Vis-NIR spectroscopy as an eco-friendly method for monitoring pedoenvironmental variations and pedological assessments in Lut Watershed, Central Iran

Rasooli, Najmeh; Farpoor, Mohammad Hady; Mahmoodabadi, Majid; Esfandiarpour-Boroujeni, I.

doi:10.1016/j.still.2023.105808

Cited by 5 publications

(3 citation statements)

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“…In light of the literature data, determining the precise wavelength at which a substance-specific signal is recorded can be debatable. For calcium carbonate, specific wavelengths have been determined: 1800 nm, 2350 nm, 2360 nm [ 45 ], 2325 nm [ 46 ], 2338 nm [ 47 ], 2340 nm [ 36 ], and 2341 nm (Gomez et al 2008). However, in light of our analyses, the authors would suggest indicating wider ranges, related, on the one hand, to the diversity in soil samples and, on the other hand, the measurement method.…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, the selection of analytical material seems to be crucial in terms of prediction. Interfering factors are important, affecting the direct measurement of the spectral response of the soil in certain ranges or the specific “offset” of the entire spectral curve, due to high reflectance [ 36 ]. This is of great importance in the case of soils rich in calcium carbonate, such as those analyzed in this publication.…”

Section: Resultsmentioning

confidence: 99%

“…[ 34 , 35 ]. One of the substances that could interfere and significantly affect the quality of SOC prediction is calcium carbonate; a significant interrelation of the two components in the samples studied, in terms of spectral properties, was pointed out by Rasooli et al [ 36 ], among others.…”

Section: Introductionmentioning

confidence: 99%

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Using Spectroradiometry to Measure Organic Carbon in Carbonate-Containing Soils

Bartmiński,

Siedliska,

Siłuch

2024

Sensors

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This study explores the feasibility of analyzing soil organic carbon (SOC) in carbonate-rich soils using visible near-infrared spectroscopy (VIS-NIR). Employing a combination of datasets, feature groups, variable selection methods, and regression models, 22 modeling pipelines were developed. Spectral data and spectral data combined with carbonate contents were used as datasets, while raw reflectance, first-derivative (FD) reflectance, and second-derivative (SD) reflectance constituted the feature groups. The variable selection methods included Spearman correlation, Variable Importance in Projection (VIP), and Random Frog (Rfrog), while Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR) were the regression models. The obtained results indicated that the FD preprocessing method combined with RF, results in the model that is sufficiently robust and stable to be applied to soils rich in calcium carbonate.

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