Publication Details

Category Text Publication
Reference Category Journals
DOI 10.2136/vzj2012.0217
Title (Primary) Analysis of vegetation and soil patterns using hyperspectral remote sensing, EMI, and gamma-ray measurements
Author Lausch, A.; Zacharias, S. ORCID logo ; Dierke, C.; Pause, M.; Kühn, I. ORCID logo ; Doktor, D.; Dietrich, P. ORCID logo ; Werban, U. ORCID logo
Source Titel Vadose Zone Journal
Year 2013
Department CLE; BZF; MET
Volume 12
Issue 4
Language englisch
UFZ wide themes TERENO; RU5
Abstract The identification of spatial and temporal patterns of soil properties and moisture structures
is an important challenge in environmental and soil monitoring as well as for soil landscape
model approaches. This work examines the use of hyperspectral remote sensing techniques
for quantifying geophysical parameters from the hyperspectral reflectance of the vegetation
canopy. These can be used as proxies of the underlying soil and soil water conditions.
Different spectral index derivatives, single band reflectance, and spectral indices from the
airborne hyperspectral sensor AISA were quantified and tested in univariate and multivariate
regression models for their correlation with geophysical measurements with electromagnetic
induction (EMI) and gamma-ray spectrometry. The best univariate models for predicting electrical
conductivity based on spectral information were based on the vertical dipole EM38DD
V with an R2 = 0.54 with the spectral index Normalized Pigments Reflectance Index (NPCI) as
well as for the horizontal dipole EM38DD H with an R2 = 0.65 with the spectral index NPCI.
For predicting soil characteristics measured with gamma-ray spectrometry we received the
best model results for gTh with an R2 = 0.55 with the spectral index NPCI and gK with an R2
= 0.44 with the spectral index Triangular Vegetation Index (TVI) and NPCI. The combination
of variables including the geographical elevation was tested as the input for a multivariate
regression analysis. For EMI and gamma-ray measurements, the “elevation” was found to be
the most predictive variable and an integration of spectral indices into the elevation-based
model led to only a slight improvement in the predictive power for EMI. An improvement
could be made to explain the variance of gamma-ray measurement signals by combining
elevation and spectral information.
Persistent UFZ Identifier
Lausch, A., Zacharias, S., Dierke, C., Pause, M., Kühn, I., Doktor, D., Dietrich, P., Werban, U. (2013):
Analysis of vegetation and soil patterns using hyperspectral remote sensing, EMI, and gamma-ray measurements
Vadose Zone J. 12 (4) 10.2136/vzj2012.0217