Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation. Issue 1 (27th February 2020)

• Record Type: Journal Article
• Authors: Knadel, Maria; de Jonge, Lis Wollesen; Tuller, Markus; Rehman, Hafeez Ur; Jensen, Peter Weber; Moldrup, Per; Greve, Mogens H.; Arthur, Emmanuel
• Is Part Of: Vadose zone journal. Volume 19:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-27
• Subjects: Soil science -- Periodicals; Zone of aeration -- Periodicals; Groundwater flow -- Periodicals; Groundwater flow; Zone of aeration; Periodicals; Electronic journals; 631.4
• DOI: 10.1002/vzj2.20007
• Languages: English
• ISSNs: 1539-1663

Abstract

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. Core Ideas: A new method to estimate SSA by combining vis‐NIRS and two WSI models is proposed. The vis‐NIRS models are also compared with models where the SSA is determined with the EGME method. Three types of regression techniques including PLS, SVM, and ANN are tested. The combination of vis‐NIRS with the WSI as a reference provides SSA estimates similar to the EGME method.