20190405
Author :
Moura-Bueno, J. M.
Dalmolin, R. S. D.
ten Caten, A.
Dotto, A. C.
Dematte, J. A. M.
Moura-Bueno, J. M.
Dalmolin, R. S. D.
ten Caten, A.
Dotto, A. C.
Dematte, J. A. M.
Title : Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions
Journal : Geoderma
Comment :
The aim of this research was to i) characterize and identify differences among spectra obtained for subtropical soils samples, ii) evaluate different pre-processing techniques and multivariate methods to propose SOC prediction models from the spectral data and iii) evaluate the performance of SOC prediction models calibrated from the stratification of a local library.
Spectral reflectance measurements were performed in the laboratory with a spectroradiometer in the range of 350–2500 nm. Six pre-processing techniques were applied to the spectra (including derivatives, normalization and non-linear transformations) and four multivariate calibration methods, namely, partial least squares regression (PLSR), multiple linear regression (MLR), support vector machines (SVM) and random forest (RF), were used with the objective of identifying the best combination to predict SOC.
Among the multivariate methods, PLSR had the best performance for SOC prediction for the total set of samples (R2 =0.74, RMSE=0.52% and RPIQ=2.23), followed by models SVM, MLR, and RF. The FR-CTS (n=445) group showed the best model performance after stratification, with R2 =0.82, RMSE=0.29% and RPIQ=2.60.
This study highlights the potential for the application of VIS-NIR-SWIR spectroscopy as a reliable and economical tool to quantify SOC concentrations.
Comment :
The aim of this research was to i) characterize and identify differences among spectra obtained for subtropical soils samples, ii) evaluate different pre-processing techniques and multivariate methods to propose SOC prediction models from the spectral data and iii) evaluate the performance of SOC prediction models calibrated from the stratification of a local library.
Spectral reflectance measurements were performed in the laboratory with a spectroradiometer in the range of 350–2500 nm. Six pre-processing techniques were applied to the spectra (including derivatives, normalization and non-linear transformations) and four multivariate calibration methods, namely, partial least squares regression (PLSR), multiple linear regression (MLR), support vector machines (SVM) and random forest (RF), were used with the objective of identifying the best combination to predict SOC.
Among the multivariate methods, PLSR had the best performance for SOC prediction for the total set of samples (R2 =0.74, RMSE=0.52% and RPIQ=2.23), followed by models SVM, MLR, and RF. The FR-CTS (n=445) group showed the best model performance after stratification, with R2 =0.82, RMSE=0.29% and RPIQ=2.60.
This study highlights the potential for the application of VIS-NIR-SWIR spectroscopy as a reliable and economical tool to quantify SOC concentrations.
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