Global soil characterization with VNIR diffuse reflectance spectroscopy
There has been growing interest in the use of diffuse infrared reflectance as a quick, inexpensive tool for soil characterization. In studies reported to date, calibration and validation samples have been collected at either a local or regional scale. For this study, we selected 3768 samples from al...
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| Published in | Geoderma Vol. 132; no. 3; pp. 273 - 290 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.06.2006
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | There has been growing interest in the use of diffuse infrared reflectance as a quick, inexpensive tool for soil characterization. In studies reported to date, calibration and validation samples have been collected at either a local or regional scale. For this study, we selected 3768 samples from all 50 U.S. states and two tropical territories and an additional 416 samples from 36 different countries in Africa (125), Asia (104), the Americas (75) and Europe (112). The samples were selected from the National Soil Survey Center archives in Lincoln, NE, USA, with only one sample per pedon and a weighted random sampling to maximize compositional diversity. Applying visible and near-infrared (VNIR) diffuse reflectance spectroscopy (DRS) to air-dry soil (<
2 mm) with auxiliary predictors including sand content or pH, we obtained validation root mean squared deviation (RMSD) estimates of 54 g kg
−
1
for clay, 7.9 g kg
−
1
for soil organic C (SOC), 5.6 g kg
−
1
for inorganic C (IC), 8.9 g kg
−
1
for dithionate–citrate extractable Fe (FEd), and 5.5 cmol
c kg
−
1
for cation exchange capacity (CEC) with NH
4 at pH
=
7. For all of these properties, boosted regression trees (BRT) outperformed PLS regression, suggesting that this might be a preferred method for VNIR-DRS soil characterization. Using BRT, we were also able to predict ordinal clay mineralogy levels for montmorillonite and kaolinite, with 88% and 96%, respectively, falling within one ordinal unit of reference X-ray diffraction (XRD) values (0–5 on ordinal scale). Given the amount of information obtained in this study with ∼4
×
10
3 samples, we anticipate that calibrations sufficient for many applications might be obtained with large but obtainable soil-spectral libraries (perhaps 10
4–10
5 samples). The use of auxiliary predictors (potentially from complementary sensors), supplemental local calibration samples and theoretical spectroscopy all have the potential to improve predictions. Our findings suggest that VNIR soil characterization has the potential to replace or augment standard soil characterization techniques where rapid and inexpensive analysis is required. |
|---|---|
| AbstractList | There has been growing interest in the use of diffuse infrared reflectance as a quick, inexpensive tool for soil characterization. In studies reported to date, calibration and validation samples have been collected at either a local or regional scale. For this study, we selected 3768 samples from all 50 U.S. states and two tropical territories and an additional 416 samples from 36 different countries in Africa (125), Asia (104), the Americas (75) and Europe (112). The samples were selected from the National Soil Survey Center archives in Lincoln, NE, USA, with only one sample per pedon and a weighted random sampling to maximize compositional diversity. Applying visible and near-infrared (VNIR) diffuse reflectance spectroscopy (DRS) to air-dry soil (<
2 mm) with auxiliary predictors including sand content or pH, we obtained validation root mean squared deviation (RMSD) estimates of 54 g kg
−
1
for clay, 7.9 g kg
−
1
for soil organic C (SOC), 5.6 g kg
−
1
for inorganic C (IC), 8.9 g kg
−
1
for dithionate–citrate extractable Fe (FEd), and 5.5 cmol
c kg
−
1
for cation exchange capacity (CEC) with NH
4 at pH
=
7. For all of these properties, boosted regression trees (BRT) outperformed PLS regression, suggesting that this might be a preferred method for VNIR-DRS soil characterization. Using BRT, we were also able to predict ordinal clay mineralogy levels for montmorillonite and kaolinite, with 88% and 96%, respectively, falling within one ordinal unit of reference X-ray diffraction (XRD) values (0–5 on ordinal scale). Given the amount of information obtained in this study with ∼4
×
10
3 samples, we anticipate that calibrations sufficient for many applications might be obtained with large but obtainable soil-spectral libraries (perhaps 10
4–10
5 samples). The use of auxiliary predictors (potentially from complementary sensors), supplemental local calibration samples and theoretical spectroscopy all have the potential to improve predictions. Our findings suggest that VNIR soil characterization has the potential to replace or augment standard soil characterization techniques where rapid and inexpensive analysis is required. There has been growing interest in the use of diffuse infrared reflectance as a quick, inexpensive tool for soil characterization. In studies reported to date, calibration and validation samples have been collected at either a local or regional scale. For this study, we selected 3768 samples from all 50 U.S. states and two tropical territories and an additional 416 samples from 36 different countries in Africa (125), Asia (104), the Americas (75) and Europe (112). The samples were selected from the National Soil Survey Center archives in Lincoln, NE, USA, with only one sample per pedon and a weighted random sampling to maximize compositional diversity. Applying visible and near-infrared (VNIR) diffuse reflectance spectroscopy (DRS) to air-dry soil (- 2 mm) with auxiliary predictors including sand content or pH, we obtained validation root mean squared deviation (RMSD) estimates of 54 g kg super(- 1) for clay, 7.9 g kg super(- 1) for soil organic C (SOC), 5.6 g kg super(- 1) for inorganic C (IC), 8.9 g kg super(- 1) for dithionate-citrate extractable Fe (FEd), and 5.5 cmol sub(c) kg super(- 1) for cation exchange capacity (CEC) with NH sub(4) at pH = 7. For all of these properties, boosted regression trees (BRT) outperformed PLS regression, suggesting that this might be a preferred method for VNIR-DRS soil characterization. Using BRT, we were also able to predict ordinal clay mineralogy levels for montmorillonite and kaolinite, with 88% and 96%, respectively, falling within one ordinal unit of reference X-ray diffraction (XRD) values (0-5 on ordinal scale). Given the amount of information obtained in this study with 4 x 10 super(3) samples, we anticipate that calibrations sufficient for many applications might be obtained with large but obtainable soil-spectral libraries (perhaps 10 super(4)-10 super(5) samples). The use of auxiliary predictors (potentially from complementary sensors), supplemental local calibration samples and theoretical spectroscopy all have the potential to improve predictions. Our findings suggest that VNIR soil characterization has the potential to replace or augment standard soil characterization techniques where rapid and inexpensive analysis is required. |
| Author | Reinsch, Thomas G. Brown, David J. Shepherd, Keith D. Walsh, Markus G. Dewayne Mays, M. |
| Author_xml | – sequence: 1 givenname: David J. surname: Brown fullname: Brown, David J. email: djbrown@montana.edu organization: Department of Land Resources and Environmental Sciences, Montana State University, USA – sequence: 2 givenname: Keith D. surname: Shepherd fullname: Shepherd, Keith D. organization: World Agroforestry Center (ICRAF), Nairobi, Kenya – sequence: 3 givenname: Markus G. surname: Walsh fullname: Walsh, Markus G. organization: World Agroforestry Center (ICRAF), Nairobi, Kenya – sequence: 4 givenname: M. surname: Dewayne Mays fullname: Dewayne Mays, M. organization: USDA National Soil Survey Center, Soil Survey Laboratory (NSSC-SSL), Lincoln, NE, USA – sequence: 5 givenname: Thomas G. surname: Reinsch fullname: Reinsch, Thomas G. organization: USDA National Soil Survey Center, Soil Survey Laboratory (NSSC-SSL), Lincoln, NE, USA |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17773940$$DView record in Pascal Francis |
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| CODEN | GEDMAB |
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| Keywords | PLS regression VNIR Diffuse reflectance spectroscopy Boosted regression trees Clay mineralogy Soil characterization Archive tropical zone global Soil investigation North America Reflection spectrometry diversity America pedons calibration Validation Center Territory cost Diffuse reflection Europe Africa sampling Regional scope Property of soil samples Weight Near infrared radiation Asia characterization Reflectance |
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| Snippet | There has been growing interest in the use of diffuse infrared reflectance as a quick, inexpensive tool for soil characterization. In studies reported to date,... |
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| SubjectTerms | Agronomy. Soil science and plant productions Biological and medical sciences Boosted regression trees Clay mineralogy Diffuse reflectance spectroscopy Earth sciences Earth, ocean, space Exact sciences and technology Fundamental and applied biological sciences. Psychology PLS regression Soil characterization Soils Surficial geology VNIR |
| Title | Global soil characterization with VNIR diffuse reflectance spectroscopy |
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