Combining Vis-NIR hyperspectral imagery and legacy measured soil profiles to map subsurface soil properties in a Mediterranean area (Cap-Bon, Tunisia)

The study was conducted in a pedologically contrasted 300 km2-cultivated area located in the Cap Bon region (Northern Tunisia) and tested on three soil surface properties (clay and sand contents and cation exchange capacity). The main results were as follows: i) fairly satisfactory (cross-validation R2 between 0.55 and 0.81) surface-subsurface functions were obtained for predicting the soil properties at 15-30 cm and 30-60 cm, whereas predictions at 60-100 cm were less accurate (R2 between 0.38 and 0.43); ii) linear models outperformed random-forest models in developing surface-subsurface functions; iii) due to the error propagations, the final predicted maps of the subsurface soil properties captured from 1/3 to 2/3 of the total variance with a significantly decreasing performance with depth; and iv) these maps brought significant improvements over the existing soil maps of the region and showed soil patterns that largely agreed with the local pedological knowledge. This paper demonstrates the added value of combining modern remote sensing techniques with old legacy soil databases.