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The present study assessed the visible and short wave infrared (VNIR-SWIR) laboratory spectroscopy coupled random forest regression (RF) technique for predicting soil properties in the southern Karnataka Plateau, India. The spectral data acquired for about 228 profile samples were used to predict key soil properties. The RF model fits well for the spectral prediction of clay (R 2  = 0.65), sand (R 2  = 0.60), cation exchange capacity (R 2  = 0.74), field capacity (R 2  = 0.65) and permanent wilting point (R 2  = 0.72). Wherein soil organic carbon was poorly predicted with an R 2  of 0.22 and RPD of 1.2 due to its lower content and narrow range (0.8 to 20 g kg −1 ). The spectral assessment by PCA showed that the first (50%) and third (34%) components had high spectral variation and significantly correlated with soil properties such as pH, CEC, clay, FC, and PWP related to wavelengths indicating clay minerals and iron oxides. However, the second component had less spectral variation (13%) that is related to wavelengths indicating various organic components and correlated well with SOC. Thus, the VNIR-SWIR spectroscopy could be a suitable supplementary method for rapidly predicting soil properties related to clay minerals and iron oxides.

Spectral assessment of soil properties in semi-arid tropical regions of southern Karnataka Plateau