Predicting Dynamics of Soil Salinity and Sodicity Using Remote Sensing Techniques: A Landscape-Scale Assessment in the Northeastern Egypt

Abstract

Traditional mapping of salt affected soils (SAS) is very costly and cannot precisely depict the space–time dynamics of soil salts over landscapes. Therefore, we tested the capacity of Landsat 8 Operational Land Imager (OLI) data to retrieve soil salinity and sodicity during the wet and dry seasons in an arid landscape. Seventy geo-referenced soil samples (0–30 cm) were collected during March (wet period) and September to be analyzed for pH, electrical conductivity (EC), and exchangeable sodium percentage (ESP). Using 70% of soil and band reflectance data, stepwise linear regression models were constructed to estimate soil pH, EC, and ESP. The models were validated using the remaining 30% in terms of the determination coefficient (R2) and residual prediction deviation (RPD). Results revealed the weak variability of soil pH, while EC and ESP had large variabilities. The three indicators (pH, EC, and ESP) increased from the wet to dry period. During the two seasons, the OLI bands had weak associations with soil pH, while the near-infrared (NIR) band could effectively discriminate soil salinity and sodicity levels. The EC and ESP predictive models in the wet period were developed with the NIR band, achieving adequate outcomes (an R2 of 0.65 and 0.61 and an RPD of 1.44 and 1.43, respectively). In the dry period, the best-fitted models were constructed with deep blue and NIR bands, yielding an R2 of 0.59 and 0.60 and an RPD of 1.49 and 1.50, respectively. The SAS covered 50% of the study area during the wet period, of which 14 and 36% were saline and saline-sodic soils, respectively. The extent increased up to 59% during the dry period, including saline soils (12%) and saline-sodic soils (47%). Our findings would facilitate precise, rapid, and cost-effective monitoring of soil salinity and sodicity over large areas.