Abstract
Soil salinization poses a persistent threat to irrigated agroecosystems, yet remote sensing-based salinity assessment remains predominantly calibrated against bulk electrical conductivity without fully integrating crop physiological variability. This review examines the evolution of remote sensing approaches for soil salinity mapping (1994–2024), with particular emphasis on the role of crop salt tolerance in shaping spectral interpretation and mapping accuracy. A systematic synthesis of 58 peer-reviewed studies retrieved from the Scopus database was conducted using bibliometric analysis and structured full-text thematic classification to evaluate methodological trends and conceptual integration across soil, crops, and spectral domains. The results reveal substantial technological advancement, including multispectral and hyperspectral sensing, machine learning frameworks, and multi-source data integration. However, most approaches remain surface-oriented and statistically calibrated, with limited operationalization of crop-specific tolerance thresholds, root-zone salinity dynamics, and hydrochemical variability. The findings indicate that crop salt tolerance functions as a mediating factor within the soil–plant–spectral continuum, influencing the stability and transferability of spectral–salinity relationships. Integrating physiological tolerance parameters and subsurface processes into modeling frameworks is essential for improving agronomic interpretability and supporting more reliable salinity management in irrigated systems.