Bayer, Anita and Bachmann, Martin and Müller, Andreas and Kaufmann, Hermann (2012) A Comparison of Feature-Based MLR and PLS Regression Techniques for the Prediction of Three Soil Constituents in a Degraded South African Ecosystem. Applied and Environmental Soil Science, 2012, pp. 1-20. Hindawi Publishing Corporation. doi: 10.1155/2012/971252. ISSN 1687-7667.
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Official URL: http://www.hindawi.com/journals/aess/2012/971252/
Abstract
The accurate assessment of selected soil constituents can provide valuable indicators to identify and monitor land changes coupled with degradation which are frequent phenomena in semiarid regions. Two approaches for the quantification of soil organic carbon, iron oxides, and clay content based on field and laboratory spectroscopy of natural surfaces are tested. (1) A physical approach which is based on spectral absorption feature analysis is applied. For every soil constituent, a set of diagnostic spectral features is selected and linked with chemical reference data by multiple linear regression (MLR) techniques. (2) Partial least squares regression (PLS) as an exclusively statistical multivariate method is applied for comparison. Regression models are developed based on extensive ground reference data of 163 sampled sites collected in the Thicket Biome, South Africa, where land changes are observed due to intensive overgrazing. The approaches are assessed upon their prediction performance and significance in regard to a future quantification of soil constituents over large areas using imaging spectroscopy.
Item URL in elib: | https://elib.dlr.de/78409/ | |||||||||||||||
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Document Type: | Article | |||||||||||||||
Title: | A Comparison of Feature-Based MLR and PLS Regression Techniques for the Prediction of Three Soil Constituents in a Degraded South African Ecosystem | |||||||||||||||
Authors: |
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Date: | 2012 | |||||||||||||||
Journal or Publication Title: | Applied and Environmental Soil Science | |||||||||||||||
Refereed publication: | Yes | |||||||||||||||
Open Access: | Yes | |||||||||||||||
Gold Open Access: | Yes | |||||||||||||||
In SCOPUS: | Yes | |||||||||||||||
In ISI Web of Science: | Yes | |||||||||||||||
Volume: | 2012 | |||||||||||||||
DOI : | 10.1155/2012/971252 | |||||||||||||||
Page Range: | pp. 1-20 | |||||||||||||||
Publisher: | Hindawi Publishing Corporation | |||||||||||||||
Series Name: | Quantitative Soil Spectroscopy | |||||||||||||||
ISSN: | 1687-7667 | |||||||||||||||
Status: | Published | |||||||||||||||
Keywords: | Imaging Spectroscopy, Soil organic carbon, degradation, South Africa, Iron oxides, multiple linear regression analysis, partial least regression analysis | |||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | |||||||||||||||
HGF - Program: | Space | |||||||||||||||
HGF - Program Themes: | Earth Observation | |||||||||||||||
DLR - Research area: | Raumfahrt | |||||||||||||||
DLR - Program: | R EO - Earth Observation | |||||||||||||||
DLR - Research theme (Project): | R - Vorhaben Fernerkundung der Landoberfläche (old) | |||||||||||||||
Location: | Oberpfaffenhofen | |||||||||||||||
Institutes and Institutions: | German Remote Sensing Data Center > Land Surface | |||||||||||||||
Deposited By: | Bayer, Anita Daniela | |||||||||||||||
Deposited On: | 28 Nov 2012 10:45 | |||||||||||||||
Last Modified: | 01 Oct 2020 08:53 |
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