The Use of Hyperspectral Visible and Near Infrared Reflectance Spectroscopy for the Characterization of Salt-Affected Soils in the Harran Plain, Turkey

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

“…For soil salinity, the SWIR bands (i.e., Bands 9, 8 and 7) and NIR (i.e., Band 3) had the highest contribution to estimating soil salinity (Figure 4a). These results were consistent with the results of many studies on spectral wavebands and their relationships with soil salinity [26,74,86]. For instance, Farifteh et al [26] reported that the best performing bands for different scales (field, image and experimental) were found in the NIR and SWIR regions of the spectrum.…”

“…For soil salinity, MARS achieved high performance (R 2 = 0.85), better than the PLSR output (R 2 = 0.80), exceeding also the results acquired by Bilgili et al [54,74] based on laboratory spectra (R 2 = 0.39 and 0.77, respectively), Nawar et al [58], who used laboratory spectra and Landsat data (R 2 = 0.73), Shamsi et al [92] based on MODIS data (R 2 = 0.39) and Allbed et al [93], who tested IKONOS data (R 2 = 0.65). Excellent predictions for clay content were obtained in this study by both the MARS and PLSR models.…”

“…Recently, MARS was applied as a regression method in several disciplines [54,59,[72][73][74] and was generally reported to perform better than conventional statistical methods. MARS uses basis functions to model the predictor and response variables by utilizing adaptive piecewise linear regressions; i.e., the non-linearity of a model is simulated through the use of separate linear regression slopes in definite intervals of the response variable space [75].…”

Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS)

“…Developed by Friedman [26], MARS is a non-parametric regression technique used for fitting the relationship between dependent and independent variables via application of the splines theory. Recently, MARS was applied as a regression method in several disciplines [28,29,32,48,49] and was consistently noted to perform better than traditional statistical methods. The MARS analysis uses basis functions to model the predictor and response variables [50].…”

“…Multivariate adaptive regression splines (MARS), typically known as a nonparametric method that estimates complex nonlinear relationships among independent and dependent variables [26], has been effectively applied in different fields [27][28][29][30] and generally exhibits high-performance results compared with other linear and non-parametric regression models, such as principal component regression (PCR) and artificial neural networks (ANN) [31]. Bilgili et al [28,32] used MARS to model soil salinity and reported that MARS provided better estimations for the soil ECe than the generally used PLSR method, yielding the best cross-validation R 2 and RPD values for air-dried soils. These results were confirmed by Nawar et al [33], who modelled soil salinity using MARS and obtained similarly high R 2 and RPD values (0.81 and 2.3, respectively).…”