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Estimating Soil Particle Density using Visible Near‐infrared Spectroscopy and a Simple, Two‐compartment Pedotransfer Function
Author(s) -
Marakkala Manage Lashya P.,
Katuwal Sheela,
Norgaard Trine,
Knadel Maria,
Moldrup Per,
Jonge Lis W.,
Greve Mogens Humlekrog
Publication year - 2019
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2018.06.0217
Subject(s) - pedotransfer function , soil water , soil texture , soil science , chemistry , soil test , bulk density , water content , soil organic matter , environmental science , analytical chemistry (journal) , environmental chemistry , geology , hydraulic conductivity , geotechnical engineering
Core Ideas The two‐compartment pedotransfer function successfully predicted soil particle density. Vis–NIR showed slightly poorer performance than the two‐compartment function for predicting soil ρ d . Spectroscopy or OM based pedotransfer models gave better estimates of ρ d when a wide range in soil OM data was used. The average particle density (ρ d ) is a fundamental soil property, used for calculating the total porosity. Traditional ρ d measurement by pycnometer method is tedious and time‐consuming. In this study, visible–near‐infrared (vis–NIR) spectroscopy and a simple two‐compartment linear and curvilinear pedotransfer function only requiring knowledge of soil organic matter content (OM) were tested and compared as alternative, indirect, rapid, and cost‐effective methods. Soil ρ d was measured by water pycnometer on 179 soils representing a wide range of OM (0.002–0.767 kg kg −1 ), whereas soil spectra were measured on air‐dry samples by vis–NIR spectroscopy. The ρ d models were developed using partial least squares regression with leave‐one‐out‐cross‐validation using vis–NIR spectral data, and a simple two‐compartment pedotransfer function, ρ d = A (OM) + B (1 − OM) using the OM content. Predictive abilities of these two methods were tested using three different datasets: (i) minerals soils (OM < 0.1 kg kg −1 ), (ii) organic soils (OM > 0.1 kg kg −1 ), and (iii) all soils. Calibrating the two‐compartment pedotransfer function for the entire dataset gave expected values for the individual particle densities of OM ( A = 1.244 g cm −3 ) and mineral particles ( B = 2.615 g cm −3 ). The vis–NIR spectroscopy model successfully predicted soil ρ d for the entire dataset ( R 2 = 0.87, RMSECV = 0.10 g cm −3 ), with a poorer performance than the two‐compartment linear model ( R 2 = 0.96, RMSE = 0.06 g cm −3 ). Using only the mineral soils data did not suffice to obtain realistic and accurate vis–NIR spectroscopy ( R 2 = 0.62, RMSECV = 0.02 g cm −3 ) or OM based ( R 2 = 0.80, RMSE = 0.02 g cm −3 ) models for ρ d , illustrating the importance of the wide range of OM content considered in the present study.