Carbonates and organic matter in soils characterized by reflected energy from 350-25000 nm wavelength
文献类型：期刊论文
| 作者 | Asgari, Najmeh; Ayoubi, Shamsollah; Dematte, Jose Alexandre Melo; Dotto, Andre Carnieletto |
| 刊名 | JOURNAL OF MOUNTAIN SCIENCE
 |
| 出版日期 | 2020 |
| 卷号 | 17期号:7页码:1636-1651 |
| 关键词 | Modeling Reflectance spectroscopy Soil analysis Preprocessing |
| ISSN号 | 1672-6316 |
| DOI | 10.1007/s11629-019-5789-9 |
| 文献子类 | Article |
| 英文摘要 | 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 ofcarbonatescaused 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. SOM absorbed energy in several ranges, but also showed specific peaks in MIR. Both facts are associated with the structure ofcarbonatesand SOM and its interaction with energy. The best combination of preprocessing and calibration models for carbonates quantification in Vis-NIR spectra was Det/PLSR (R-2= 0.74, RPD= 2.19, RMSE= 6.45). For SOM, it was Det/PLSR (R-2= 0.82, RPD= 2.41, RMSE= 0.75). The Det/RF (R-2= 0.87, RPD= 2.44, RMSE= 0.66) for the quantification of SOM and MSC/RF (R-2= 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. |
| 电子版国际标准刊号 | 1993-0321 |
| 语种 | 英语 |
| WOS记录号 | WOS:000540675700001 |
| 源URL | [http://ir.imde.ac.cn/handle/131551/50443]  |
| 专题 | Journal of Mountain Science_Journal of Mountain Science-2020_Vol17 No.7 |
| 推荐引用方式 GB/T 7714 | Asgari, Najmeh,Ayoubi, Shamsollah,Dematte, Jose Alexandre Melo,et al. Carbonates and organic matter in soils characterized by reflected energy from 350-25000 nm wavelength[J]. JOURNAL OF MOUNTAIN SCIENCE,2020,17(7):1636-1651. |
| APA | Asgari, Najmeh,Ayoubi, Shamsollah,Dematte, Jose Alexandre Melo,&Dotto, Andre Carnieletto.(2020).Carbonates and organic matter in soils characterized by reflected energy from 350-25000 nm wavelength.JOURNAL OF MOUNTAIN SCIENCE,17(7),1636-1651. |
| MLA | Asgari, Najmeh,et al."Carbonates and organic matter in soils characterized by reflected energy from 350-25000 nm wavelength".JOURNAL OF MOUNTAIN SCIENCE 17.7(2020):1636-1651. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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