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

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