[Comparative study on hyperspectral inversion accuracy of soil salt content and electrical conductivity]

Area of Science:

• Earth and Planetary Sciences
• Environmental Sciences
• Agricultural Science

Context:

• Soil salinization is a major threat to agricultural productivity, particularly in arid and semi-arid regions.
• Accurate monitoring of soil salinization is crucial for effective land and water management strategies.
• Hyperspectral remote sensing offers a promising non-invasive approach for large-scale soil monitoring.

Purpose:

• To investigate the mechanism of hyperspectral remote sensing for monitoring soil salinization.
• To compare the accuracy of hyperspectral techniques with traditional electrical conductivity (EC1:5) measurements for soil salt content (St) prediction.
• To identify optimal spectral indices and regression methods for improved soil salinization monitoring.

Summary:

• Significant correlations were found between soil salt content (St) and hyperspectral indices across various spectral bands, outperforming correlations with electrical conductivity (EC1:5).
• Multivariate regression models utilizing hyperspectral data demonstrated higher prediction accuracy for St compared to EC1:5.
• The study highlights the superior sensitivity of hyperspectral data to soil salt content, providing a robust method for salinization assessment.

Impact:

• Provides a theoretical foundation for enhancing the accuracy of hyperspectral remote sensing in soil salinization monitoring.
• Offers a more precise and efficient method for assessing soil salt levels, aiding in targeted land management interventions.
• Contributes to sustainable agriculture by enabling early detection and mitigation of soil salinization impacts.