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Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

Sila AM, Shepherd KD, Pokhariyal GP - Chemometr Intell Lab Syst (2016)

Bottom Line: The root mean square error of prediction was computed using a one-third-holdout validation set.In summary, the results show that global models outperformed the subspace models.We, therefore, conclude that global models are more accurate than the local models except in few cases.

View Article: PubMed Central - PubMed

Affiliation: World Agroforestry Centre (ICRAF), P.O. Box 30677-00100 GPO, Nairobi, Kenya; School of Mathematics, University of Nairobi, P.O Box 30196-00100 GPO, Nairobi, Kenya.

ABSTRACT

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.

No MeSH data available.


Related in: MedlinePlus

ICRAF LDSF sampling layout at plot and subplot levels. The black dots indicated soil-sampling locations; larger (dashed) circles represented 0.01 ha sub-plots in which soil surface and vegetation observations were carried out. r was the subplot radius, and d was the center-point distance.
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f0010: ICRAF LDSF sampling layout at plot and subplot levels. The black dots indicated soil-sampling locations; larger (dashed) circles represented 0.01 ha sub-plots in which soil surface and vegetation observations were carried out. r was the subplot radius, and d was the center-point distance.

Mentions: Sampling for AfSIS library was carefully executed to obtain representative soil samples covering approximately 18.1 million km2 of the non-desert, including Madagascar [46]. To achieve this 60, 10 × 10 km sized “Sentinel Sites”, stratified by the major Koppen–Geiger climate zones of Africa [25], excluding some of the African countries which were no-go zones due to security reasons were used. Each sentinel site was subdivided into 16 sampling units (clusters), each cluster was further split into 10 smaller sampling units (plots). The sampling plot was designed to sample approximately 1000 m2 (0.1 ha or 30 ∗ 30 m) area Fig. 1. Longitude and latitude coordinates were generated for each plot and saved into a Geographical Positioning System (GPS) unit. Field crewmembers easily navigated the geo-referenced plots with the help of the GPS unit but when a point led to a difficult point to sample an alternative plot was established nearby and the new coordinates are saved into the GPS unit. Within a plot, four subplots were identified. To determine the subplot layouts [43], one field crewmember stood at the center marked as subplot 1 as shown in Fig. 2.


Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

Sila AM, Shepherd KD, Pokhariyal GP - Chemometr Intell Lab Syst (2016)

ICRAF LDSF sampling layout at plot and subplot levels. The black dots indicated soil-sampling locations; larger (dashed) circles represented 0.01 ha sub-plots in which soil surface and vegetation observations were carried out. r was the subplot radius, and d was the center-point distance.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834557&req=5

f0010: ICRAF LDSF sampling layout at plot and subplot levels. The black dots indicated soil-sampling locations; larger (dashed) circles represented 0.01 ha sub-plots in which soil surface and vegetation observations were carried out. r was the subplot radius, and d was the center-point distance.
Mentions: Sampling for AfSIS library was carefully executed to obtain representative soil samples covering approximately 18.1 million km2 of the non-desert, including Madagascar [46]. To achieve this 60, 10 × 10 km sized “Sentinel Sites”, stratified by the major Koppen–Geiger climate zones of Africa [25], excluding some of the African countries which were no-go zones due to security reasons were used. Each sentinel site was subdivided into 16 sampling units (clusters), each cluster was further split into 10 smaller sampling units (plots). The sampling plot was designed to sample approximately 1000 m2 (0.1 ha or 30 ∗ 30 m) area Fig. 1. Longitude and latitude coordinates were generated for each plot and saved into a Geographical Positioning System (GPS) unit. Field crewmembers easily navigated the geo-referenced plots with the help of the GPS unit but when a point led to a difficult point to sample an alternative plot was established nearby and the new coordinates are saved into the GPS unit. Within a plot, four subplots were identified. To determine the subplot layouts [43], one field crewmember stood at the center marked as subplot 1 as shown in Fig. 2.

Bottom Line: The root mean square error of prediction was computed using a one-third-holdout validation set.In summary, the results show that global models outperformed the subspace models.We, therefore, conclude that global models are more accurate than the local models except in few cases.

View Article: PubMed Central - PubMed

Affiliation: World Agroforestry Centre (ICRAF), P.O. Box 30677-00100 GPO, Nairobi, Kenya; School of Mathematics, University of Nairobi, P.O Box 30196-00100 GPO, Nairobi, Kenya.

ABSTRACT

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.

No MeSH data available.


Related in: MedlinePlus