Use of portable X-ray fluorescence spectrometry for classifying soils from different land use land cover systems in India

Abstract In this study, elemental data from portable X-ray fluorescence (PXRF) spectrometry was used to test the efficiency of four machine learning techniques (random forest; linear and nonlinear support vector machine; classification and regression tree) for distinguishing three land use types in India based upon scans of mineral surface (0–20 cm) soil. Results showed similar performance among the four tested algorithms, with classification accuracy of a randomly selected validation set ranging from 83% to 91%. The classification and regression tree was favored based upon simple “IF AND THEN” rules which make classification of the data simple. In sum, PXRF data was shown highly effective at differentiating land use types in India. Future work should focus on a larger number of land use classification types and possible combination of PXRF data with complimentary proximal sensing datasets (e.g., visible near infrared spectroscopy).