VISIBLE AND NEAR-INFRARED (0.4-1.1 MU-M) ANALYSIS OF ARID AND SEMIARID SOILS

Near-infrared analysis (NIRA) methodology was applied to the reflectance spectra of arid and semiarid soils in the visible and near infrared (VIS-NIR; 0.4-1.1 mum) spectral region. The method is termed visible and near-infrared analysis (VNIRA). Although the spectra of the soils were characterized as monotonous and featureless, the methodology has yielded a prediction equation for estimating several soil constituents from the reflectance curves. The constituents were: CaCO3, Fe2O3, Al2O3, SiO2, LOI (lost-on-ignition), Fed (free iron oxides), and K2O. Several mathematic manipulations were applied to the raw data in order to derive the optimal prediction equation. Spectral compression into 6, 8, 15, 71, and 350 spectral bands and a spectral derivation technique were applied to four separate soil groups, which were selected on the basis of their chemical characteristics. The wave-lengths selected by the method for the optimal prediction equation were assigned to constituents that consisted of spectral features within the VIS-NIR region. An intercorrelation between spectrally featureless constituents and constituents with special features was found to be the major mechanism by which to predict constituents that had no spectral features within this part of the spectrum. It was shown that low spectral resolution is not necessarily a limiting factor in obtaining quantitative information about the chemistry of soil samples. All the examined soil constituents except Fed (which needed 700 spectral bands) required between 15 and 350 spectral bands for optimal prediction. It was concluded that the VIS-NIR (0.4-1.1 mum) is a suitable spectral region for obtaining quantitative information about soil chemistry. Although the VNIRA performance is not as precise as the chemical performance, the precision obtained is likely to be useful for rapid soil characterization and remote-sensing applications. We strongly recommend the use of both the VNIRA and the NIRA methods to better interpret high resolution remote-sensing data.