Soil salinity drives land degradation and vegetation loss, posing a significant challenge for agriculture and global efforts to combat desertification. Enhancing land degradation monitoring methodologies is essential and aligns with the mission of the United Nations Convention to Combat Desertification (UNCCD), which aims to promote sustainable land management. In this context, this study aimed to develop soil salinity prediction models (SSPMs) for monitoring land degradation caused by soil salinity using Sentinel-2 (S2) satellite data and geographic information system (GIS) techniques. An area of 17.5 km 2 (1750 ha) of Sabkhat Ghuwaymid, located in the Al-Qassim region of Saudi Arabia, was delineated as the study area for developing SSPMs. A total of 118 soil samples were collected using a grid sampling method between February 19 and 22, 2024. The collected samples were analysed for texture, acidity (pH: 6.97–8.61), total dissolved solids (TDS: 4.25–62.60 g L -1 ), and electrical conductivity (EC: 8.49–125.23 dS m -1 ) as an indicator of soil salinity. Soil analyses were performed using a hydrometer technique and Ohaus pH and EC meters. A promising model for predicting and mapping soil salinity was developed using S2 satellite imagery in combination with stepwise multiple linear regression (SMLR) analysis. The developed model demonstrated a correlation coefficient (R 2 ) of 0.61 during development and 0.66 during validation, with a P-value < 0.05, and an RMSE of 12%. These results indicate that S2-based SSPMs provide a reliable and cost-effective approach for monitoring soil salinity, thereby supporting sustainable land management in arid environments.
Alneami et al. (Wed,) studied this question.