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

Michael Blaschek,Matteo Poggio,Carolyn B. Hedley,Pierre Roudier

doi:10.1038/s41598-019-49226-6

Michael Blaschek, Matteo Poggio + Show 2 more

Open Access

https://doi.org/10.1038/s41598-019-49226-6

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Abstract

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.

Highlights

Soil data of adequate quality and reasonable quantity is critically important for sustainable land management
The recorded spectrum is used to derive a statistical model which relates a soil property to the spectral information. Such spectroscopic models have been derived for a range of soil attributes[6,7], especially those related to organic molecules or water retention, such as soil organic carbon[8,9,10], cation exchange capacity (CEC)[11] and clay content[12,13], as well as soil bulk density[14,15] and saturated soil hydraulic conductivity[16,17]
Partial least squares (PLS) models were calibrated for field capacity (FC) and permanent wilting point (PWP) showing optimal numbers of latent variables of 16 and 18, respectively

Summary

Introduction

Soil data of adequate quality and reasonable quantity is critically important for sustainable land management. Arslan et al.[19] used visible and near-infrared (vis-NIR) spectroscopy to predict FC and PWP as well as particle size (clay, sand and silt content) for 305 soil samples collected within a study area of 8,000 ha in the Bafra plain, Turkey. They compared a number of different regression models, and concluded that multiple regression models provided best prediction results. They recommend, instead, that MIR be used in conjunction with PTF to improve moisture retention predictions

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