Rapid estimation of soil cation exchange capacity through sensor data fusion of portable XRF spectrometry and Vis-NIR spectroscopy

Wan, Meixiang; Hu, Wenyou; Qu, Mingkai; Li, Weidong; Zhang, Chuanrong; Kang, Junfeng; Hong, Yongsheng; Chen, Yong; Huang, Biao

doi:10.1016/j.geoderma.2019.114163

Cited by 71 publications

(26 citation statements)

References 50 publications

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“…For the SF-PLS approach, the number of latent variables was determined according to the best cross-validation that resulted in the maximum R 2 and lowest RMSE. The regression based on SVM is a linear machine learning method [ 67 ] that uses the most prominent data (referred to as support vectors) for regression and can be adopted for non-linear modeling by using appropriate kernels [ 54 ]. For SF-SVM, we resorted to the epsilon-SVM algorithm, which uses the radial-based kernel.…”

Section: Methodsmentioning

confidence: 99%

“…A third approach is to combine spectral data (denoted here as spectra fusion (SF)) in one matrix, which is subjected to linear or non-linear analysis. Furthermore, the majority of papers reporting the fusion of vis-NIR and XRF data focused on the prediction of one or limited number of soil attributes, e.g., soil textural attributes [ 44 ], TN and TC [ 43 ], textural attributes, pH [ 53 ], CEC [ 54 ], and chromium [ 48 ]. Although O’Rourke et al [ 26 ] combined the vis-NIR and XRF data for the analysis of a wide range of soil attributes, they have explored the averaging data fusion methods only.…”

Section: Introductionmentioning

confidence: 99%

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Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Tavares

Molin

Javadi

et al. 2020

Sensors

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Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data. Six data fusion approaches were evaluated and compared against individual models using relative improvement (RI). The data fusion approaches included (i) two spectra fusion approaches, which simply combined the data of both sensors in a merged dataset, followed by support vector machine (SF-SVM) and PLS (SF-PLS) regression analysis; (ii) two model averaging approaches using the Granger and Ramanathan (GR) method; and (iii) two data fusion methods based on least squares (LS) modeling. For the GR and LS approaches, two different combinations of inputs were used for MLR. The GR2 and LS2 used the prediction of individual sensors, whereas the GR3 and LS3 used the individual sensors prediction plus the SF-PLS prediction. The individual vis-NIR models showed the best results for clay and OM prediction (RPD ≥ 2.61), while the individual XRF models exhibited the best predictive models for CEC, V, ex-K, ex-Ca, and ex-Mg (RPD ≥ 2.57). For eight out of nine soil attributes studied (clay, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg), the combined use of vis-NIR and XRF sensors using at least one of the six data fusion approaches improved the accuracy of the predictions (with RI ranging from 1 to 21%). In general, the LS3 model averaging approach stood out as the data fusion method with the greatest number of attributes with positive RI (six attributes; namely, clay, CEC, pH, ex-P, ex-K, and ex-Mg). Meanwhile, no single approach was capable of exploiting the synergism between sensors for all attributes of interest, suggesting that the selection of the best data fusion approach should be attribute-specific. The results presented in this work evidenced the complementarity of XRF and vis-NIR sensors to predict fertility attributes in tropical soils, and encourage further research to find a generalized method of data fusion of both sensors data.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Tavares

Molin

Javadi

et al. 2020

Sensors

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“…Keen interest in the use of NIR spectroscopy for analysis of soil quality and contamination can be deducted from a number of recently published articles (e.g., refs. [85–87]).…”

Section: Miniaturized Nir Spectroscopy In Practical Applicationsmentioning

confidence: 99%

Principles and Applications of Miniaturized Near‐Infrared (NIR) Spectrometers

Beć

Grabska

Huck

2020

Chemistry A European J

224

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This review article focuses on the principles and applications of miniaturized near‐infrared (NIR) spectrometers. This technology and its applicability has advanced considerably over the last few years and revolutionized several fields of application. What is particularly remarkable is that the applications have a distinctly diverse nature, ranging from agriculture and the food sector, through to materials science, industry and environmental studies. Unlike a rather uniform design of a mature benchtop FTNIR spectrometer, miniaturized instruments employ diverse technological solutions, which have an impact on their operational characteristics. Continuous progress leads to new instruments appearing on the market. The current focus in analytical NIR spectroscopy is on the evaluation of the devices and associated methods, and to systematic characterization of their performance profiles.

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“…Ion exchange is a natural process with certain applications in agriculture, environmental management, industries, laboratory, geotechnical and other soil reclamation processes [15,28]. In agriculture and environmental management it plays an important role of soil nutrient dynamics, retention and supply, fertility, base saturation, buffering capacity, also the fate of heavy metals and pesticides depends on the ion exchange capacity of soil [29]. Different soil reclamation processes whether it may be acid soil or alkaline soil, follows the principle of ion exchange.…”

Section: Importance Of Ion Exchangementioning

confidence: 99%

Ion Exchange: The Most Important Chemical Reaction on Earth after Photosynthesis

Meetei

Devi

Chanu

2020

IRJPAC

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Ion exchange is the interchange of equivalent amount of ions from the solution with ions which are swarming in a boundary of charged surface in equilibrium. It is developed due to the presence of charge in the soil colloids or layer lattice clay minerals. The source of charge developed in the colloidal surface site of soil is mainly from two processes viz. isomorphous substitution and pH dependent charge. The charge can be positive or negative due to the exchange reaction in the layer lattice. The ion exchange capacity is the sum of cation exchange capacity (CEC) and anion exchange capacity (AEC). It depends on the types of soil and the amount of charge present in the layer lattice colloidal structure. With high negative charge in the lattice surface the CEC increases and with positive charge the AEC. Ions with higher charge have larger affinity to adsorbed more strongly than lower. Ion exchange capacity in soil has the ability to retained more nutrients in the form of cations or anions making available to plant for a long time which improved the fertility of soil. Leaching loss of different nutrients from the soil is reduced by holding different ions. Ion exchange processes have been widely used for heavy metal removal for waste water treatment and water purification because of its high remedial capacity, high removal efficiency and fast kinetic. Due to its applications in agriculture, environmental management, industries, waste water treatment in mining industries, laboratory, nanotechnology, geotechnical and other soil reclamation processes it is considered as the second most important reaction in the globe after photosynthesis.

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Rapid estimation of soil cation exchange capacity through sensor data fusion of portable XRF spectrometry and Vis-NIR spectroscopy

Cited by 71 publications

References 50 publications

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Principles and Applications of Miniaturized Near‐Infrared (NIR) Spectrometers

Ion Exchange: The Most Important Chemical Reaction on Earth after Photosynthesis

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