Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative.

Area of Science:

• Soil Science
• Remote Sensing
• Spectroscopy

Background:

• Accurate soil parameter estimation relies on effective spectral reflectance pretreatment.
• Classic integer derivatives can lead to spectral information loss and noise.
• Fractional order derivatives offer a potential improvement for spectral data processing.

Purpose of the Study:

• To apply fractional order derivative algorithm for spectral pretreatment in estimating desert soil clay content.
• To compare the effectiveness of fractional order derivatives against classic integer derivatives for this application.
• To identify the optimal fractional order for improved model accuracy.

Main Methods:

• Collected 103 soil samples from the Ebinur Lake basin for calibration and validation.
• Measured spectral reflectance and clay content in the laboratory.
• Applied fractional order derivative (0.0-2.0 order) to spectral reflectance and absorbance data.
• Utilized partial least squares regression (PLSR) for model building.
• Evaluated model performance using RPD, R², RMSEC, and RMSEP.

Main Results:

• Models using fractional order derivatives showed superior performance compared to classic integer derivatives.
• The optimal fractional order for spectral reflectance was 1.8 (R²=0.907, RMSEP=0.364%, RPD=2.484).
• The optimal fractional order for absorbance was also effective (R²=0.888, RMSEP=0.383%, RPD=2.511).
• Both optimal models achieved RPD values greater than 2.0, indicating good predictive ability.

Conclusions:

• Fractional order derivative pretreatment is a highly effective method for enhancing soil clay content estimation.
• This technique overcomes limitations of classic integer derivatives, reducing noise and preserving spectral information.
• The study demonstrates the practical application of fractional order derivatives in quantitative soil analysis for arid regions.