Representative subset selection

Daszykowski, M.; Walczak, Beata; Massart, D.L.

doi:10.1016/s0003-2670(02)00651-7

Cited by 269 publications

(168 citation statements)

References 16 publications

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“…Scheme 9, with the training set (Dataset KSc) and test set (Dataset KSp) divided by the Kennard-Stone algorithm [36,37], was used to examine the research question of whether half of the total samples selected according to their spectral differences could be representative enough to calibrate a successful model for predicting the SOM content of the remaining samples. By computing the Euclidean distances on full spectra (350-2500 nm) between all pairwise spectra of soil samples, the Kennard-Stone algorithm first selected the two samples farthest apart from each other.…”

Section: Plsr Modeling Of Soil Organic Mattermentioning

confidence: 99%

“…These selected samples were used as training set (Dataset KSc), while the remaining 54 samples were used as test set (Dataset KSp). The Kennard-Stone algorithm has been regarded as an effective method for the selection of a representative subset in the VNIR modeling of soil properties [7,22,37]. This algorithm, however, has seldom been applied using spectra from soil samples without air-drying, grinding and 2-mm sieving pretreatment.…”

Section: Plsr Modeling Of Soil Organic Mattermentioning

confidence: 99%

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Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes

et al. 2014

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Abstract:The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set with mixed land-use types can be applied in the SOM prediction of cropland soil samples (r 2 Pre = 0.66, RMSE = 2.78 g•kg −1 , residual prediction deviation (RPD) = 1.45). The models calibrated from cropland soil samples, however, cannot be transferred to the SOM prediction of soil samples with diverse land-use types and different SOM ranges. Wavelengths in the region of 350-800 nm and around 1900 nm are important for SOM estimation. The correlation analysis reveals that the spectral wavelengths from the soil samples with and without the air-drying, grinding and 2-mm sieving pretreatment are not linearly correlated at each wavelength in the region of 350-1000 nm, which is an important spectral region for SOM estimation in riparian landscapes. This result explains why the models calibrated from samples without pretreatment fail in the SOM estimation. The Kennard-Stone algorithm performed well in the selection of a representative subset for SOM estimation using the spectra of soil samples with pretreatment, but failed in soil samples without the pretreatment. Our study also demonstrates that a widely applicable SOM prediction model for riparian landscapes should be based on a wide range of SOM content.

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Section: Plsr Modeling Of Soil Organic Mattermentioning

confidence: 99%

Section: Plsr Modeling Of Soil Organic Mattermentioning

confidence: 99%

Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes

et al. 2014

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“…Stone algorithm (Daszykowski et al, 2002), 2) consistent spatial distribution within Germany, 3) 127 exclusion of sites with SOC content >87 g kg -1 in any horizon, as such soils may be organic (> 30% 128 organic substance) or in transition between organic and mineral soils and it was assumed that the 129 processes governing the variability of SOC in organic soils differ from those in mineral soils, and 4) 130 representative mapping of land use, soil type and carbon stock. 131…”

mentioning

confidence: 99%

Hot regions of labile and stable soil organic carbon in Germany – Spatial variability and driving factors

Vos¹,

Jaconi²,

Jacobs³

et al. 2017

Preprint

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Abstract. Atmospheric carbon dioxide levels can be mitigated by sequestering carbon in the soil. Sequestration can be facilitated by agricultural management, but its influence is not the same on all soil carbon pools, as labile pools with high turnover may be accumulated much faster, but are also more vulnerable to losses. The aims of this study were to (1) assess how soil organic carbon (SOC) is distributed among SOC fractions on national scale in Germany, (2) identify factors influencing this distribution and (3) identify regions with high vulnerability to SOC losses. The SOC content and proportion of two different SOC fractions were estimated for more than 2500 mineral topsoils (

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“…The selection of a calibration set is important in order to ensure the stability and accuracy of the VNIR prediction model [7]. The selection process aims at selecting samples that are representative enough to reveal relationships between component concentrations (e.g., SOM) and VNIR spectra.…”

Section: Introductionmentioning

confidence: 99%

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

et al. 2017

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Abstract:The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting the calibration set for constructing the VNIR prediction model merely consider either the gradients of SOM or the soil VNIR spectra and neglect the influence of environmental variables. However, soil samples generally present a strong spatial variability, and, thus, the relationship between the SOM content and VNIR spectra may vary with respect to locations and surrounding environments. Hence, VNIR prediction models based on conventional calibration set selection methods would be biased, especially for estimating highly spatially variable soil content (e.g., SOM). To equip the calibration set selection method with the ability to consider SOM spatial variation and environmental influence, this paper proposes an improved method for selecting the calibration set. The proposed method combines the improved multi-variable association relationship clustering mining (MVARC) method and the Rank-Kennard-Stone (Rank-KS) method in order to synthetically consider the SOM gradient, spectral information, and environmental variables. In the proposed MVARC-R-KS method, MVARC integrates the Apriori algorithm, a density-based clustering algorithm, and the Delaunay triangulation. The MVARC method is first utilized to adaptively mine clustering distribution zones in which environmental variables exert a similar influence on soil samples. The feasibility of the MVARC method is proven by conducting an experiment on a simulated dataset. The calibration set is evenly selected from the clustering zones and the remaining zone by using the Rank-KS algorithm in order to avoid a single property in the selected calibration set. The proposed MVARC-R-KS approach is applied to select a calibration set in order to construct a VNIR prediction model of SOM content in the riparian areas of the Jianghan Plain in China. Results indicate that the calibration set selected using the MVARC-R-KS method is representative of the component concentration, spectral information, and environmental variables. The MVARC-R-KS method can also select the calibration set for constructing a VNIR model of SOM content with a relatively higher-fitting degree and accuracy by comparing it to classical calibration set selection methods.

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Representative subset selection

Cited by 269 publications

References 16 publications

Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes

Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes

Hot regions of labile and stable soil organic carbon in Germany – Spatial variability and driving factors

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

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