Iranian Journal of Remote Sensing and GIS

Iranian Journal of Remote Sensing and GIS

Comparing Different Statistical Models and Pre-processing Techniques for Estimation several chemical properties of the soil Using VNIR/SWIR Spectrum

Document Type : علمی - پژوهشی

Authors
Elham Mehrabi Gohari 1
Hamid Reza Matinfar 2
Azam Jafari 3
Ruhollah Taghizadeh-Mehrjardi 4
F khayamim 5
1 College of Agriculture, Lorestan University, Lorestan, Iran and Faculty, Department of Agriculture, Payame Noor University.
2 College of Agriculture, Lorestan University, Lorestan, Iran
3 Agriculture Faculty, Shahid Bahonar University of Kerman, Kerman, , Iran
4 Faculty of Agriculture and Natural Resources, Ardakan University, Yazd, Iran.
5 Faculty of Agriculture, Isfahan university of Technology,Isfahan,Iran
10.52547/gisj.11.4.47
Abstract
Visible and Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) reflectance spectroscopy (400-2450nm), which are at least as costly and time-consuming, are widely used in the estimation of physical and chemical properties of the soil. The purpose of this study was to investigate the ability of this method to estimate the amount of organic matter, carbonates and gypsum content of soil surface. In the present study, 115 profiles were identified based on the Hypercube technique, and the horizons were sampled and the amount of organic matter, carbonates and gypsum content were measured by standard methods. Reflectance spectra of all samples were measured using an ASD field-portable spectrometer in the laboratory. Soil samples were divided into two random groups (80% and 20%) for calibration and validation of models. PLSR and PCR models and different pre-processing methods i.e. First (FD) and Second Derivatives (SD), Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) were applied and compared to estimate texture elements. The highest RPD of calibration and validation were obtained for PLSR with First derivative of reflectance+ Savitzky_Golay filter pre-processing technique which was classified as a good for the amount of organic matter and gypsum and was classified as a poorly for the amount of carbonates.
Keywords
Partial Least Squares Regression (PLSR)
Principal Component Regression (PCR)
spectroscopy
carbonate
gypsum
organic matter
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