Hyperspectral imaging spectroscopy has facilitated the mapping of soil properties at large scales, but since the presence of photosynthetic or non-photosynthetic vegetation affects the reflectance spectra, soil properties mapping is limited to bare soil surfaces. This study analyzed the impact of bare soil pixel identification on clay content estimation using two methods (i) combination of two spectral indices, Normalized Difference Vegetation Index for identifying photosynthetic vegetation and Cellulose Absorption Index for non-photosynthetic vegetation and (ii) spectral unmixing for estimating fractions of soil, photosynthetic and non-photosynthetic vegetation. The study used AVIRIS-NG image and laboratory measured clay content of 272 soil samples acquired over Karnataka, India. Bare soil pixels were identified using the two methods and performances of partial least squares regression (PLSR) models used to estimate the clay contents and the predicted clay content maps were analysed and compared. PLSR model based on bare soil pixels identified by unmixing provided slightly better performances (R2val of 0.61) than spectral indices (R2val of 0.46), even though the percentage of study area mapped was reduced by half. This study highlighted that an improvement in prediction performance comes at the cost of reduction in spatial coverage in mapping of clay content.