Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques

Area of Science:

• Environmental Science
• Agricultural Science
• Remote Sensing Technology

Background:

• Industrialization and urbanization in China have increased soil heavy metal pollution, threatening ecosystems and human health.
• Spectral technology offers a rapid, non-destructive method for monitoring soil heavy metal content.
• Rice leaf spectra show potential for indirectly estimating soil heavy metal levels.

Purpose of the Study:

• To explore the potential of rice leaf spectra for indirect estimation of soil heavy metal content (Cd and As).
• To establish and optimize an estimation model using spectral data and soil heavy metal concentrations.
• To evaluate the accuracy of the developed model for large-scale, non-destructive monitoring.

Main Methods:

• Collected farmland soil samples and rice leaf spectral data in Jiangsu Province, China.
• Determined soil heavy metal content (Cd and As) in a laboratory setting.
• Applied spectral pre-processing techniques and a genetic algorithm (GA) optimized partial least squares regression (PLSR) model.

Main Results:

• Spectral pre-processing, particularly the first-order derivative of absorbance, effectively extracted sensitive spectral information.
• The GA-PLSR model achieved higher accuracy than standard PLSR, utilizing only about 10% of spectral bands.
• Rice leaf spectra demonstrated the capacity to estimate soil Cd (RPD = 2.09) and As (RPD = 2.97).

Conclusions:

• Soil heavy metal content (Cd and As) can be indirectly estimated using rice leaf spectral data.
• This approach provides a quantitative, dynamic, and non-destructive method for monitoring soil heavy metal pollution over large areas.
• The study offers a valuable reference for future remote sensing applications in agricultural environmental monitoring.