The possibility of spatial mapping of soil organic carbon content at three depths using easy-to-obtain ancillary data in a Mediterranean area

Abstract

Background: Unlike most of Europe, Andalucía in southern Spain as a Mediterranean area still lacks digital maps of soil organic carbon (SOC) content at multiple depths, which can be generated by machine learning algorithms. The wide diversity of climate, geology, hydrology, landscape, topography, vegetation, and micro-relief data as easy-to-obtain covariates has 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 km2 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 18 remotely-sensed spectral data (band ratios calculated by Landsat-8 Operational Land Imager ‘OLI’ and Sentinel-2A Multispectral Instrument ‘MSI’ in July 2019). In total, 300 soil samples from 100 points at three depths (0-25 cm, 25-50 cm, and 50-75 cm) were taken from existing literature. Both machine learning techniques were compared taking into account their accuracy using the goodness-of-fit criteria to predict SOC. Results: The findings showed that integrating the indices derived by Landsat-8 OLI and Sentinel-2A MSI satellite data had a better result than when satellite data was used separately.  Conclusions: We obtained evidence that the resolution of satellite images is a key parameter in modelling and digital mapping.