Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques

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dc.contributor.authorNawar, Said
dc.contributor.authorMouazen, Abdul Mounem
dc.date.accessioned2017-01-17T11:58:54Z
dc.date.available2017-01-17T11:58:54Z
dc.date.issued2016-12-22
dc.description.abstractThe development of accurate visible and near infrared (vis-NIR) spectroscopy calibration models for selected soil properties based on mobile measurements is essential for site specific soil management at fine sampling scale. The objective of the present study was to compare the mobile and laboratory prediction performance of vis-NIR spectroscopy for total nitrogen (TN), total carbon (TC) and soil moisture content (MC) of field soil samples based on single field (SFD), two-field dataset (TFD), UK national dataset (UND) and European continental dataset (ECD) calibration models developed with linear and nonlinear data mining techniques including spiking. Fresh soil samples collected from fields in the UK, Czech Republic, Germany, Denmark and the Netherlands were scanned with a fibre-type vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305–2200 nm. After dividing spectra into calibration (75%) and validation (25%) sets, spectra in the calibration set were subjected to three multivariate calibration models, including the partial least squares regression (PLSR), multivariate adaptive regression splines (MARS) and support vector machines (SVM), with leave-one-out cross-validation to establish calibration models of TN, TC and MC. Results showed that the best model performance in cross-validation was obtained with MARS methods for the majority of dataset scales used, whereas the lowest model performance was obtained with the SFD. The effect of spiking was significant and the best model performance in general term was obtained when local samples collected from two target fields in the UK were spiked with the ECD, with coefficients of determination (R2) values of 0.96, 0.98 and 0.93, root mean square error (RMSE) of 0.01, 0.1 and 1.75, and ratio of performance to interquartile distance (RPIQ) of 7.46, 6.57 and 3.98, for TC, TN and MC, respectively. Therefore, these results suggest that ECD vis-NIR MARS calibration models can be successfully used to predict TN, TC and MC under both laboratory and mobile measurement conditions.en_UK
dc.identifier.citationNawar S, Mouazen AM, Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques, CATENA, Volume 151, April 2017, Pages 118–129en_UK
dc.identifier.issn0341-8162
dc.identifier.urihttp://dx.doi.org/10.1016/j.catena.2016.12.014
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/11283
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-Non-Commercial-No Derivatives 4.0 (CC BY-NC-ND 4.0). You are free to: Share — copy and redistribute the material in any medium or format. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: Non-Commercial — You may not use the material for commercial purposes. No Derivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectDiffuse reflectance spectroscopyen_UK
dc.subjectSpectral libraryen_UK
dc.subjectSoil propertiesen_UK
dc.subjectData miningen_UK
dc.subjectSpikingen_UK
dc.titlePredictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniquesen_UK
dc.typeArticleen_UK

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