Pattern Extraction of Topsoil and Subsoil Heterogeneity and Soil-Crop Interaction Using Unsupervised Bayesian Machine Learning: An Application to Satellite-Derived NDVI Time Series and Electromagnetic Induction Measurements

The link between remotely sensed surface vegetation performances with the heterogeneity of subsurface physical properties is investigated by means of a Bayesian unsupervised learning approach. This question has considerable relevance and practical implications for precision agriculture as visible spatial differences in crop development and yield are often directly related to horizontal and vertical variations in soil texture caused by, for example, complex deposition/erosion processes. In addition, active and relict geomorphological settings, such as floodplains and buried paleochannels, can cast significant complexity into surface hydrology and crop modeling. This also requires a better approach to detect, quantify, and analyze topsoil and subsoil heterogeneity and soil-crop interaction. In this work, we introduce a novel unsupervised Bayesian pattern recognition framework to address the extraction of these complex patterns. The proposed approach is first validated using two synthetic data sets and then applied to real-world data sets of three test fields, which consists of satellite-derived normalized difference vegetation index (NDVI) time series and proximal soil measurement data acquired by a multireceiver electromagnetic induction geophysical system. We show, for the first time, how the similarity and joint spatial patterns between crop NDVI time series and soil electromagnetic induction information can be extracted in a statistically rigorous means, and the associated heterogeneity and correlation can be analyzed in a quantitative manner. Some preliminary results from this study improve our understanding the link of above surface crop performance with the heterogeneous subsurface. Additional investigations have been planned for further testing the validity and generalization of these findings.