LAUSR

Abstract Selenium (Se) is an important trace element that is essential to human beings. In the past, the Se concentration has mostly been obtained by field sampling and analysed under laboratory conditions. Unfortunately, this process is expensive, and the number of available samples is usually relatively small. A soil geochemical survey was conducted in conjunction with an airborne survey via hyperspectral remote sensing in the Chuangye Farm area, China. Twenty-five elements/oxides including Se were analysed in the samples, and the results showed that Se has a highly negative correlation with K. Using hyperspectral Shortwave Infrared Airborne Spectrographic Imager (SASI) data, the abundances of clay minerals were obtained through the sequential maximum angle convex cone (SMACC) classification operation. According to the abundances of clay minerals, the reflectance of clay minerals was obtained using the spectral retrieval method. Due to the correlation among K, Se, clay minerals and their spectral characteristics, a stepwise regression model was established using the results from the geochemical survey data and retrieved hyperspectral SASI data; then, the K and Se concentrations were predicted. The results of this study show that predicting the Se content in soil by using SASI images through the spectral retrieval of clay minerals in soil in conjunction with actual geochemical analysis results boasts a higher prediction accuracy than the use of the raw SASI images, and this prediction approach has been proven to be feasible.