Abstract
BACKGROUND: Unlike most of Europe, Andalucía in southern Spain as a Mediterranean area still lacks digital maps of SOC content provided by machine learning algorithms. The wide diversity of climate, geology, hydrology, landscape, topography, vegetation, and micro-relief data as easy-to-obtain covariates facilitated the development of digital soil mapping (DSM). The purpose of this research is to model and map the spatial distribution of SOC at three depths, in an area of approximately 10000 km (2) located in Seville and Cordoba Provinces, and to use R programming to compare two machine learning techniques (cubist and random forest) for developing SOC maps at multiple depths. METHODS: Environmental covariates used in this research include nine derivatives from digital elevation models (DEM), three climatic variables and finally eighteen remotely-sensed spectral data (band ratios calculated by the acquired Landsat-8 OLI and Sentinel-2A MSI in July 2019). In total, 300 soil samples from 100 points were taken (0-25 cm). The purpose of this research is to model and map the spatial distribution of SOC, in an area with approximately 10000 km2 located in Seville and Cordoba Provinces, and to compare two machine learning techniques (cubist and random forest) by R programming. RESULTS: The findings showed that the novel approach for integrating the indices using Landsat-8 OLI and Sentinel-2A MSI satellite data had a better result. CONCLUSIONS: Finally, we obtained evidence that the resolution of satellite images is more important in modelling and digital mapping.