Systematic discrimination of irrigation and upheaval associated salinity using multitemporal SAR data

Area of Science:

• Remote Sensing and Geoscience
• Agricultural Science
• Environmental Science

Background:

• Soil salinization from irrigation and groundwater upheaval threatens agriculture in arid/semi-arid regions.
• Distinguishing salinity sources is crucial for effective management and crop protection.

Purpose of the Study:

• To systematically simulate and quantify irrigation-induced versus upheaval-associated soil salinity.
• To investigate the impact of different salinity sources on crop growth using remote sensing data.
• To discriminate total salinity into its irrigation and upheaval components.

Main Methods:

• Utilized Sentinel-1 C-band radar data (5.36 GHz) from 2015-2019 for dry and wet seasons.
• Employed semi-empirical microwave simulation (DSDM-SS) to quantify total soil Electrical Conductivity (EC) from the imaginary part of the dielectric constant (ε″).
• Developed a novel site- and climatic-specific model to extract irrigation-induced (ε_Irrigation″) and upheaval-associated (ε_Upheaval″) salinity from ε″.

Main Results:

• Both ε_Upheaval″ and ε_Irrigation″ showed strong statistical correlation with in-situ soil EC (R² = 0.89) and groundwater EC (R² = 0.85).
• Salinity increase rate was significantly higher due to irrigation (77%) compared to upheaval (42%).
• Intensive groundwater irrigation had a greater negative impact on vegetation vigor (θ = -0.87) than upheaval.
• Crop failure in the dry season was linked to osmotic stress and waterlogging.

Conclusions:

• The developed SAR simulation effectively quantifies and discriminates salinity sources.
• Irrigation practices are the dominant driver of soil salinization and negatively affect crop health.
• Understanding salinity origins is vital for mitigating agricultural losses in water-scarce regions.