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Mogens Humlekrog Greve

Visible–Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries

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Visible–Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries. / Knadel, Maria; A. Viscarra Rossel, Raphael ; Deng, Fan; Thomsen, Anton Gårde; Greve, Mogens Humlekrog.

I: Soil Science Society of America Journal, Bind 77, Nr. 2, 2013, s. 568-579.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Knadel, M, A. Viscarra Rossel, R, Deng, F, Thomsen, AG & Greve, MH 2013, 'Visible–Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries', Soil Science Society of America Journal, bind 77, nr. 2, s. 568-579. https://doi.org/10.2136/sssaj2012.0093

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Author

Knadel, Maria ; A. Viscarra Rossel, Raphael ; Deng, Fan ; Thomsen, Anton Gårde ; Greve, Mogens Humlekrog. / Visible–Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries. I: Soil Science Society of America Journal. 2013 ; Bind 77, Nr. 2. s. 568-579.

Bibtex

@article{1a9d1eaee34b4b949efd346e820d58b7,
title = "Visible–Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries",
abstract = "Spectroscopy is widely recognized as an effective tool for the analysis of soil constituents. It is an efficient alternative to conventional laboratory analysis that can be cumbersome, time consuming and expensive. The majority of studies on the use of spectroscopy focus on the spectroscopic modeling to predict soil properties. However, information derived from spectra can also be used to describe the soil and how it varies across landscapes. The reason is that spectra contain information on the fundamental composition of soil: its organic matter, and iron oxide, clay and carbonate minerals as well as on water and particle size. In this study we use visible near infrared (vis–NIR) spectra to describe topsoils across Denmark. We used 693 agricultural topsoil samples (0–30cm) from the Danish soil collection and measured them with a vis–NIR spectrometer. Spectra were collected in the range between 350–2500nm. We interpreted the soils by gleaning the organic and mineralogical information from the spectra. To summarize the information content in the spectra we performed a principal component analysis (PCA). The first three PC{\textquoteright}s explained 94% of the variability in the spectra. The scores from the PCA were clustered using k-means to help with interpretation. Soil properties of the clusters were described using the mean spectrum of each class. We mapped the scores of the first three principal components using ordinary kriging. These maps and a cluster map derived with k-means clustering were used in the final discussion. The spectroscopic cluster map showed clearly soils with large clay contents, soils that are predominantly sandy, those that are silty and those with large amounts of organic matter, respectively. Their distribution reflects the general pattern of soil variability in Denmark.",
author = "Maria Knadel and {A. Viscarra Rossel}, Raphael and Fan Deng and Thomsen, {Anton G{\aa}rde} and Greve, {Mogens Humlekrog}",
year = "2013",
doi = "10.2136/sssaj2012.0093",
language = "English",
volume = "77",
pages = "568--579",
journal = "Soil Science Society of America Journal",
issn = "0361-5995",
publisher = "Soil Science Society of America",
number = "2",

}

RIS

TY - JOUR

T1 - Visible–Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries

AU - Knadel, Maria

AU - A. Viscarra Rossel, Raphael

AU - Deng, Fan

AU - Thomsen, Anton Gårde

AU - Greve, Mogens Humlekrog

PY - 2013

Y1 - 2013

N2 - Spectroscopy is widely recognized as an effective tool for the analysis of soil constituents. It is an efficient alternative to conventional laboratory analysis that can be cumbersome, time consuming and expensive. The majority of studies on the use of spectroscopy focus on the spectroscopic modeling to predict soil properties. However, information derived from spectra can also be used to describe the soil and how it varies across landscapes. The reason is that spectra contain information on the fundamental composition of soil: its organic matter, and iron oxide, clay and carbonate minerals as well as on water and particle size. In this study we use visible near infrared (vis–NIR) spectra to describe topsoils across Denmark. We used 693 agricultural topsoil samples (0–30cm) from the Danish soil collection and measured them with a vis–NIR spectrometer. Spectra were collected in the range between 350–2500nm. We interpreted the soils by gleaning the organic and mineralogical information from the spectra. To summarize the information content in the spectra we performed a principal component analysis (PCA). The first three PC’s explained 94% of the variability in the spectra. The scores from the PCA were clustered using k-means to help with interpretation. Soil properties of the clusters were described using the mean spectrum of each class. We mapped the scores of the first three principal components using ordinary kriging. These maps and a cluster map derived with k-means clustering were used in the final discussion. The spectroscopic cluster map showed clearly soils with large clay contents, soils that are predominantly sandy, those that are silty and those with large amounts of organic matter, respectively. Their distribution reflects the general pattern of soil variability in Denmark.

AB - Spectroscopy is widely recognized as an effective tool for the analysis of soil constituents. It is an efficient alternative to conventional laboratory analysis that can be cumbersome, time consuming and expensive. The majority of studies on the use of spectroscopy focus on the spectroscopic modeling to predict soil properties. However, information derived from spectra can also be used to describe the soil and how it varies across landscapes. The reason is that spectra contain information on the fundamental composition of soil: its organic matter, and iron oxide, clay and carbonate minerals as well as on water and particle size. In this study we use visible near infrared (vis–NIR) spectra to describe topsoils across Denmark. We used 693 agricultural topsoil samples (0–30cm) from the Danish soil collection and measured them with a vis–NIR spectrometer. Spectra were collected in the range between 350–2500nm. We interpreted the soils by gleaning the organic and mineralogical information from the spectra. To summarize the information content in the spectra we performed a principal component analysis (PCA). The first three PC’s explained 94% of the variability in the spectra. The scores from the PCA were clustered using k-means to help with interpretation. Soil properties of the clusters were described using the mean spectrum of each class. We mapped the scores of the first three principal components using ordinary kriging. These maps and a cluster map derived with k-means clustering were used in the final discussion. The spectroscopic cluster map showed clearly soils with large clay contents, soils that are predominantly sandy, those that are silty and those with large amounts of organic matter, respectively. Their distribution reflects the general pattern of soil variability in Denmark.

U2 - 10.2136/sssaj2012.0093

DO - 10.2136/sssaj2012.0093

M3 - Journal article

VL - 77

SP - 568

EP - 579

JO - Soil Science Society of America Journal

JF - Soil Science Society of America Journal

SN - 0361-5995

IS - 2

ER -