publication/01H258CNSST3T993JT07HJPH5M http://hdl.handle.net/1854/LU-01H258CNSST3T993JT07HJPH5M https://doi.org/10.3390/rs15061712 https://biblio.ugent.be/publication/01H258CNSST3T993JT07HJPH5M/file/01H258G9E402ZMT2N0QT26K3ZK |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
REMOTE SENSING ISSN: 2072-4292 |
op_relation |
https://biblio.ugent.be/publication/01H258CNSST3T993JT07HJPH5M http://hdl.handle.net/1854/LU-01H258CNSST3T993JT07HJPH5M http://doi.org/10.3390/rs15061712 https://biblio.ugent.be/publication/01H258CNSST3T993JT07HJPH5M/file/01H258G9E402ZMT2N0QT26K3ZK |
op_rights |
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/rs15061712 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
6 |
container_start_page |
1712 |
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1799481049488556032 |