Publication

Abstract : Remote sensing techniques are capable of mapping soil nutrient concentrations and preparing soil maps for long-term agricultural productivity and food security. Recently, hyperspectral imaging techniques have been widely used to quantify and map nitrogen levels in the soil in large areas. In this study, we employed a partial least square regression (PLSR) technique over PRISMA hyperspectral data on part of the Radhapuram area, Tirunelveli District, India to improve the accuracy of estimating soil nutrient levels. The results of the study show that the PLSR prediction accuracy rates using field observations provided the most accurate estimates of soil containing organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK). Soil nutrient predictions were carried out using bands in visible near-infrared and near-infrared regions. Analysis of 150 bands using random analyses provided an R2 value of 0.970 and the PLSR technique performed best while using the combined bands in the VNIR+NIR regions. Based on the analyses, PRISMA hyperspectral images using spectral angle mapper (SAM) image classification provided a better map of the soil consisting of organic carbon. The research findings are important references for the prediction of soil nutrients with high accuracy.