Digital modeling of surface and subsurface soil salinity in Golestan Province, Iran

Abstract

Soil salinity is a challenging issue in many arid and semiarid regions and requires suitable management strategies. In the present study, the ability of three ML algorithms (random forest (RF), quantile regression forest (QRF), and cubist (Cu)) together with a depth function were evaluated to predict and compare the spatial distribution of soil salinity, and to identify the main environmental variables that influence soil salinity spatial distribution in 105 soil profiles in 90,000 ha in Northern Iran, Golestan Province. Environmental variables to support modeling included land parameters obtained from the digital elevation model, remote sensing information retrieved from Landsat 7 ETM+ images, rainfall, and piezometric maps. The electrical conductivity was estimated (ECe – dS m−1) at five depth intervals (i.e., 0–25, 25–50, 50–75, 75–100, and 100–125 cm) by spline model in all soil profiles. The model performance was assessed using root mean squared error (RMSEcv), mean absolute error (MAEcv), and coefficient of determination (R2cv). RF, QRF, and Cu methods similarly predicted ECe at the soil surface (0 to 25 cm). At 25–50 cm depth, the QRF model (R2cv = 0.55 and RMSEcv = 12.10) outperformed the other two models. QRF produced more consistent results with the expected ECe distribution on Taylor diagrams. This result showed that QRF is a well-suited model for mapping soil salinity at different depths in the studied region. The produced maps indicated a similar trend of increasing salinity as depth increased. Additionally, results showed that rainfall, NDVI, MRVBF, and piezometer were the most important variables to predict salinity in the study area.