George Kargas

Soil salinization and its detrimental agricultural, environmental, and socioeconomic impact over extended regions represent a major global concern that needs to be addressed. The sustainability of agricultural lands and the development of proper mitigation strategies require effective monitoring and mapping of the saline areas of the world. Therefore, robust modeling techniques and efficient sensors that assess and monitor the spatial and temporal variations in soil salinity within an area, promptly and accurately, are essential. The aim of this paper is to provide a comprehensive and up-to-date review of the modeling approaches for the assessment and mapping of saline soils using data collected by the EM38 and EM38MK2 (MK2) sensors at different scales. By examining the current and latest approaches and highlighting the most noteworthy considerations related to their accuracy and reliability, the intention of this review is to elucidate and underline the role of the EM38 and the MK2 type in the recent needs of detecting and interpreting soil salinity. Another aim is to assist researchers and users in selecting the optimal approach for future surveys and making well-informed decisions for the implementation of precise management practices. The study’s findings revealed that the integration of the EM38 and MK2 sensors with remote sensing data and advanced methods like machine learning and inversion is a promising approach to the accurate prediction and mapping of the spatiotemporal variations in soil salinity. Therefore, future research focused on validating and expanding such sophisticated modeling applications to regional and global scales should be increased.