Assessment of reclaimed soils by unsupervised clustering of proximal sensor data

Abstract

The application of soil proximal sensors on reclaimed sites presents a novel method for assessing the quality of reclaimed landscapes. This method improves assessment reliability, information management, and environmental assurance. One proximal sensing system that could be used to provide high spatial resolution measurements of soil parameters is an on-the-go optical sensor that collects data at two wavelengths: 660 and 940 nm. Proximal soil sensing data were collected at 27 sites, where organic matter, cation exchange capacity (CEC), and soil water content were collected from 221 soil samples from 0 to 15 cm. The proximal soil sensor data were then automatically clustered using a combination of self-organizing maps and random uniform forests. Overall, the proximal sensor data combined with this data analysis approach created maps with either three or four soil zones. On average, soil zones had statistically significant differences in organic matter, CEC, and water content. This system could be used to map out zones with significant soil variation as part of reclamation monitoring and then used to guide laboratory analytical sampling. Future work should focus on development of on-the-go reflectance spectroscopy systems to provide quantitative soil data with high spatial resolution.