Modeling soil cation concentration and sodium adsorption ratio using observed diffuse reflectance spectra

Abstract

12Spectral analysis is a useful tool for the rapid and accurate prediction of soil properties. Our ]) and sodium adsorption ratio (SAR). Three methods were applied, i.e., stepwise multiple 15 linear regression (SMLR), partial least squares regression (PLSR), and support vector machine 16 (SVM). Estimation models for four soil properties were developed using three different spectral 17 processing and transformation techniques, i.e., reflectance (R e ), logarithm of reciprocal R e (LR), and 18 standard normal variable of R e (SNV) were used. A total of 36 models were established. Of these, (RPD) were all smaller than the 1.4 threshold. However, the models for SAR~R using PLSR (R The prediction of the physico-chemical properties of degraded soils is very important for crop 37 growth, low quality land improvement, and environmental sustainability (Childs and Hank, 1975; 38 Metternicht and Zinck, 2003 method to quantitatively characterize soil chemical properties (Ben-Dor et al., 1995; Aldabaa et al., 48 2015), and offers both cost and statistical power advantages (Cohen et al., 2005). Reflectance 49 spectroscopy analysis of ground objects is a good means of predicting soil properties, and of different soil properties (Islam et al., 2003; Davies, 2005; Cambule et al., 2012). 52There have been a number of studies relating soil physico-chemical properties to 53 reflectance-related features (Mahesh et al., 2014; Kim et al., 2014). Lü et al. (2013) and other physical-chemical properties (Li et al. 2012; Zhang et al., 2016). 63Since reflectance data are numerous and complex, the modeling approaches are crucial to 64 achieve an accurate estimate of soil properties. Zhang and Li (2016) conducted in the region, and has achieved good results in improving soil quality. 117The soils in the region were characterized as typical grey desert soils (Luo, 1985). The samples 118were collected from the 22nd of April to the 15th of May, 2013. Plant residues present on the soil 119 surface were carefully removed before sampling. A total of 78 samples were randomly collected 120 from within a 2 to 3 km range. The sampling depth was from 0 -10 cm, and the sampling location 121 is shown in Figure 1.Each soil sample was put into a cloth bag and transported to the laboratory. All of the soil 124 samples were air-dried, ground, and passed through a 2 mm sieve. The soil moisture of all the the R e data (Buddenbaum and Steffens, 2012). LR is used to reduce the effects of noise, light, and 153 background on R e . SNV is adopted to eliminate the effects of solid particle size and optical path 154 change of surface scattering on R e . SNV is calculated by (Barnes et al., 1989):where µ is mean value of R e , and σ is standard deviation of R e . 157 Model establishment and validation 158Soil samples were randomly divided into a calibration set for model establishment, and a 171The model establishment using the SMLR method was performed using SPSS 17.0 software, The leave-one-out cross-validation was chosen as the validatio...

Peer Review #1 of "Quantitative estimation of wastewater quality parameters by hyperspectral band screening using GC, VIP and SPA (v0.1)"

“…In general, the external noise can be eliminated to some degree with such effective pretreatments as resampling, smoothing and transformation, which can improve the spectral characteristics (Ding et al, 2018). Therefore, two steps were adopted to pretreat the R raw : (1) removing the marginal wavelengths (2,401-2,500nm and 350-399nm ) of higher noise in each water sample, then smoothing the remaining spectrum data through filter method (polynomial order = 2; window size = 5) of Savitzky-Golay (SG) (Savitzky&Golay,1964) by Origin Pro software (2017SR2 version); and (2) obtaining the precise R raw−SNV using the standard normal variable (SNV) to remove the effects of baseline shift and surface scattering on the spectral data (Xiao et al, 2016). The spectral curves of R raw and R raw−SNV are shown in Figs.…”

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“…A new spectral curve consisting of 200 wave bands was obtained. iii) The precise R raw-SNV was obtained by using standard normal variable (SNV) to eliminate the effects of soil particle size, surface scattering and baseline shift on the spectrum data (Xiao et al, 2016b;Barnes et al, 1989). The spectral curves of R raw and R raw-SNV are shown in Fig.…”

“…During the modeling in this study, the type of SVR and kernel were set as epsilon-SVR and linear function, respectively; the penalty parameter C and nuclear parameter g were acquired by a grid-searching technique and a leave-one-out cross validation procedure. The optimal values of C and g were selected when the minimum RMSE CV (root mean squared error of cross validation) was produced (Xiao et al 2016b). The two models were constructed and validated using the Unscrambler software version X10.4 (CAMO AS Oslo, Norway)…”

“…The spectral response characteristics of midinfrared (MIR) spectroscopy are better than those of VIS-NIR spectroscopy in predicting soil salinity information, the latter has high predicting accuracy of the total salts content, HCO 3 -, SO 4 2and Ca 2+ , followed by Mg 2+ , Cland Na + . The spectral models have satisfactory prediction of the SAR (sodium absorption ratio) of soil salinization evaluation parameter, which is composed of the contents of Ca 2+ , Mg 2+ and Na + (Xiao et al 2016b). Qu et al (2009) found that the contents of the total salt, SO 4 2-, pH and K + +Na + have a higher inversion accuracy using spectral data to create PLSR model.…”

“…In particular, support vector regressions (SVR) based on kernel-based learning methods has the ability to handle nonlinear analysis case with high model accuracy (Vapnik, 1995;Hong et al 2018b). Over the past several decades, the use of SVR for classification and regression has been extensively applied in soil VIS-NIR spectroscopy (Ben-Dor, 2002;Xiao et al 2016b;Hong et al 2018a). Moreover, the SVR model works well in estimating the contents of K + , Na + , Ca 2+ and SO 4 2in the soil (Wang et al 2018a).…”