Multi-temporal bare surface image associated with transfer functions to support soil classification and mapping in southeastern Brazil.

• Soil variability was correctly described by the bare surface composite image. • Depth-transfer functions were combined to remote sensing to map subsoil reflectance. • Prediction models had good performance for clay content, color and iron concentration. • Soil classes were mapped using attribute predictions and Boolean classification key. • Uncertainties in clay and CEC prediction contributed to soil misclassifications. Detailed soil maps are essential for agricultural management, but they are scarce in many regions. Even with the recent development of digital soil mapping (DSM) strategies, providing an adequate spatial representation of soils is still a challenging task. Therefore, this work aims to define a DSM approach, which combines proximal and remote sensing data to describe the spatial variation of soil attributes and types. The study was carried out in a site at southeastern Brazil, where 326 sampling points were defined and collected at two depths. Soil Vis-NIR spectra and physico-chemical attributes were measured in laboratory. A bare surface synthetic image (SYSI) was created from multi-temporal Landsat images and later validated with lab. spectra. Geographically weighted regression was used to calibrate depth transfer functions, which were applied to SYSI, generating a subsurface soil synthetic image (SYSI sub). Soil attributes at both depths were mapped with SYSI, SYSI sub and terrain derivatives. A soil classification key was designed following the Brazilian Soil Classification System and boolean logic. Soils were classified based on the soil attributes maps and boolean key. Hence, Monte-Carlo simulation (MCS) was performed to evaluate the error propagation from predicted attribute maps to soil types map. Correlations between satellite and lab. spectra varied from 0.68 to 0.8, indicating good capacity of SYSI in retrieving bare soil reflectance. Depth transfer functions also had good performance, with R2 ranging from 0.62 to 0.72. The soil attribute maps with best performance were clay content (R2 = 0.63), iron concentration (R2 = 0.72) and soil color (hue R2 = 0.57; value R2 = 0.73; chroma R2 = 0.63). Soil organic matter and chemical attributes were poorly predicted, with R2 between 0.12 and 0.38. MCS indicated that uncertainties in attributes maps might result in confusion between Ferralsols and Acrisols, Regosols and Luvisols, as well as Luvisols and Acrisols. Comparison between digital and conventional maps of soil classes, presented satisfactory kappa (34.65%) and global accuracy (54.46%). The technique presents an improvement to DSM, as it integrates soil sensing and depth transfer functions into DSM. [ABSTRACT FROM AUTHOR]