Combining X‐ray Computed Tomography and Visible Near‐Infrared Spectroscopy for Prediction of Soil Structural Properties
In this study, the potential of using vis‐NIR spectroscopy to predict X‐ray CT derived soil structural properties was investigated. In this study, 127 soil samples from six agricultural fields within Denmark with a wide range of textural properties and organic C (OC) contents were studied. Macroporo...
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| Định dạng: | BB |
| Ngôn ngữ: | English |
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2020
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oai:localhost:DHTL-96792020-11-06T03:35:02Z Combining X‐ray Computed Tomography and Visible Near‐Infrared Spectroscopy for Prediction of Soil Structural Properties Katuwal, S. Hermansen, C. Knadel, M. Moldrup, P. Greve, M.H. de Jonge, L.W. Computed tomography Hounsfield units Multiple linear regression Principal component analysis Partial least squares regression In this study, the potential of using vis‐NIR spectroscopy to predict X‐ray CT derived soil structural properties was investigated. In this study, 127 soil samples from six agricultural fields within Denmark with a wide range of textural properties and organic C (OC) contents were studied. Macroporosity (>1.2 mm in diameter) and CTmatrix (the density of the field‐moist soil matrix devoid of large macropores and stones) were determined from X‐ray CT scans of undisturbed soil cores (19 by 20 cm). Both macroporosity and CTmatix are soil structural properties that affect the degree of preferential transport. Bulk soils from the 127 sampling locations were scanned with a vis‐NIR spectrometer (400–2500 nm). Macroporosity and CTmatrix were statistically predicted with partial least squares regression (PLSR) using the vis‐NIR data (vis‐NIR‐PLSR) and multiple linear regression (MLR) based on soil texture and OC. The statistical prediction of macroporosity was poor, with both vis‐NIR‐PLSR and MLR (R2 < 0.45, ratio of performance to deviation [RPD] < 1.4, and ratio of performance to interquartile distance [RPIQ] < 1.8). The CTmatrix was predicted better (R2 > 0.65, RPD > 1.5, and RPIQ > 2.0) combining the methods. The results illustrate the potential applicability of vis‐NIR spectroscopy for rapid assessment/prediction of CTmatrix. https://acsess.onlinelibrary.wiley.com/doi/10.2136/vzj2016.06.0054 2020-11-06T03:35:01Z 2020-11-06T03:35:01Z 2018 BB 1539-1663 http://tailieuso.tlu.edu.vn/handle/DHTL/9679 en Vadose Zone Journal, Volume 17, Issue 1 (2018), pp.1-13 |
| institution |
Trường Đại học Thủy Lợi |
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DSpace |
| language |
English |
| topic |
Computed tomography Hounsfield units Multiple linear regression Principal component analysis Partial least squares regression |
| spellingShingle |
Computed tomography Hounsfield units Multiple linear regression Principal component analysis Partial least squares regression Katuwal, S. Combining X‐ray Computed Tomography and Visible Near‐Infrared Spectroscopy for Prediction of Soil Structural Properties |
| description |
In this study, the potential of using vis‐NIR spectroscopy to predict X‐ray CT derived soil structural properties was investigated. In this study, 127 soil samples from six agricultural fields within Denmark with a wide range of textural properties and organic C (OC) contents were studied. Macroporosity (>1.2 mm in diameter) and CTmatrix (the density of the field‐moist soil matrix devoid of large macropores and stones) were determined from X‐ray CT scans of undisturbed soil cores (19 by 20 cm). Both macroporosity and CTmatix are soil structural properties that affect the degree of preferential transport. Bulk soils from the 127 sampling locations were scanned with a vis‐NIR spectrometer (400–2500 nm). Macroporosity and CTmatrix were statistically predicted with partial least squares regression (PLSR) using the vis‐NIR data (vis‐NIR‐PLSR) and multiple linear regression (MLR) based on soil texture and OC. The statistical prediction of macroporosity was poor, with both vis‐NIR‐PLSR and MLR (R2 < 0.45, ratio of performance to deviation [RPD] < 1.4, and ratio of performance to interquartile distance [RPIQ] < 1.8). The CTmatrix was predicted better (R2 > 0.65, RPD > 1.5, and RPIQ > 2.0) combining the methods. The results illustrate the potential applicability of vis‐NIR spectroscopy for rapid assessment/prediction of CTmatrix. |
| author2 |
Hermansen, C. |
| author_facet |
Hermansen, C. Katuwal, S. |
| format |
BB |
| author |
Katuwal, S. |
| author_sort |
Katuwal, S. |
| title |
Combining X‐ray Computed Tomography and Visible Near‐Infrared Spectroscopy for Prediction of Soil Structural Properties |
| title_short |
Combining X‐ray Computed Tomography and Visible Near‐Infrared Spectroscopy for Prediction of Soil Structural Properties |
| title_full |
Combining X‐ray Computed Tomography and Visible Near‐Infrared Spectroscopy for Prediction of Soil Structural Properties |
| title_fullStr |
Combining X‐ray Computed Tomography and Visible Near‐Infrared Spectroscopy for Prediction of Soil Structural Properties |
| title_full_unstemmed |
Combining X‐ray Computed Tomography and Visible Near‐Infrared Spectroscopy for Prediction of Soil Structural Properties |
| title_sort |
combining x‐ray computed tomography and visible near‐infrared spectroscopy for prediction of soil structural properties |
| publishDate |
2020 |
| url |
http://tailieuso.tlu.edu.vn/handle/DHTL/9679 |
| work_keys_str_mv |
AT katuwals combiningxraycomputedtomographyandvisiblenearinfraredspectroscopyforpredictionofsoilstructuralproperties |
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