Research Papers

Near-surface soil moisture estimation by combining airborne L-band brightness temperature observations and imaging hyperspectral data at the field scale

[+] Author Affiliations
Marion Pause

University of Tuebingen, WESS—Water & Earth System Science Competence Centre, Hölderlinstraße 12, 72074 Tübingen, Germany

Helmholtz-Centre for Environmental Research—UFZ, Department Monitoring and Exploration Technologies, Permoserstr. 15, 04318 Leipzig, Germany

Karsten Schulz

Ludwig-Maximilians-Universität München, Department of Geography, Luisenstr. 37, 80333 München, Germany

Steffen Zacharias

Helmholtz-Centre for Environmental Research—UFZ, Department Monitoring and Exploration Technologies, Permoserstr. 15, 04318 Leipzig, Germany

Angela Lausch

Helmholtz-Centre for Environmental Research—UFZ, Department Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, Germany

J. Appl. Remote Sens. 6(1), 063516 (Apr 27, 2012). doi:10.1117/1.JRS.6.063516
History: Received October 20, 2011; Revised January 27, 2012; Accepted February 27, 2012
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Abstract.  The observation of spatially distributed soil moisture fields is an essential component for a large range of hydrological, climate, and agricultural applications. While direct measurements are expensive and limited to small spatial domains, the inversion of airborne and satellite L-band radiometer data has shown the potential to provide spatial estimates of near surface soil moisture from the local up to the global scale. When using L-band radiometer observations for soil moisture retrieval, a major limitation is the attenuation of the microwave signal by the vegetation, hampering the signal inversion and thereby making spatially distributed plant information necessary. Usually vegetation types are considered with a vegetation type specific global parameterization, e.g., for leaf area index (LAI). Within this study we evaluate and address the effect of spatially varying LAI on high spatial resolution (pixel size 50 m) airborne L-band brightness temperature of crop canopies that are usually regarded homogeneous. To account for within field variations of LAI we used airborne imaging spectrometer data (pixel size 1.5 m) to empirically create maps of LAI using spectral greenness vegetation indices. We found clear (R2<0.90) functional relationships between spatially varying L-band brightness temperature and LAI variations within crop canopies that in literature are usually assumed homogeneous. Very good (R2=0.93) near surface soil moisture estimates were achieved using multi-variate regression and adding plant specific spectral information to the independent variable set for final soil moisture retrieval. The study shows that a multi-sensor campaign using airborne L-band radiometer and imaging spectrometers provide a powerful data set for monitoring patterns of near surface soil moisture and vegetation canopy at the field scale with high accuracy.

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© 2012 Society of Photo-Optical Instrumentation Engineers

Topics

L band

Citation

Marion Pause ; Karsten Schulz ; Steffen Zacharias and Angela Lausch
"Near-surface soil moisture estimation by combining airborne L-band brightness temperature observations and imaging hyperspectral data at the field scale", J. Appl. Remote Sens. 6(1), 063516 (Apr 27, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063516


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