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The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco
摘要： Soil organic matter (SOM) is a fundamental soil constituent. The estimation of this parameter in the laboratory using the classical method is complex time-consuming and requires the use of chemical reagents. The objectives of this study were to assess the accuracy of two laboratory measurement setups of the VIS-NIR spectroscopy in estimating SOM content and determine the important spectral bands in the SOM estimation model. A total of 115 soil samples were collected from the non-root zone (0-20 cm) of soil in the study area of the Triffa Plain and then analysed for SOM in the laboratory by the Walkley–Black method. The reflectance spectra of soil samples were measured by two protocols, Contact Probe (CP) and Pistol Grip (PG)) of the ASD spectroradiometer (350-2500 nm) in the laboratory. Partial least squares regression (PLSR) was used to develop the prediction models. The results of coefficient of determination (R2) and the root mean square error (RMSE) showed that the pistol grip offers reasonable accuracy with an R2 = 0.93 and RMSE = 0.13 compared to the contact probe protocol with an R2 = 0.85 and RMSE = 0.19. The near-Infrared range were more accurate than those in the visible range for predicting SOM using the both setups (CP and PG). The significant wavelengths contributing to the prediction of SOM for (PG) setup were at: 424, 597, 1432, 1484, 1830 ,1920, 2200, 2357 and 2430 nm, while were at 433, 587, 1380, 1431, 1929, 2200 and 2345 nm for (CP) setup.
关键词： soil organic matter,SOM analysis.,VIS-NIR spectroscopy,reflectance spectra,SOM estimation
更新于2025-09-23 15:21:01
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Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra
摘要： Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy ?elds in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coef?cient of determination (R2), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R2 = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ signi?cantly between the model with GA (R2 = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R2 = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R2 = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R2 = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R2 = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.
关键词： soil organic matter,paddy soil,vis-NIR spectra,pH,machine learning approaches
更新于2025-09-19 17:15:36
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[IEEE 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Amsterdam, Netherlands (2019.9.24-2019.9.26)] 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Closed-Loop Moving Windows Wavelength Selection Method With Application To Near-Infrared Spectroscopic Analysis
摘要： Wavelength selection plays a very important role in near-infrared (NIR) spectroscopy, which has always been an important research direction. Based on the reverse-deletion waveband idea, the closed-loop moving window-partial least squares (closed-loop MW-PLS) method was proposed, which can eliminate interference wavelengths and enable flexible multi-band wavelength selection. NIR analysis of soil organic matter was taken as an example to evaluate the performance of closed-loop MW-PLS. And the MW-PLS was also used for comparison. The proposed algorithm traversed all the sub-bands of the original range to perform forward and backward optimization. The forward optimization was just the original MW-PLS. Therefore, the closed-loop MW-PLS completely covered the MW-PLS. The results of soil organic matter indicated that the close-loop MW-PLS was strictly superior to original MW-PLS, and the method extension was meaningful and had no increase in operational complexity. We believe that the closed-loop MW-PLS method will have a wider application.
关键词： soil,organic matter,closed-loop moving window-partial least squares,wavelength model optimization,Near-infrared spectroscopic analysis
更新于2025-09-12 10:27:22
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Applications of Laser-Induced Breakdown Spectroscopy for Soil Analysis, Part I: Review of Fundamentals and Chemical and Physical Properties
摘要： Laser-induced breakdown spectroscopy (LIBS) has become a prominent analytical technique in recent years for real-time characterization of soil properties. However, only a few studies of soil chemical and physical properties have been reported using LIBS until recently. The aims of this article are to: (1) provide the basic principles of LIBS for soil analysis and (2) present the use of LIBS for soil pH, soil texture, and humification degree of soil organic matter (SOM). The second article will cover soil classification and soil elemental analysis, including plant nutrients, carbon (C), and toxic elements. LIBS is a multi-element analytical technique based on atomic spectroscopy that employs a high-energy laser pulse focused onto a sample surface to create a transient plasma. It is a spectroscopic analytical technique that requires very little or no sample preparation, examines each sample in seconds, and offers a flexible platform for the examination of a broad array of elements in the sample. LIBS also can be used to infer soil chemical and physical properties if a relationship exists between the chemical composition and the soil properties. With proper calibration, LIBS has a great potential for real-time in-field soil analysis and precision farming that could lead to improved soil management and agricultural production, and reduced agricultural environmental impacts.
关键词： humification degree of soil organic matter,soil texture,precision agriculture,soil sensing,soil analysis,soil pH
更新于2025-09-11 14:15:04
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A Self-Adaptive Model for the Prediction of Soil Organic Matter Using Mid-Infrared Photoacoustic Spectroscopy
摘要： Fast quantification of soil organic matter (SOM) is important in crop production and soil fertility evaluation. Fourier-transform infrared (FTIR) spectroscopy has been widely utilized for rapid, cost-effective, and non-destructive SOM determination. However, the lack of accuracy has limited the application of FTIR spectroscopy to quantitative SOM prediction because the models are built from a typical database, resulting in large errors in new independent samples. In this study, using 933 paddy soil samples collected in Lishui, China, a “self-adaptive” model was designed for predicting SOM content, in conjunction with Fourier-transform mid-infrared photoacoustic spectroscopy (FTIR-PAS). The resulting FTIR-PAS spectra afforded abundant soil information, reflected in O–H, N–H, and C–H vibrations (4000–2800 cm?1), C=O and C–H vibrations (2500–1200 cm?1), and the fingerprint region (1200–500 cm?1). The self-adaptive model was established by: (i) identification of soil samples, selected by Euclidean distance, with soil spectra to similar the target (unknown) soil sample and ranking of the Euclidean distance values in ascending order; (ii) selection of the optimal parameters to build a partial least squares (PLS) model based on an optimal calibration sample subset; and (iii) prediction and validation of the unknown soil sample. The predictive capabilities of the self-adaptive model and conventional PLS model were compared; the self-adaptive and conventional PLS models had R2 values of 0.9293 and 0.5796, root mean square errors of prediction of 1.65 and 3.26 g kg?1, and ratios of percentage deviation (RPD) of 3.18 and 1.62, respectively. Therefore, the self-adaptive model showed greater potential for application, having significantly enhanced applicability while improving the accuracy of prediction.
关键词： Fourier-transform mid-infrared photoacoustic spectroscopy,partial least squares,paddy soils,self-adaptive model,soil organic matter
更新于2025-09-11 14:15:04
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Elucidating molecular level impact of peat fire on soil organic matter by laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry
摘要： In this work, laser desorption ionization coupled with Fourier transform ion cyclotron resonance mass spectrometry (LDI–FTICRMS) was used to investigate the molecular composition of a peat fire and laboratory heated soil organic matter (SOM). SOM isolated from soils obtained from unburned and burned sites at Central Kalimantan, Indonesia, were analyzed with LDI–FTICRMS. About 7500 peaks were found and assigned with molecular formulas for each mass spectrum. SOM isolated from fire-affected soil sites are relatively more abundant in low oxygenated classes (e.g., O1–O5) and thermally stable compounds, including condensed hydrocarbon and nitrogen heterocyclic compounds. Abundances of highly condensed hydrocarbon compounds with carbon number > 30 were increased for the fire-affected SOM. In vivo heating experiments were conducted for SOM extracted from unburned sites, and the prepared SOMs were analyzed with LDI–FTICRMS. Overall, the same trend of change at the molecular level was observed from both the laboratory heated and the peat fire-affected SOM samples. In addition, it was observed that heat caused the degradation of SOM, generating lignin and tannin-type molecules. It was hypothesized that they were formed by thermal degradation of high molecular weight SOM. All the information presented in this study was obtained by consuming ~ 5 μg of sample. Therefore, this study shows that LDI–FTICRMS is a sensitive analytical technique that is effective in obtaining molecular level information of SOM.
关键词： Soil organic matter,Molecular transformation,LDI–FTICRMS,Peat fire
更新于2025-09-11 14:15:04