Integration of multispectral and hyperspectral data to map magnetic susceptibility and soil attributes at depth: A novel framework

The understanding of attributes and magnetic susceptibility (chi) at soil surface, mainly subsurface, is crucial due to their role to identify climate changes, soil degradation, soil classification systems, soil fertility, and pedogenesis. The integration of proximal sensing (PS) and remote sensing (RS) data sources could increase the efficiency of Digital Soil Mapping. Nevertheless, products of this integration need to be evaluated in hybrid, stochastic, and deterministic models to predict soil attributes and chi at surface and subsurface. This study investigates the PS and RS integration by applying four deterministic (e.g. Bayesian Regularised Neural Network, Generalised Linear Model, Random Forest and Cubist) and hybrid models (e.g. Regression Kriging of residuals of the best-fitted model) to create a new environmental variable, the Best Synthetic Soil Image (BSSI), at three soil depths (e.g. 0 - 20, 40 - 60 and 80 - 100 cm) that quantitatively represent the soil spectral signature. We also used the BSSI in a comparison with bare soil surface (e.g. SYSI - Synthetic Soil Image) to predict soil attributes and chi by performing the deterministic and hybrid models. We hypothesize that the BSSI, which integrates PS and RS data, enhances soil modelling predictions at subsurface by selecting the best model approach. The BSSI demonstrated original and valuable contribution to increase the predictive model power at deeper layers, while SYSI was effective at upper layers. The PS and RS integration helped to identify the main soil patterns horizontally and vertically, which traditional soil surveys have not been capable of representing.

Automated summary

This study investigates the integration of PS and RS data sources to enhance Digital Soil Mapping efficiency, by applying deterministic and hybrid models to predict soil attributes and chi at different depths. The integration of PS and RS data proved to increase the predictive model power at subsurface levels, with the BSSI representing soil spectral signatures effectively. Traditional soil surveys have limitations in representing soil patterns, which can be addressed by PS and RS integration.