Cargando…

Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra

Sustainable land management requires reliable information about soil hydraulic properties. Among these properties, available water-holding capacity (AWC) is a key attribute, as it quantifies the amount of water available for plants that the soil can hold. Since direct measurements of AWC are costly,...

Descripción completa

Detalles Bibliográficos
Autores principales: Blaschek, Michael, Roudier, Pierre, Poggio, Matteo, Hedley, Carolyn B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6731310/
https://www.ncbi.nlm.nih.gov/pubmed/31492888
http://dx.doi.org/10.1038/s41598-019-49226-6
_version_ 1783449665133346816
author Blaschek, Michael
Roudier, Pierre
Poggio, Matteo
Hedley, Carolyn B.
author_facet Blaschek, Michael
Roudier, Pierre
Poggio, Matteo
Hedley, Carolyn B.
author_sort Blaschek, Michael
collection PubMed
description Sustainable land management requires reliable information about soil hydraulic properties. Among these properties, available water-holding capacity (AWC) is a key attribute, as it quantifies the amount of water available for plants that the soil can hold. Since direct measurements of AWC are costly, pedotransfer functions (PTF) are often used to estimate AWC, leveraging statistical relationships with properties that are easier to measure, such as texture, bulk density, and organic carbon content. This study evaluates visible near-infrared spectroscopy (vis-NIR) as an alternative approach to predict volumetric water content at field capacity (FC) and permanent wilting point (PWP) — AWC being the difference between PWP and FC. A suite of 970 vis-NIR soil spectra, recorded from air-dried, 2-mm, sieved soil samples, were associated with FC and PWP analytical data obtained from New Zealand’s National Soils Database. Partial least squares (PLS) regression and support vector machines on PLS latent variables (PLS-SVM) were used for spectroscopic modelling. With root mean squared errors below 7% and 5% for FC and PWP, respectively, our results indicate that vis-NIR spectroscopy can be used to quantitatively predict volumetric water content at FC and PWP.
format Online
Article
Text
id pubmed-6731310
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-67313102019-09-18 Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra Blaschek, Michael Roudier, Pierre Poggio, Matteo Hedley, Carolyn B. Sci Rep Article Sustainable land management requires reliable information about soil hydraulic properties. Among these properties, available water-holding capacity (AWC) is a key attribute, as it quantifies the amount of water available for plants that the soil can hold. Since direct measurements of AWC are costly, pedotransfer functions (PTF) are often used to estimate AWC, leveraging statistical relationships with properties that are easier to measure, such as texture, bulk density, and organic carbon content. This study evaluates visible near-infrared spectroscopy (vis-NIR) as an alternative approach to predict volumetric water content at field capacity (FC) and permanent wilting point (PWP) — AWC being the difference between PWP and FC. A suite of 970 vis-NIR soil spectra, recorded from air-dried, 2-mm, sieved soil samples, were associated with FC and PWP analytical data obtained from New Zealand’s National Soils Database. Partial least squares (PLS) regression and support vector machines on PLS latent variables (PLS-SVM) were used for spectroscopic modelling. With root mean squared errors below 7% and 5% for FC and PWP, respectively, our results indicate that vis-NIR spectroscopy can be used to quantitatively predict volumetric water content at FC and PWP. Nature Publishing Group UK 2019-09-06 /pmc/articles/PMC6731310/ /pubmed/31492888 http://dx.doi.org/10.1038/s41598-019-49226-6 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Blaschek, Michael
Roudier, Pierre
Poggio, Matteo
Hedley, Carolyn B.
Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra
title Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra
title_full Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra
title_fullStr Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra
title_full_unstemmed Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra
title_short Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra
title_sort prediction of soil available water-holding capacity from visible near-infrared reflectance spectra
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6731310/
https://www.ncbi.nlm.nih.gov/pubmed/31492888
http://dx.doi.org/10.1038/s41598-019-49226-6
work_keys_str_mv AT blaschekmichael predictionofsoilavailablewaterholdingcapacityfromvisiblenearinfraredreflectancespectra
AT roudierpierre predictionofsoilavailablewaterholdingcapacityfromvisiblenearinfraredreflectancespectra
AT poggiomatteo predictionofsoilavailablewaterholdingcapacityfromvisiblenearinfraredreflectancespectra
AT hedleycarolynb predictionofsoilavailablewaterholdingcapacityfromvisiblenearinfraredreflectancespectra