Upgrading the Salinity Index Estimation and Mapping Quality of Soil Salinity Using Artificial Neural Networks in the Lower-Cheliff Plain of Algeria in North Africa: Amélioration de l'estimation de l'indice de salinité et de la qualité de la cartographie de la salinité des sols en utilisant les réseaux de neurones artificiels dans la plaine du Bas Cheliff au Nord de l'Algérie

Abstract

Since decades ago, the Lower Cheliff plain is under the continuous influence of soil salinization induced by mismanagement of the groundwater resources. The main purpose of this study was to estimate and map soil salinity using both Salinity Index (SI) and Artificial Neural Networks (ANN). In doing so, a total of 796 soil samples of Electrical Conductivity (EC, dS.m–1) measured in laboratory combined to spectral parameters data of Landsat-8 OLI, by applying a Salinity Index (SI) and used also to training the ANN model (80% of total data), the rest of the dataset (20%) was retained for validation with both methods. The results of applying an ANN estimator based on the reflectance values of three bands: green (B3), red (B4) and near-infrared (B5) as learning input neurons, proved their interest in the estimation of EC given a high determination coefficient (R2 = 0.80) between the values of simulated truth and ground, compared to the results obtained using only the SI method giving a moderate precision (R2 = 0.42). Regarding the soil salinity mapping, the two methods generated contrasting results, the SI estimates that 68.5% of the total area is affected by salinity (underestimation) meanwhile the ANN gave an estimation of 78.8%. In a conclusion, the estimation and mapping of soil salinity using the SI method has been upgraded significantly when ANN was involved.