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Mogens Humlekrog Greve

Combining visible near-infrared spectroscopy and water vapor sorption for soil specific surface area estimation

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Combining visible near-infrared spectroscopy and water vapor sorption for soil specific surface area estimation. / Knadel, Maria; de Jonge, Lis Wollesen; Tuller, Markus; Rehman, Hafeez Ur; Jensen, Peter Weber; Moldrup, Per; Greve, Mogens H.; Arthur, Emmanuel.

I: Vadose Zone Journal, Bind 19, Nr. 1, e20007, 2020.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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Bibtex

@article{01d57cafc8e549d19828355633434da2,
title = "Combining visible near-infrared spectroscopy and water vapor sorption for soil specific surface area estimation",
abstract = "The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near-infrared spectroscopy (vis-NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller–Or (TO) and the Guggenheim–Anderson–de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis-NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis-NIRS with the WSI as a reference technique for vis-NIRS models provides SSA estimations akin to the EGME method.",
author = "Maria Knadel and {de Jonge}, {Lis Wollesen} and Markus Tuller and Rehman, {Hafeez Ur} and Jensen, {Peter Weber} and Per Moldrup and Greve, {Mogens H.} and Emmanuel Arthur",
year = "2020",
doi = "10.1002/vzj2.20007",
language = "English",
volume = "19",
journal = "Vadose Zone Journal",
issn = "1539-1663",
publisher = "GeoScienceWorld",
number = "1",

}

RIS

TY - JOUR

T1 - Combining visible near-infrared spectroscopy and water vapor sorption for soil specific surface area estimation

AU - Knadel, Maria

AU - de Jonge, Lis Wollesen

AU - Tuller, Markus

AU - Rehman, Hafeez Ur

AU - Jensen, Peter Weber

AU - Moldrup, Per

AU - Greve, Mogens H.

AU - Arthur, Emmanuel

PY - 2020

Y1 - 2020

N2 - The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near-infrared spectroscopy (vis-NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller–Or (TO) and the Guggenheim–Anderson–de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis-NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis-NIRS with the WSI as a reference technique for vis-NIRS models provides SSA estimations akin to the EGME method.

AB - The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near-infrared spectroscopy (vis-NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller–Or (TO) and the Guggenheim–Anderson–de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis-NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis-NIRS with the WSI as a reference technique for vis-NIRS models provides SSA estimations akin to the EGME method.

UR - http://www.scopus.com/inward/record.url?scp=85080993369&partnerID=8YFLogxK

U2 - 10.1002/vzj2.20007

DO - 10.1002/vzj2.20007

M3 - Journal article

AN - SCOPUS:85080993369

VL - 19

JO - Vadose Zone Journal

JF - Vadose Zone Journal

SN - 1539-1663

IS - 1

M1 - e20007

ER -