Carbonates and organic matter in soils characterized by reflected energy from 350-25000 nm wavelength Carbonates and organic matter in soils characterized by reflected energy from 350-25000 nm wavelength

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Vol17 No.7: 1636-1651

Title】Carbonates and organic matter in soils characterized by reflected energy from 350-25000 nm wavelength

Author】Najmeh ASGARI1; Shamsollah AYOUBI1*; Jose Alexandre Melo DEMATTÊ2; André Carnieletto DOTTO2

Addresses】1 Department of Soil Science, College of Agriculture, Isfahan University of Technology, 841156-83111, Isfahan, Iran; 2 Department of Soil Science, College of Agriculture Luiz de Queiróz, Av. Pádua Dias, 11, CEP 13418-900, Piracicaba, São Paulo, Brazil

Corresponding author】Shamsollah AYOUBI

Citation】Asgari N, Ayoubi S, Demattê JAM, et al. (2020) Carbonates and organic matter in soils characterized by reflected energy from 350-25000 nm wavelength. Journal of Mountain Science 17(7). https://doi.org/10.1007/s11629-019-5789-9

DOI】https://doi.org/10.1007/s11629-019-5789-9

Abstract】The soil carbon pool which is the sum of soil organic carbon (SOC) and soil inorganic carbon (SIC) is the second largest active store of carbon after the oceans and it is an important component of the global carbon cycle. Hence, accurate estimation of SOC and SIC as important carbon reservoirs in terrestrial ecosystems using fast, inexpensive and non-destructive methods is crucial for planning different climate change policies. The aim of the current research was to examine the effectiveness of Vis-NIR (visible and near-infrared spectroscopy: 350 - 2500 nm) and MIR (mid-infrared spectroscopy: 4000 - 400 cm-1) to characterize and estimate soil organic matter (SOM) and carbonates as main components of soil carbon stocks in Juneqan, Charmahal va Bakhtiari, Iran. To do so, a total of 548 soil samples from this area were collected (October 2015) and analyzed in laboratory (August 2017). In order to develop models capable of predicting SOM and carbonates content, seven spectral preprocessing methods comprising Absorbance (Abs), De-trending (Det), Continuum removal (CR), Savitzky-Golay derivatives (SGD), standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and Normalization by range (NBR) were conducted along with five multivariate methods including Random Forest (RF), Partial Least-Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Gaussian Process Regression (GPR).The content of carbonates caused spectral reflectance intensity to augment on several ranges of spectrum and strong absorption feature at 2338 nm in the Vis-NIR and 714, 850, 870, 1796, 2150 and 2510 cm-1 in the MIR spectra range. SOMabsorbed energy in several ranges, but also showed specific peaks in MIR. Both facts are associated with the structure of carbonates and SOMand its interaction with energy. The best combination of preprocessing and calibration models for carbonates quantification in Vis-NIR spectra was Det/PLSR (R2= 0.74, RPD= 2.19, RMSE= 6.45). For SOM, it was Det/PLSR (R2= 0.82, RPD= 2.41, RMSE= 0.75). The Det/RF (R2= 0.87, RPD= 2.44, RMSE= 0.66) for the quantification of SOM and MSC/RF (R2= 0.84, RPD= 2.84, RMSE= 5.50) for carbonates in MIR spectra range showed the greatest results. The stronger occurrence of spectral bands in MIR as well as the specificity of the absorption features indicated that this range produced better predictions. The obtained results highlighted the significant role of soil spectroscopy technique in predicting SOC and soil carbonates as key components of soil carbon stocks in the study area. Therefore, this technique can be used as a more cost-effective, time saving and nondestructive alternative to traditional methods of soil analysis.

Keywords】Modeling; Reflectance spectroscopy; Soil analysis; Preprocessing