Soil Specific Surface Area Determination by Visible Near‐Infrared Spectroscopy

Abstract

The soil specific surface area (SSA) affects soil physical and chemical properties. Numerous studies applied visible near-infrared spectroscopy (Vis-NIRS) to estimate clay content (particles < 2 µm). Since SSA is better defined and more directly related to particle size distribution and mineralogy than clay content, predictions of SSA from Vis-NIRS are expected to be better than that for clay. Thus, the aims of this study were to (i) test the feasibility of using Vis-NIRS for SSA determination, (ii) compare the predictive ability of Partial Least Squares (PLS) model of SSA with that of clay, (iii) identify important wavelengths using interval Partial Least Squares (iPLS) regression, and to test if the application of iPLS improves the predictive ability of the models. A total of 550 soil samples with a wide range in SSA (3-437 m 2 g-1) and clay content (1-83%) was divided into a calibration and a validation set. The PLS models had similar predictive ability for SSA (ratio of performance to interquartile range, RPIQ = 1.7) and clay content (RPIQ = 1.6). utilizing iPLS led to only limited improvement in the prediction accuracy (RPIQ of 1.8 and 1.7 for SSA and clay content, respectively), yet decreased the number of relevant wavelengths and indicated a higher specificity of SSA over the broader spectral response of clay. The important wavelengths for SSA and clay predictions were indicative of the organo-mineral content and its interactions, including spectral response from not only iron oxides and minerals but also organic matter due to masking effect of the non-complexed organic carbon on the mineral phases of some of the soils. Abbreviations: EGME, ethylene glycol monoethyl ether; iPLS, interval partial least squares; OM, organic matter; PC, principal component; PCA, principal component analysis; PLS, partial least squares; RPIQ, ratio of performance to interquartile range; RMSEP, root mean squared error of prediction; SOC, soil organic carbon; SSA, soil specific surface area; vis-NIRS, visible near-infrared spectroscopy. T he soil specific surface area (SSA) is a basic soil property and indicates the surface area per unit mass of soil. It affects physical and chemical properties of the soil and is important for processes such as water retention and movement, ion exchange reactions, contaminant adsorption, microbial attachment, nutrient dynamics, soil aggregation, and irrigation management (Pennell, 2002). The SSA can vary greatly depending on the soil type and the method used for its determination. Different soils present differences in mineralogical and organic composition as well as in particle-size distribution resulting in large variations in the amount of available reactive surfaces. For instance, soils rich in clay and organic matter can have SSA of up to 8 × 10 5 m 2 kg-1 , whereas the SSA for sandy soils can be as low as 1 × 10 3 m 2 kg-1 (Pennell, 2002). Due to the importance of SSA, there is a worldwide interest and need for its accurate determination. Although SSA is measured routinely by scientists, a...

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

“…High R 2 values (>.89) were obtained for all SSA estimations. The SSA EGME validation results exhibit higher accuracy than obtained in Knadel et al, 2018 (SRMSE = 0.13), who validated with a set representing a smaller range of SSA EGME values (4-116 m 2 g −1 ), but also lower SD values (SD = 27) than the validation set used in this study (range: 10-392 m 2 g −1 , SD = 94) ( Table 1).…”

“…However, the application of vis-NIRS to SSA determination is still relatively rare. The few successful attempts to determine the SSA from vis-NIR spectra included the predictions of SSA obtained from the EGME method only (Ben-Dor & Banin, 1995;Ben-Dor, Heller, & Chudnovsky, 2008;Knadel et al, 2018).…”

“…Moreover, high sand content increases light scattering and was reported to have a negative effect on model performance of SOC (Stenberg, 2010;Stevens et al, 2013). Knadel et al (2018) showed that, aside from the differences in texture and SOC content, the complexation status of SOC also affects the vis-NIRS estimation of SSA. The subset with SSA EGME values > 100 m 2 g −1 represents soils with the capacity of clay to complex SOC (clay/SOC ratio, defined by Dexter et al, 2008, as n = 10) with n values > 10 (Supplemental Figure S5), meaning that soils unsaturated with SOC are present and all SOC is in complexed form.…”

Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2

“…Among these approaches, promising techniques such as spectroscopy in the visible (VIS) and near-infrared (NIR) spectroscopy have been used as proximal sensing techniques and enable the measurement of texture. Indeed, soil components are sensitive to these portions of the electromagnetic spectrum, which allow the assessment of soil textural composition [9]. Curcio et al [10] have predicted soil texture (sand, clay, and silt) using VNIR-SWIR band reflectance spectroscopy.…”

Combining Visible−Near‐Infrared and Pedotransfer Functions for Parameterization of Tile Drain Flow Simulations

“…The required preparation for this method is simple, involving only air‐drying of the soil and sieving down to 2 mm. Visible near‐infrared spectroscopy has been successfully used to predict soil properties such as the complete soil texture distribution (Hermansen et al, 2017), soil structure (Katuwal et al, 2018), and soil specific surface area (Ben‐Dor et al, 2008; Knadel et al, 2018). Furthermore, studies have shown its ability to predict the wet part of the soil‐water retention curve (Babaeian et al, 2015; Pittaki‐Chrysodonta et al, 2018; Santra et al, 2009).…”