Soil organic carbon prediction using visible-near infrared reflectance spectroscopy employing artificial neural network modelling

Visible-near infrared (VNIR) spectroscopy is a relatively fast and cost-effective analytical technique for estimating soil organic carbon (SOC). The present study was undertaken for predicting SOC using VNIR reflectance spectroscopy employing artificial neural network (ANN). Surface soil samples (0-15 cm) were collected from 75 georeferenced locations through grid sampling approach in a hilly watershed of Himachal Pradesh, India, and analysed for SOC. The reflectance spectra of soil samples was measured using a spectroradiometer in the wavelength range of 350-2500 nm. Various spectral indices were generated using the sensitive bands in the visible region. The SOC-sensitive spectral indices and reflectance transformations were utilized for predictive modelling of SOC using the ANN model. This model could predict SOC values with R-2 of 0.92 and MSE value of 0.24, indicating that this technique can be used to predict SOC in a spatial domain when coupled with high-resolution hyperspectral satellite/airborne data.