Soil moisture estimation under vegetation from PALSAR FBD data by means of polarimetric decomposition techniques

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Title:Main Title: Soil moisture estimation under vegetation from PALSAR FBD data by means of polarimetric decomposition techniques
Descriptions:Abstract: The disturbing effects caused by vegetation and surface roughness are major impediments to accurate quantitative retrievals of soil moisture from Synthetic Aperture Radar (SAR). With most of the operational spaceborne systems it is not possible to separate the different scattering contributions of the soil and vegetation components. In this study we use the coherent-on-receive dual-pol standard acquisitions (FBD343) of Phased Array type L-band Synthetic Aperture Radar (PALSAR) aboard the Advanced Land Observing Satellite (ALOS 'Daichi') acquired over an arable land test site in Western Germany. By applying a PolSAR decomposition technique, namely the H/A/Alpha decomposition, we exploit the phase information to increase the amount of observables. The potential to derive information on biomass and surface roughness from the dual-pol data is investigated based on correlation analyses between PALSAR observables and in-situ measurements. High sensitivities towards surface roughness and crop biomass could be ascertained. Using these findings, we estimate surface roughness ks and sugar beet total wet weight with RMS errors of 0.11 and 2.66 kg/m?, respectively. The good quality of the estimates allows correcting the backscattering coefficients for the surface roughness and vegetation effects. The accuracy of soil moisture retrievals could be increased from 4.5 to 3.6 Vol.-% using the roughness correction for bare soil and from > 10.0 to 3.6 Vol.-% using the biomass correction for sugar beet. The results give a promising outlook in terms of the possibility to develop an operational soil moisture retrieval model for PALSAR data collected in the Fine Beam Dual Polarization (FBD) mode.
Series Information: Proceedings on the Workshop of Remote Sensing Methods for Change Detection and Process Modelling, 18-19 November 2010, University of Cologne, Germany, Kölner Geographische Arbeiten, 92, pp. 63-70
Identifier:10.5880/TR32DB.KGA92.9 (DOI)
Related Resource:Is Part Of 0454-1294 (ISBN)
Citation Advice:Koyama, C. et al., 2011. Soil moisture estimation under vegetation from PALSAR FBD data by means of polarimetric decomposition techniques. In: Lenz-Wiedemann, V., Bareth, G. (Eds.), Proceedings on the Workshop of Remote Sensing Methods for Change Detection and Process Modelling. Geographisches Institut der Universität zu Köln (Kölner Geographische Arbeiten, 92), Cologne, Germany, 63-70. doi: 10.5880/TR32DB.KGA92.9
Responsible Party
Creators:Christian N. Koyama (Author), Peter Fiener (Author), Karl Schneider (Author)
Contributors:Victoria Lenz-Wiedemann (Editor), Georg Bareth (Editor), Transregional Collaborative Research Centre 32 (Meteorological Institute, University of Bonn) (Data Manager), University of Cologne (Regional Computing Centre (RRZK)) (Hosting Institution)
Publisher:Geographisches Institut der Universität zu Köln - Kölner Geographische Arbeiten
Publication Year:2011
Topic
TR32 Topic:Other
Related Subproject:C3
Subjects:Keywords: ALOS, PALSAR, Polarimetry, Soil Moisture, Vegetation, Surface Roughness
File Details
Filename:Koyama_et_al_2011_KGA92.pdf
Data Type:Text - Book Section
Sizes:1686 Kilobytes
8 Pages
File Size:1.6 MB
Dates:Created: 18.11.2010
Issued: 05.10.2010
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
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Specific Information - Publication
Publication Status:Published
Review Status:Not peer reviewed
Publication Type:Book Section
Book Title:Proceedings on the Workshop of Remote Sensing Methods for Change Detection and Process Modelling
Editors:Victoria Lenz-Wiedemann, Georg Bareth
Series Title:Kölner Geographische Arbeiten
City:Cologne, Germany
Volume:92
Number of Pages:8 (63 - 70)
Metadata Details
Metadata Creator:Constanze Curdt
Metadata Created:05.08.2013
Metadata Last Updated:11.05.2021
Subproject:C3
Funding Phase:2
Metadata Language:English
Metadata Version:V50
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