Integrated use of remote sensing techniques and machine learning in assessing soil salinity: current approaches, methods, and future perspectives
Year: 2026
Type: article
Abstract: Soil salinity, driven by climate change and human activities, threatens global food security across over 412 million hectares, requiring precise monitoring. Traditional methods are accurate but costly and time-consuming. This review synthesizes recent advances integrating remote sensing (RS; UAVs, satellites, hyperspectral) with machine learning (ML; Random Forest, SVM, ANN) for soil salinity assessment. RS supplies large-scale temporal data, while ML captures complex non-linear relationships. Multi-sensor fusion (e.g. optical-radar) and ensemble ML improve accuracy (R2 up to 0.98). Yet limitations remain: reliance on surface data (0–20 cm), spectral interference from vegetation, small training datasets, and poor generalizability. Deep learning (DL) architectures (e.g. U-Net, FCNN) outperform traditional ML for specific tasks, including semantic segmentation of salinity classes (accuracy > 90%) and subsurface EC prediction (R2 up to 0.85). Future work must close the root-zone salinity gap using DL, time-series analysis, and proximal sensing. A Digital Soil Mapping framework incorporating climatic, topographic, and soil covariates is essential for robust, transferable models. Prioritizing large-scale validation and standardized methods will shift salinity assessment from mapping to predictive management, supporting sustainable agriculture and land restoration.
Institutions: Harran University, University of Inland Norway
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7225018593