Capability of vis-NIR Spectroscopy to Predict Selected Chemical Soil Properties in Isfahan Province

Khayamim, F; Khademi, H; Stenberg, Bo; Wetterlind, Johanna

doi:10.18869/acadpub.jstnar.19.72.8

Cited by 2 publications

(1 citation statement)

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“…The PLSR is able to correlate spectral variables in the range of (350-2500 nm) with the soil laboratory data, which sorts them in latent components or factors at the same time. Therefore, PLSR is able to extract the complex interactions between spectral variables and soil data [9,11,14,15]. Soil vis-NIR spectroscopy is a rapid, cheap, non-laborious, and eco-friendly technique which does not require preparation of soil samples.…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Erodible Fraction Using Multivariate Regression and Proximal Sensing Data in Arid Lands, South Egypt

Abd-Elazem,

El-Sayed,

Fadl

et al. 2024

Soil Systems

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Estimating soil erodible fraction based on basic soil properties in arid lands is a valuable research topic in the field of soil science and land management. The Proximal Sensing (PS) technique offers a non-destructive and efficient method to assess wind erosion potential in arid regions. By using Partial Least Squares Regression (PLSR) and Support Vector Machine (SVM) models and combining soil texture and chemical properties, determined through Visible-Near Infrared (vis-NIR) spectroscopy in 96 soil samples, this study aims to predict soil erodibility, soil organic matter (SOM), and calcium carbonate equivalent (CaCO3) in arid lands located in Elkobaneyya Valley, Aswan Governorate, Egypt. Results showed that the soil erodibility fraction (EF-Factor) had the highest values and possessed a strong relationship between slope and SOM of 0.01% in determining soil erodibility. The PLSR model performed better than SVM for estimating SOM, CaCO3, and EF-Factor. Furthermore, the results showed that the spectral responses of CaCO3 were observed in separate places in the wavelengths of 570, 649, 802, 1161, 1421, 1854, and 2362 nm, and the wavelengths with SOM parameter were 496, 658, 779, 1089, 1417, 1871, and 2423 nm. The EF-factor shows the highest significant correlation with spectral reflectance values at 526, 688, 744, 1418, 1442, 2292, and 2374 nm. The accuracy and performance of the PLSR model in estimating the EF-Factor using spectral reflectance data and the distribution of data points for both the calibration and validation data-sets indicate a good accuracy of the PLSR model, with RMSE values of 0.0921 and 0.0836 Mg h MJ−1 mm−1, coefficient of determination (R2) values of 0.931 and 0.76, and RPD values of 2.168 and 2.147, respectively.

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Section: Introductionmentioning

confidence: 99%