Synthesized use of VisNIR DRS and PXRF for soil characterization: Total carbon and total nitrogen

Wang, Dandan; Chakraborty, Somsubhra; Weindorf, David C.; Li, Bin; Sharma, Aakriti; Paul, Subhadip; Ali, Md. Nasim

doi:10.1016/j.geoderma.2014.12.011

Cited by 141 publications

(66 citation statements)

References 68 publications

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“…This outcome is thought to be due to a large amount of redundancy in the combined spectra. This contrasts to the findings of Wang et al (2015), Aldabaa et al (2015), and .…”

Section: Potential Of Synergistic Use Of Spectral Methods In Soil Anacontrasting

confidence: 98%

“…The data fusion approach, employed by Wang et al (2015), Aldabaa et al (2015), and Chakraborty et al (2015), was tested on a range of elements in the preliminary stages of this study. This involved concatenation of vis-NIR, MIR, and pXRF spectra, followed by Cubist modeling of the combined data.…”

Section: Potential Of Synergistic Use Of Spectral Methods In Soil Anamentioning

confidence: 99%

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Synergistic Use of Vis‐NIR, MIR, and XRF Spectroscopy for the Determination of Soil Geochemistry

O’Rourke

Minasny

Holden

et al. 2016

Soil Science Soc of Amer J

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Proposed legislation to secure and maintain soil quality in Europe has generated interest surrounding how best to characterize soil geochemistry, and how to assess and monitor soil contamination. Visible-near infrared (vis-NIr), mid-infrared (MIr), and portable X-ray fluorescence (pXrF) spectroscopy can reduce time and cost associated with new soil monitoring programs. Before becoming deployable, accuracy of these techniques needs to be quantified. This study investigated potential of these techniques to characterize a full suite of soil geochemistry (40 elements), pH, and soil organic carbon (SOC) in a diverse set of agricultural soils from the Irish National Soil Database (NSDB) archive. Cubist models were employed to assess goodness of fit from spectrally derived estimates of each soil property. Then a model ensemble, or model averaging, approach was tested to examine if combining model outcomes of either vis-NIr or MIr with pXrF could improve accuracy of soil property prediction. In total, 15 of the 42 soil properties could be predicted to a good accuracy status using individual spectral methods, mostly achieved by MIr and pXrF. Combining model outcomes of vis-NIr or MIr with pXrF resulted in a positive improvement, increasing the number of soil properties that could be predicted from 15 to 25. Most notably is the large number of trace elements (As, Cd, Co, Cu, Hg, Mn, Ni, and Zn) predicted to good or reasonable accuracy. It was concluded that the synergistic use of vis-NIr, MIr, and pXrF spectral methods is well placed as a tool to permit large scale routine soil monitoring.Abbreviations: ICP, inductively coupled plasma; IQ, interquartile range; MIR, midinfrared; NSDB, Irish National Soil Database; pXRF, portable X-ray fluorescence; RPIQ, ratio of performance to inter quartile distance; SOC, soil organic carbon; vis-Nir, visiblenear infrared.T he strategy for protection of soils in Europe (European Commission, 2006a) recognizes a number of degradation processes or 'threats' to important soil functions. Among these is soil contamination arising from the introduction of substances on/in the soil that hamper soil functions, or present risks to human health or the environment. Proposed legislation for the protection of soils in Europe outlines a plan for member states to identify contaminated sites and introduce a program of remediation (European Commission, 2006b). A lack of legislation for soil protection in Europe (aside from the Directive on the management of waste from extractive industries [European Commission, 2006c]) makes regulation of contaminated sites difficult ( Jones et al., 2012). In the 39 countries of the European Environment Agency, the total number of contaminated soil sites in 2011 was estimated to be 2.5 million. The key sources of soil contamination arose from waste disposal and treatment, industrial, and commercial activities (e.g., mining, oil extraction, and power plants), storage (e.g., oil, oil extraction, and obsolete chemicals), transport spills on land (e.g., oil and other hazardou...

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“…This outcome is thought to be due to a large amount of redundancy in the combined spectra. This contrasts to the findings of Wang et al (2015), Aldabaa et al (2015), and .…”

Section: Potential Of Synergistic Use Of Spectral Methods In Soil Anacontrasting

confidence: 98%

Section: Potential Of Synergistic Use Of Spectral Methods In Soil Anamentioning

confidence: 99%

Synergistic Use of Vis‐NIR, MIR, and XRF Spectroscopy for the Determination of Soil Geochemistry

O’Rourke

Minasny

Holden

et al. 2016

Soil Science Soc of Amer J

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“…Wang et al . () characterized total carbon and total nitrogen by the synthesized use of vis–NIR and PXRF and obtained a more stable and solid result than with single sensors. O'Rourke et al .…”

Section: Introductionmentioning

confidence: 99%

“…() used vis–NIR and PXRF to determine agronomic soil properties and concluded that using model averaging techniques improved or maintained the status of predictions. Previous studies combined two (vis–NIR and mid‐IR or vis–NIR and XRF) or three sensors (vis–NIR, mid‐IR and XRF) to predict soil properties by using either sensor data fusion or model averaging (Viscarra Rossel et al ., ; Wang et al ., ; O'Rourke et al ., ). The study of fusion from four sensors (vis–NIR, mid‐IR, XRF and LIBS) based on fusion of the sensor data and the sensor prediction results has not been done yet.…”

Section: Introductionmentioning

confidence: 99%

Multi‐sensor fusion for the determination of several soil properties in the Yangtze River Delta, China

Zhao

et al. 2018

European J Soil Science

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Summary Soil organic matter (SOM), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and pH are key chemical properties for evaluating soil fertility and quality. This study involved the integration of four soil sensors, visible near‐infrared (vis–NIR) spectrometer, mid‐infrared (mid‐IR) spectrometer, portable X‐ray fluorescence (PXRF) analyser and laser‐induced breakdown spectroscopy (LIBS), to achieve rapid measurement of these soil properties. A genetic algorithm and partial least‐squares regression (GA–PLSR) were used to select characteristic bands to reduce data redundancy. We then calibrated models from three aspects: models using partial least‐squares regression (PLSR) based on single sensor data, models using PLSR based on fused sensor data, involving data combined from the four sensors into a new dataset to create a data fusion (DF) model, and models with Bayesian model averaging (BMA) based on prediction results of fused sensor data, involving prediction results combined from the four sensors into a new dataset to form the BMA model. The results showed the following. (i) For the single sensor, the predictive performance decreased as follows: mid‐IR > vis–NIR > LIBS > PXRF. (ii) Compared with the single sensor approach, the DF approach slightly improved or even reduced prediction accuracy and caused a large amount of redundancy. We suggest that this approach is not able to improve predictive ability. (iii) The BMA approach achieved the best prediction for the six soil properties. Our findings suggest that model averaging of vis–NIR, mid‐IR and LIBS could be a reliable and stable approach for the fast measurement of soil properties. Highlights We used four proximal soil sensors to evaluate six key properties for evaluating soil fertility and quality. GA–PLSR was used to select characteristic bands. We compared predictions of six soil properties from single sensor, DF and BMA approaches. BMA predictions were more accurate than predictions from single and fused sensor data.

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“…Aldabaa et al (2015) recently proved the utility of the PXRF + VisNIR DRS system in rapid assessment of soil salinity. Similarly, Wang et al (2015) used a combined PXRF + VisNIR DRS approach for quantifying soil carbon and nitrogen simultaneously with excellent results. Combining these two approaches will allow for comprehensive assessment of both organic and inorganic soil contaminants and allow for instant quantitative results on-site.…”

Section: Introductionmentioning

confidence: 99%

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Chakraborty

Weindorf

et al. 2015

Science of The Total Environment

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Synthesized use of VisNIR DRS and PXRF for soil characterization: Total carbon and total nitrogen

Cited by 141 publications

References 68 publications

Synergistic Use of Vis‐NIR, MIR, and XRF Spectroscopy for the Determination of Soil Geochemistry

Synergistic Use of Vis‐NIR, MIR, and XRF Spectroscopy for the Determination of Soil Geochemistry

Multi‐sensor fusion for the determination of several soil properties in the Yangtze River Delta, China

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

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