Sensor Data Fusion for Topsoil Clay Mapping of an Agricultural Field

Abstract

Abstract Data from proximal sensors for gamma (γ) radiation and apparent electrical conductivity (ECa) was combined with elevation, radiance and drainage data. Predictions of clay content were made with different combinations of predictor variables. Predictions from ECa were improved by using multitemporal measurements or multiple measurements with different depth responses. They were also improved by addition of radiance data. Predictions from γ radiation were found to be accurate and was not much improved by adding ECa or any other ancillary data. Predictions by a k Nearest Neighbor algorithm were somewhat better than predictions by Partial Least Squares regression