An automated soil line identification method using relevance vector machine

Abstract

The soil line is an important concept that describes the linear relationship between reflectance of bare soils in the near-infrared (NIR) and red (R) spectral bands. Bare soil line parameters (slope and intercept) are used in calculating several vegetation indices. Previous studies have proposed both manual and empirical procedures in estimating the bare soil parameters. Manual procedures introduce some amount of subjectivity in identifying the soil line. Empirical methods often suffer because of variations caused by soil type, moisture, and organic matter contents. The existence of non-bare soil pixels also affects these procedures. In this study, we proposed an automated supervised learning algorithm using relevance vector machine (RVM) for extracting the soil line from Landsat images. The 10-fold cross validation (10-fold CV) indicated 92% accuracy for distinguishing bare soil and other non-bare soil pixels from an image. The area under the receiver operating characteristic (ROC) curve reached a value of 0.98, indicating a significant predicting power of the proposed procedure. Additionally, this procedure was evaluated using data from 10 bare soil fields in the Texas High Plains region in 2008 and 2009. Statistical analysis indicated no significant difference between the observed and estimated bare soil line parameters. The proposed RVM-based procedure successfully incorporated machine-learning algorithms into agricultural remote sensing and eliminated the dependency on empiricism and minimized subjectivity.