Topsoil mapping using hyperspectral airborne data and multivariate regression modeling

Thomas Selige; Urs Schmidhalter

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Topsoil mapping using hyperspectral airborne data and multivariate regression modeling

In: Precision Agriculture â05

Authors:

Thomas Selige

Thomas Selige selige@wzw.tum.de

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Urs Schmidhalter

Urs Schmidhalter
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Type:: Chapter

Pages:: 537â545

DOI:: https://doi.org/10.3920/978-90-8686-549-9_067

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The spatial variability of topsoil texture and organic matter across fields was studied using airborne hyperspectral imagery to lead towards improved fine-scale soil mapping procedures. Two important topsoil features for precision farming applications, soil organic matter and texture, were correlated with spectral properties of the airborne HyMap scanner. Sand, clay, organic carbon and total nitrogen contents can be predicted quantitatively and simultaneously by a multivariate calibration approach using Partial Least Square Regression or Multiple Linear Regression. The suite of topsoil parameters can be determined simultaneously from a single spectral signature since the various features are represented by varying combinations of wavebands across the spectra.

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Precision Agriculture â05

E-Book ISBN:: 9789086865499

Publisher:: Wageningen Academic

Print Publication Date:: 24 Oct 2005

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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Topsoil mapping using hyperspectral airborne data and multivariate regression modeling

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Precision Agriculture â05

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Precision Agriculture â05