Selection of "Local" Models for Prediction of Soil Organic Matter Using a Regional Soil Vis-NIR Spectral Library. Issue 1 (January 2016)
- Record Type:
- Journal Article
- Title:
- Selection of "Local" Models for Prediction of Soil Organic Matter Using a Regional Soil Vis-NIR Spectral Library. Issue 1 (January 2016)
- Main Title:
- Selection of "Local" Models for Prediction of Soil Organic Matter Using a Regional Soil Vis-NIR Spectral Library
- Authors:
- Zeng, Rong
Zhao, Yu-Guo
Li, De-Cheng
Wu, Deng-Wei
Wei, Chang-Long
Zhang, Gan-Lin - Abstract:
- Abstract : Abstract: Soil spectral libraries have been established as a reference for predicting soil properties by visible and near-infrared (Vis-NIR) spectroscopy. Numerous studies show that predictions of soil properties over a local area can be improved by selecting an appropriate "local" subset from a large library; although these have usually been geographically local, they can be local in other than the geographic sense. We investigated prediction of soil organic matter at a local site using a regional soil Vis-NIR spectral library with 1, 365 samples. Models built using the entire library were compared with subsets selected by (i) parent material (Cali_pm), (ii) land use type (Cali_lu), (iii) material-land use combination (Cali_com), and (iv) spectral similarity (Cali_ss). Models were built by partial least-squares regression, and their performances were evaluated using two independent test sets, one for paddy field (Test_paddy) and another one for upland agriculture (Test_up). Prediction accuracy was measured by the ratio of percentage deviation (RPD) compared with models built on the entire library. Ratios of percentage deviation for Cali_lu increased from 1.58 to 1.65 (Test_up) and from 2.05 to 3.02 (Test_paddy); for Cali_ss, RPD increased from 1.58 to 1.89 (Test_up) and from 2.05 to 2.26 (Test_paddy). Cali_pm models performed well for Test_paddy (RPD = 2.76) but poorly for Test_up (RPD = 1.11). Cali_com models used the fewest number of samples and performedAbstract : Abstract: Soil spectral libraries have been established as a reference for predicting soil properties by visible and near-infrared (Vis-NIR) spectroscopy. Numerous studies show that predictions of soil properties over a local area can be improved by selecting an appropriate "local" subset from a large library; although these have usually been geographically local, they can be local in other than the geographic sense. We investigated prediction of soil organic matter at a local site using a regional soil Vis-NIR spectral library with 1, 365 samples. Models built using the entire library were compared with subsets selected by (i) parent material (Cali_pm), (ii) land use type (Cali_lu), (iii) material-land use combination (Cali_com), and (iv) spectral similarity (Cali_ss). Models were built by partial least-squares regression, and their performances were evaluated using two independent test sets, one for paddy field (Test_paddy) and another one for upland agriculture (Test_up). Prediction accuracy was measured by the ratio of percentage deviation (RPD) compared with models built on the entire library. Ratios of percentage deviation for Cali_lu increased from 1.58 to 1.65 (Test_up) and from 2.05 to 3.02 (Test_paddy); for Cali_ss, RPD increased from 1.58 to 1.89 (Test_up) and from 2.05 to 2.26 (Test_paddy). Cali_pm models performed well for Test_paddy (RPD = 2.76) but poorly for Test_up (RPD = 1.11). Cali_com models used the fewest number of samples and performed poorly for both test sets (RPD < 1.5). These results show the potential of using land use types or spectral similarity to select "local" models for prediction of soil organic matter using a regional spectral library. Abstract : Supplemental digital content is available in the text. … (more)
- Is Part Of:
- Soil science. Volume 181:Issue 1(2016)
- Journal:
- Soil science
- Issue:
- Volume 181:Issue 1(2016)
- Issue Display:
- Volume 181, Issue 1 (2016)
- Year:
- 2016
- Volume:
- 181
- Issue:
- 1
- Issue Sort Value:
- 2016-0181-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2016-01
- Subjects:
- SOM contents -- spectroscopy -- spectral library -- Vis-NIR
Soil science -- Periodicals
631.405 - Journal URLs:
- http://www.soilsci.com ↗
http://journals.lww.com ↗ - DOI:
- 10.1097/SS.0000000000000132 ↗
- Languages:
- English
- ISSNs:
- 0038-075X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8324.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 64.xml