Development of Hungarian spectral library: Prediction of soil properties and applications

Yıl 2023, Cilt: 12 Sayı: 3, 244 - 256, 02.07.2023

Mohammed Ahmed Mohammedzein Adam Csorba Brian Rotich Phenson Nsima Justin Caleb Melenya Yuri Andrei Erika Micheli

https://doi.org/10.18393/ejss.1275149

Cited By: 1

Öz

Updating soil information systems (SIS) requires advanced technologies to support the time and cost-effective and environment-friendly soil data. The use of mid- infrared (MIR) Spectroscopy as alternative to wet chemistry has been tested. The MIR spectral library is a useful technique for predicting soil attributes with high accuracy, efficiency, and low cost. The Hungarian MIR spectral library contained data on 2200 soil samples from 10 counties representing the first Soil Information and Mentoring System (SIMS) survey. Archived soil samples were prepared and scanned based on Diffuse Reflectance Infrared spectroscopy (DRIFT) technique and spectra data were saved in the fourier transform infrared (FTIR) spectrometer OPUS software. Preprocessed filtering methods, outlier detection methods and calibration sample selection methods were applied for spectral library. MIR calibration models were built for soil attributes using Partial Least Square Regression (PLSR) method. Coefficient determination (R2), The Root Mean Squared Error (RMSE) and Ratio of Performance to Deviation (RPD) were used to assess the goodness of calibration and validation models. MIR spectral library had the ability to significantly estimate soil properties such as SOC, CaCO3, sand, clay and silt through various scale models (national, county and soil type). The findings showed that our spectral library soil estimations are precise enough to provide information on national, county and soil type levels enabling a wide range of soil applications that demand huge amounts of data such as soil survey, precision agriculture and digital soil mapping.

Anahtar Kelimeler

Fourier-transform infrared spectroscopy, mid-infrared spectroscopy, partial least square regression, soil information system

Kaynakça

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