Soil reflectance modeling & hyperspectral mixture analysis: Towards vegetation spectra minimizing the soil background contamination

Author

Somers, B. ; Tits, T. ; Verstraeten, W.W. ; Coppin, P.

Author_Institution

Dept. of Biosystems, Katholieke Univ. Leuven, Leuven, Belgium

fYear

2010

fDate

14-16 June 2010

Firstpage

1

Lastpage

4

Abstract

Soil moisture variations dominate the spectral reflectance of soils in the 350-2500 nm wavelength domain and affect the effectiveness of spectral indices used to monitor variations in soil and vegetation properties. Removing soil moisture effects in spectral images is critical for agricultural remote sensing. A soil moisture reflectance model is successfully applied in combination with a hyperspectral mixture analysis approach to reduce soil moisture effects in (simulated) remote sensing images of citrus orchards, improving as such the site-specific monitoring of crop status.

Keywords

geophysical signal processing; moisture; reflectivity; remote sensing; soil; vegetation mapping; agricultural remote sensing; citrus orchards; crop status site specific monitoring; hyperspectral mixture analysis; soil background contamination minimisation; soil moisture effect removal; soil moisture reflectance model; soil moisture variations; soil property variations; soil reflectance modeling; soil spectral indices; soil spectral reflectance; spectral images; vegetation property variations; vegetation spectra; wavelength 350 nm to 2500 nm; Agriculture; Analytical models; Reflectivity; Remote sensing; Soil moisture; Vegetation mapping; ASD; Citrus; chlorophyll; radiative transfer model; unmixing; vegetation index; water;

fLanguage

English

Publisher

ieee

Conference_Titel

Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010 2nd Workshop on

Conference_Location

Reykjavik

Print_ISBN

978-1-4244-8906-0

Electronic_ISBN

978-1-4244-8907-7

Type

conf

DOI

10.1109/WHISPERS.2010.5594864

Filename

5594864