Abstract
The mapping of soil organic carbon (SOC) variability was carried out in a Sahelian region of Senegal by testing the effectiveness to include the Sentinel 2 remote sensed data in the characterization of the soil properties. Ordinary kriging (OK) applied under ArcGIS is compared with multiple linear regression (MLR) calibrated under R software. The results showed a slight decrease of the root mean square error ranging from 0.18 with kriging to 0.16 for multiple linear regression. Carbon variability was also detailed at a finer scale with multiple linear regression at the pixel scale from 10 to 20 m. Spectral bands situated in the visible wavelength, NDWI and NDVI were the most discriminating explanatory variables in the spatial modeling of organic carbon by multiple linear regression. Specific locations that require inputs of manure or compost were also geo-localized with multiple linear regression in order to ensure sustainable management of soil organic carbon. The use of remote sensed data also puts into perspective the possibility of spatializing the physical and chemical properties of the soil on larger areas and correcting the lack of information on soil mapping in the Sahelian regions of Africa.
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