A novel form of ordinary kriging, involving the local estimation and modelling
of the variogram at each prediction site (OKLV), is tested at a regional scale
on a large data set, in order to adapt to non-uniform spatial structures and
improve the assessment of the salinity hazard in the lower Chelif Valley,
Algeria. The spatial variability study was carried out on a 38000 ha area
using 5141 topsoil electrical conductivity (EC) measurements systematically
sampled on a 250 m by 250 m grid. Variography analysis confirmed the existence
of large trends in the EC variability with differing spatial structures
between sub-areas.
OKLV performed better than ordinary kriging with a whole-area variogram (OKWV)
in predicting the proportion of high saline soils in large blocks, but the
predictions appeared mostly similar. In contrast, the estimation variance maps
revealing the uncertainties of the spatial predictions were markedly different
between the 2 methods. OKLV integrates the local spatial structure in the
uncertainty assessment, whereas kriging with a whole-area variogram only
considers the sampling intensity. Comparison with prediction errors on a
validation set confirmed the consistency of the OKLV prediction variance. This
appears to be a major improvement for decision-making procedures such as
delineating areas where remediation should take place.
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Soil salinity and ground surface morphology in the Lower Cheliff plain (Algeria) can directly or indirectly impact the stability of environments. Soil salinization in this area is a major pedological problem related to several natural factors, and the topography appears to be important in understanding the spatial distribution of soil salinity. In this study, we analyzed the relationship between topographic parameters and soil salinity, giving their role in understanding and estimating the spatial distribution of soil salinity in the Lower Cheliff plain. Two satellite images of Landsat 7 in winter and summer 2013 with reflectance values and the digital elevation model (DEM) were used. We derived the elevation and slope gradient values from the DEM corresponding to the sampling points in the field. We also calculated the vegetation and soil indices (i.e. NDVI (normalized difference vegetation index), RVI (ratio vegetation index), BI (brightness index) and CI (color index)) and soil salinity indices, and analyzed the correlations of soil salinity with topography parameters and the vegetation and soil indices. The results showed that soil salinity had no correlation with slope gradient, while it was significantly correlated with elevation when the EC (electrical conductivity) values were less than 8 dS/m. Also, a good relationship between the spectral bands and measured soil EC was found, leading us to define a new salinity index, i.e. soil adjusted salinity index (SASI). SASI showed a significant correlation with elevation and measured soil EC values. Finally, we developed a multiple linear regression for soil salinity prediction based on elevation and SASI. With the prediction power of 45%, this model is the first one developed for the study area for soil salinity prediction by the combination of remote sensing and topographic feature analysis.Citation: Ibrahim YAHIAOUI, AbdelKader DOUAOUI, ZHANG Qiang, Ahmed ZIANE. 2015. Soil salinity prediction in the Lower Cheliff plain (Algeria) based on remote sensing and topographic feature analysis. Journal of Arid Land,
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