SPATIAL PREDICTION OF SOIL DEPTH BY THE COMBINATION OF MULTIPLE REMOTE SENSING TECHNIQUES

Soil depth is one of the most critical factors which impact on culture productivity and makes difficult appropriate management decisions. However, assessing this parameter is also the most challenging tasks in the agronomic field. The objective of this work was to predict the spatial distribution of soil depth using remote sensing data and machine learning techniques. 292 sites were allocated (based on the toposequence approach) and perforated (from 0 to 2 m) at three different locations in Brazil. Based on these, in-situ traditional depth maps (denominated empirical) were elaborated as for future validation. Afterwards, we elaborated a strategy to achieve these different depths by remote sensing (RS) approach. Landsat 8 OLI bands, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and emissivity in dry and rainy seasons as well as terrain attributes were applied to predict soil depth.