Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy

Abstract

Soil-sample collection and strategy are costly and time-consuming endeavors, mainly when the goal is in-field variation mapping that usually requires dense sampling. This study developed and tested a streamlined soil mapping methodology, applicable at the field scale, based on an unsupervised classification of Sentinel-2 (S2) data supporting the definition of reduced soil-sampling schemes. The study occurred in two agricultural fields of 20 hectares each near Deruta, Umbria, Italy. S2 images were acquired for the two bare fields. After a band selection based on bibliography, PCA (Principal Component Analysis) and cluster analysis were used to identify points of two reduced-sample schemes. The data obtained by these samplings were used in linear regressions with principal components of the selected S2 bands to produce maps for clay and organic matter (OM). Resultant maps were assessed by analyzing residuals with a conventional soil sampling of 30 soil samples for each field to quantify their accuracy level. Although of limited extent and with a specific focus, the low average errors (Clay ± 2.71%, OM ± 0.16%) we obtained using only three soil samples suggest a wider potential for this methodology. The proposed approach, integrating S2 data and traditional soil-sampling methods could considerably reduce soil-sampling time and costs in ordinary and precision agriculture applications.