Development of a comparative assessment of spatial interpolation methods for soil salinity mapping in the Senegal River Basin (Mauritania)

The object of research is the spatial variability of soil salinity in the Senegal River Basin (Mauritania). The problem to be solved is to identify the most reliable spatial interpolation technique for soil salinity mapping and support the appropriate soil salinity management. This problem is especially important as soil salinization is a serious environmental and socio-economic problem that severely constrains agricultural productivity, mainly in arid and semi-arid areas. The case research was carried out on two cultivated agricultural plots and one uncultivated plot having similar soil characteristics. The four spatial interpolation methods used and compared are: global polynomial interpolation (GPI), inverse distance weighting (IDW), ordinary kriging (OK) and radial basis functions (RBF). The predictive performance of the models was evaluated using mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Results showed statistically significant differences in the properties of the datasets, especially within the central tendency and distribution. Soil electrical conductivity (EC) varied substantially among the research areas, with mean values increasing from 1.35 to 5.58 dS × m–1 and spatial ranges extending up to 189 m. Across all three zones, IDW consistently provided the best interpolation performance, achieving coefficients of determination (R2) close to 0.99 and outperforming the other tested methods. This result implies that the proper mapping of soil salinity requires the selection of an interpolation method compatible with the statistical characteristics of the field data. They also stressed the need for permanent monitoring of salinity to guarantee the sustainable agricultural productivity and efficient land management in the irrigated coastal areas of the Senegal River Valley.