Estimation Soil Organic Matter (SOM) Content Using Visible and Near Infrared Spectral data, PLSR and PCR Statistical Models

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Matinfar Hamidreza; MAHMODZADEH H.; Fariabi A.

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Journal: Iranian Journal of Remote Sensing & GIS Year:2018 | Volume:10 | Issue:2 Page(s): 15-32

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Title

Estimation Soil Organic Matter (SOM) Content Using Visible and Near Infrared Spectral data, PLSR and PCR Statistical Models

Author(s)

Matinfar Hamidreza | MAHMODZADEH H. | Fariabi A. | Issue Writer Certificate

Keywords

organic matterQ1

spectroscopyQ1

PLSRQ1

PCRQ1

principal component analysisQ1

Abstract

Soil organic matter is one of the most important Physical and chemical properties of soil that it is critical in determining the quality and management of soils. Quantify of soil organic carbon due to the high spatial variability and changes over time is difficult. Near-infrared-visible spectroscopy is a feasible method to reduce the time and cost to check the organic carbon. The aim of this study was to evaluate soil organic carbon through near-infrared and visible spectroscopy with the statistical models PLSR and PCR. For this purpose, 40 soil samples from depths of 0 to 30 cm were collected by systematic random method based on previous studies and determination of different classes of soils in the region. Chemical analysis of soils was performed according to standard methods. Spectral reflectance of soil samples in the range of 350 to 2500 nm was measured then after applying the preprocessing methods such as Savitzky and Golay filter, Soil organic carbon were calculated by principal component analysis (PCA), regression partial least squares (PLSR) and principal component regression (PCR) models. The results of this study showed that the Savitzky and Golay filter was the strongest preprocessing method for spectral data. Coefficients of determination (R2), root mean square error of Prediction (RMSE) and ratio of prediction to deviation (RPD) in the calibration and validation to predict organic matter, respectively, 0. 97, 0. 05, 5. 09 and 0. 85, 0. 14, 2. 78 respectively. Therefore, for dry and semi-arid soils of the PLSR model, it is more efficient to predict the organic carbon of the soil. The results showed that the PLSR model has better performance than the PCR model in soil organic carbon estimation.

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APA: Copy

Matinfar, Hamidreza, MAHMODZADEH, H., & Fariabi, A.. (2018). Estimation Soil Organic Matter (SOM) Content Using Visible and Near Infrared Spectral data, PLSR and PCR Statistical Models. REMOTE SENSING & GIS, 10(2 ), 15-32. SID. https://sid.ir/paper/358328/en

Vancouver: Copy

Matinfar Hamidreza, MAHMODZADEH H., Fariabi A.. Estimation Soil Organic Matter (SOM) Content Using Visible and Near Infrared Spectral data, PLSR and PCR Statistical Models. REMOTE SENSING & GIS[Internet]. 2018;10(2 ):15-32. Available from: https://sid.ir/paper/358328/en

IEEE: Copy

Hamidreza Matinfar, H. MAHMODZADEH, and A. Fariabi, “Estimation Soil Organic Matter (SOM) Content Using Visible and Near Infrared Spectral data, PLSR and PCR Statistical Models,” REMOTE SENSING & GIS, vol. 10, no. 2 , pp. 15–32, 2018, [Online]. Available: https://sid.ir/paper/358328/en

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