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 R² 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 highresolution hyperspectral satellite/airborne data.