Historically, our understanding of the soil and assessment of its quality and function has been gained through field survey and routine soil physicochemical laboratory analysis. Reflectance spectroscopy can be used to non-destructively characterize materials for a wide range of applications. Hyperspectral remote sensing data provide a rich source of information produced in the form of the spectrum which can be used to identify surface materials. In this study, Field Portable Hyperspectral Radiometer (FPHR) was evaluated in an attempt for prediction of diverse soil properties related to three different soil orders (Vertisols, Aridisols, and Entisols) across Lower Egypt. Eight pedons consisting of 34 samples were collected from different semi-arid areas.
Soil horizonation and twelve soil attributes including clay, sand, silt, SOC, pH, EC, A.W, gypsum, CaCO3, Fe2O3, Al2O3, and SiO2 were traditionally analyzed and then correlated with spectral reflectance of the spectrum range. Four bands (blue, green, red, and near-infrared) were calculated for prediction of these variables. The results showed that the variations in spectral reflectance for each horizon across the spectrum range (276-1093 nm) were matched well with those of morphologically described horizons in the field. Additionally, the correlation results of different soil variables were highly correlated with spectral reflectance at different band wavelengths. For example, clay content correlated negatively (r = -0.93) with reflectance at the green band while silt (r = 0.67 at the blue band) and sand (0.87 at the green band) correlated positively. Regression equations were fitted in graphs to attempt the quantification of the soil constituents from their reflectance values. The best predictive models were obtained for clay content (R2 = 0.93), SiO2 (R2 = 0.86), Al2O3 (R2 = 0.85), A.W. (R2 = 0.79), CaCO3 (R2 = 0.79), gypsum (R2 = 0.75), Fe2O3 (R2 = 0.71), sand (R2 = 0.69), silt (R2 = 0.54), and SOC (R2 = 0.51) while the poor prediction was for EC and pH. The results concluded that the spectral reflectance of the spectrum had the potential to differentiate the soil horizonation and to predict the selected soil variable at different wavelength bands.
Conclusively, FPHR was shown to be an effective tool for enhanced soil horizon differentiation and the acquisition of soil attributes information.