Effect of the number of calibration samples on the prediction of several soil properties at the farm-scale

Debaene, Guillaume; Niedźwiecki, Jacek; Pecio, A.; Żurek, Anna

doi:10.1016/j.geoderma.2013.09.022

Cited by 82 publications

(50 citation statements)

References 36 publications

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“…Our results confirm previous research (Reeves et al, 2009;Stenberg et al, 2010;Debaene et al, 2014) on the suitability of VIS-NIRS for rapid analysis of several soil properties, especially N and SOC content (Aïchi et al, 2009;Ladoni et al, 2010). This was the first attempt at predicting Hh using VIS-NIRS technology (r 2 = 0.60; RMSE = 0.19), a very important parameter for light acidic soils.…”

Section: Support Vector Machine Classification Of the Samplessupporting

confidence: 89%

“…Thus, PCA is not ideal for grouping samples according to land management. The mathematical pretreatment did not improve the prediction results as was observed previously (Debaene et al, 2014). No other patterns were observed.…”

Section: Partial Least Square Regression and Predictionsupporting

confidence: 60%

“…Visible and near infrared spectroscopy (VIS-NIRS) is a rapid, low-cost, and efficient method for characterizing soils and can be used to analyze several constituents simultaneously (Brown et al, 2005;Reeves et al, 2009;Stenberg et al, 2010;Debaene et al, 2014). Moreover, the method is easy to implement, does not require any chemicals, and is non-destructive.…”

Section: Introductionmentioning

confidence: 99%

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Use of Vis-Nirs for Land Management Classification With a Support Vector Machine and Prediction of Soil Organic Carbon and Other Soil Properties

Debaene¹,

Pikuła²,

Niedźwiecki³

2014

Cienc. Inv. Agr.

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G. Debaene, D. Pikuła, and J. Niedźwiecki. 2014. Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties. Cien. Inv. Agr. 41(1): 21-32. The objective of this research was to investigate the effects of a long-term experiment on soil spectral properties and to develop prediction models of these properties (soil organic carbon (SOC), N, pH, Hh, P 2 O 5 , K 2 O, Ca, Mg, K, and Na content) from texturally homogeneous samples (loamy sand). To this aim, chemometric techniques, such as partial least square (PLS) regression and support vector machine (SVM) classification, were used. Our results show that visible and near infrared spectroscopy (VIS-NIRS) is suitable for the prediction of properties of texturally homogeneous samples. The effects of fertilizer applications were sufficient to modify the soil chemical composition to a range suitable for using VIS-NIRS for calibration and modeling purposes. The best results were obtained for SOC and N content prediction using the full dataset with cross-validation (r 2 = 0.76, RMSECV = 0.04, RPD = 2.02 and r 2 = 0.81, RMSECV = 0.01, RPD = 2.20, respectively) and with an independent validation dataset (r 2 = 0.70, RMSEP = 0.04, RPD = 1.80 and r 2 = 0.73, RMSEP = 0.03, RPD = 1.22, for SOC and N content, respectively). The use of fertilizers and the type of crop rotation appear to have a significant impact on soil spectral properties; the SVM methodology with a linear kernel function was able to classify soil samples as functions of the applied doses of organic and inorganic fertilizers with 75% accuracy with cross-validation and the type of crop rotation with more than 90% accuracy with full validation of separate datasets.

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Section: Support Vector Machine Classification Of the Samplessupporting

confidence: 89%

Section: Partial Least Square Regression and Predictionsupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

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Use of Vis-Nirs for Land Management Classification With a Support Vector Machine and Prediction of Soil Organic Carbon and Other Soil Properties

Debaene¹,

Pikuła²,

Niedźwiecki³

2014

Cienc. Inv. Agr.

Self Cite

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“…This result can be explained by the effects on reflectance spectra caused by variations in size, shape and arrangement of soil particles in the samples of the calibration set. In 199 samples from an experimental area in Poland, Debaene et al (2014) calculated an R 2 of 0.73 in the prediction of clay content, with RMSE of 0.32 %. The low RMSE value obtained by these authors was due to the homogeneity of soil types and the large size of the experimental area (53.6 ha).…”

Section: Clay = 4368 + (-7271×r420) + (4301×r440) + (-31600×r460) +mentioning

confidence: 99%

Potential of Spectroradiometry to Classify Soil Clay Content

Dotto¹,

Dalmolin²,

Caten

et al. 2016

Rev. Bras. Ciênc. Solo

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“…partial least square regression, Pls) and also to interpret spectra. Principal component analysis (Pca) or clustering methods based on spectral data are usually able to classify samples according to different soil properties of interest for a specific study (Debaene et al 2014a, Vasques et al 2014). most of the soil properties necessary for soil characterization according to the world reference base (wrb) for soil resources (world reference base…, 2014) have been successfully predicted with Vis-nir models In situ measures allow for faster and cheaper analyses and are environmentally friendly.…”

Section: Introductionmentioning

confidence: 99%

Visible and Near-Infrared Spectroscopy as a Tool for Soil Classification and Soil Profile Description

Debaene

Bartmiński

Niedźwiecki

et al. 2017

pjss

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this paper presents preliminary results of the use of visible and near-infrared (Vis-nir) spectroscopy for soil classification and soil profile examination. three experiments involving (1) three different soil types (albic luvisol, Gleyic Phaeozem, brunic arenosol), (2) three artificial micro-plots with similar texture (loamy sand, Gleyic Phaeozem) but different soil organic carbon (soc) content and (3) a soil profile (Fluvisol) have been investigated using Vis-nir spectroscopy. results indicated that Vis-nir is a promising technique for preliminary soil description and can classify soils according to soil properties (especially soc) and horizons. instead of complex chemical and physical analyses involved in routine soil profile classification, Vis-nir spectroscopy is suggested as a useful, rapid, and inexpensive tool for soil profile investigation.

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Effect of the number of calibration samples on the prediction of several soil properties at the farm-scale

Cited by 82 publications

References 36 publications

Use of Vis-Nirs for Land Management Classification With a Support Vector Machine and Prediction of Soil Organic Carbon and Other Soil Properties

Use of Vis-Nirs for Land Management Classification With a Support Vector Machine and Prediction of Soil Organic Carbon and Other Soil Properties

Potential of Spectroradiometry to Classify Soil Clay Content

Visible and Near-Infrared Spectroscopy as a Tool for Soil Classification and Soil Profile Description

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