Modeling of Soil Sand Particles Using Spectroscopy Technology

ABSTRACT

The advent of lab diffuse reflectance spectroscopy (LDRS) that exploits the fundamental vibration, overtones and combination of functional groups of soil components makes the soil study easier. The present research intends to predict sand content utilizing the proximal soil sensing (PSS) tech. Thus, in accord with the supplementary data layers and stratified randomized sampling (SRS) method, eventually, 128 samples were gathered from 20 cm of soil surface of Mazandaran Province, Iran. First of all, the sample set was subdivided into two subsets: calibration (96) and validation (32). Using the multivariate regression analysis-partial least squares regression (PLSR) algorithm with leave-one-out cross-validation (LOOCV) technique and some pre-processing algorithms, such as spectral averaging, smoothing and 1st derivative (1st-D), the definitive calibration model with two & four latent vectors (LVs/LFs) and correlation coefficient (R_P), determination coefficient (R²_P), root mean square error (RMSE_P), ratio of performance to deviation (RPD_P) and ratio of performance to interquartile distance (RPIQ_P) respectively: 0.83&0.82, 0.68&0.67,8.68&8.83%,1.78&1.75,2.45&2.41, were validated and spotted as the most appropriate predictive model for the sand content prediction in the study region. Last, the potentiality of the visible-near infrared diffuse reflectance spectroscopy (VNIR-DRS) for sand content estimation in Mazandaran soils was proved. Also, it is feasible to upscale the sand prediction process utilizing the principal resulted model and the key spectral domains via airborne/satellite hyperspectral data, which emphatically shows the LDRS importance as a commencement point for characterizing the informative optical wavelengths. Likewise, that will be the infrastructure for spaceborne data modeling and upscaling process.

KEYWORDS:

• PLSR
• proximal soil sensing
• sand
• spectroscopy
• textural segment

Acknowledgments

The authors wish to thank Dr. R. DarvishZadeh for her work in authenticating hyperspectral samples. Thanks are also due to Dr. A.A. Noroozi for his suggestions on preparing the dark room as well as spectroscopy technology.

Data availability statement

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

Disclosure statement

No potential conflict of interest was reported by the author(s).

Permission to reproduce material from other sources

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

Additional information

Funding

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