Spectral Study of Soil Silt and Detection of Key Wavelengths Using Diffuse Reflectance Spectroscopy in Mazandaran Province, Iran

ABSTRACT

The study of silt fraction using the traditional methods, especially on a large scale, is time-consuming, laborious and costly. The present work intends to investigate the spectral behaviors of soil silt fraction using the reflected spectra. Accordingly, 128 soil samples were collected from 20 cm of soil surface of Mazandaran province, Iran. First, samples were divided into two subsets: the calibration and the validation. Spectral signatures and domains of silt components were detected and recognized using the PLSR (Partial-Least-Square-Regression) algorithm and CV (Cross-Validation) technique with spectral preprocessing such as averaging, SG (Savitzky-Golay) smoothing filter and first derivative transformation. The final model was validated with five LFs (Latent Factors) and specifications including Rp (Correlation Coefficient): 0.78, RMSEp (Root Mean Square Error): 7.57%, RPDp (Ratio of Performance to Deviation):1.55, and RPIQp (Ratio of Performance to Interquartile Distance): 2.05. Finally, it was chosen as the best model for studying the silt of Mazandaran province, Iran. The spectral wavebands obtained with the highest correlation coefficients (R_(CCmax)) indicating the independent predictive variables with high impact on the silt modeling processes. Finally, the capability of the proximal sensing technology was confirmed in the investigation of the soil silt content in the region. In addition, the most influential spectral ranges were identified. In conclusion, the present research is an essential work for future investigations including silt digital mapping, and our findings can be used as a basis for large-scale silt content studies using airborne/satellite hyperspectral data.

KEYWORDS:

• Digital mapping
• PLSR
• proximal soil sensing
• silt
• upscaling

Acknowledgements

This research was partially supported by SANRU and TMU. We thank our colleagues from SANRU and TMU who provided insights and expertise that greatly assisted the research.

Disclosure statement

There are no potential conflicts of interest between each of the contributing factors in the production of this article (sponsors, scholars, and writers).

Data availability statement

The data that support the findings of this study are available by email only from the corresponding author, upon reasonable request.

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We authors hereby exchange all copyright proprietorship to this journal that grants the right to the corresponding author to incorporate any necessary changes and he will act as the guarantor or surety for the manuscript on our behalf.

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.