Soil Organic Carbon Prediction by Vis-NIR Spectroscopy: Case Study the Kur-Aras Plain, Azerbaijan

Ismayilov, Amin I.; Feyziyev, Fikrat; Mammadov, Elton; Babayev, Maharram

doi:10.1080/00103624.2020.1729367

Cited by 23 publications

(9 citation statements)

References 45 publications

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“…The OC model’s performance is similar to earlier findings: R 2 = 0.84–0.93 [ 102 ], R 2 = 0.63–0.90 with RMSE = 6.40–0.78 [ 103 ], R 2 = 0.91 [ 104 ], R 2 = 0.85 with RMSE = 3.77 [ 105 ], R 2 = 0.57–0.7 [ 106 ], R 2 = 0.77–0.83 [ 107 , 108 ]; and R 2 = 0.764 with RMSE = 0.344 for validation [ 109 , 110 ]. However [ 111 , 112 ], found lower prediction performance ( R 2 values varying from 0.57 to 0.73 and RPD values ranging from 1.80 to 1.93) for soil OC models.…”

Section: Resultssupporting

confidence: 87%

Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland

et al. 2022

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As classical soil analysis is time-consuming and expensive, there is a growing demand for visible, near-infrared, and short-wave infrared (Vis-NIR-SWIR, wavelength 350–2500 nm) spectroscopy to predict soil properties. The objectives of this study were to investigate the effects of soil bunds on key soil properties and to develop regression models based on the Vis-NIR-SWIR spectral reflectance of soils in Aba Gerima, Ethiopia. Soil samples were collected from the 0–30 cm soil layer in 48 experimental teff (Eragrostis tef) plots and analysed for soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av. P), and potassium (av. K). We measured reflectance from air-dried, ground, and sieved soils with a FieldSpec 4 Spectroradiometer. We used regression models to identify and predict soil properties, as assessed by the coefficient of determination (R2), root mean square error (RMSE), bias, and ratio of performance to deviation (RPD). The results showed high variability (CV ≥ 35%) and substantial variation (P < 0.05 to P < 0.001) in soil texture, OC, and av. P in the catchment. Soil reflectance was lower from bunded plots. The pre-processing techniques, including multiplicative scatter correction, median filter, and Gaussian filter for OC, clay, and sand, respectively were used to transform the soil reflectance. Statistical results were: R2 = 0.71, RPD = 8.13 and bias = 0.12 for OC; R2 = 0.93, RPD = 2.21, bias = 0.94 for clay; and R2 = 0.85 with RPD = 7.54 and bias = 0.0.31 for sand with validation dataset. However, care is essential before applying the models to other regions. In conclusion, the findings of this study suggest spectroradiometry can supplement classical soil analysis. However, more research is needed to increase the prediction performance of Vis-NIR-SWIR reflectance spectroscopy to advance soil management interventions.

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Section: Resultssupporting

confidence: 87%

Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland

et al. 2022

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“…Applications of NIR and Vis-NIR spectral techniques with the PLS algorithm were found to be efficient for evaluating the soil organic carbon (SOC) in soils [14,[38][39][40][41][42][43][44][45][46], where the difference between all these studies are the type of soils, sample set representativeness, the number of soil samples, sample preparation, the preprocessing strategies and model validation methodologies.…”

Section: Total and Organic Carbonmentioning

confidence: 99%

Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review

Barra

Haefele

Sakrabani

et al. 2021

TrAC Trends in Analytical Chemistry

103

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“…Once the spectral results were produced, they were reprocessed to optimize the measurement of organic carbon and calibrate the methods for the best results. 120 MIR spectroscopy probes the range of 2500−25 000 nm and provides information on absorption bands and clear peak resolution. A study by Metzger et al was done to determine the quantity of lime needed to change the pH of soil to the target value best suited for the desired results, such as crop production.…”

Section: T H Imentioning

confidence: 99%

Innovative Vibrational Spectroscopy Research for Forensic Application

et al. 2023

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Soil Organic Carbon Prediction by Vis-NIR Spectroscopy: Case Study the Kur-Aras Plain, Azerbaijan

Cited by 23 publications

References 45 publications

Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland

Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland

Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review

Innovative Vibrational Spectroscopy Research for Forensic Application

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