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Title
Seven years of advanced synthetic aperture radar (ASAR) global monitoring (GM) of surface soil moisture over Africa
AuthorDostálová, Alena ; Doubková, Marcela ; Sabel, Daniel ; Bauer-Marschallinger, Bernhard ; Wagner, Wolfgang In der Gemeinsamen Normdatei der DNB nachschlagen
Published in
Remote sensing, 2014, Vol. 6, Issue 8, page 7683-7707
Published2014
Edition
Published version
LanguageEnglish
Document typeJournal Article
Keywords (EN)soil moisture / SAR / Envisat ASAR / change detection / Africa
URNurn:nbn:at:at-ubtuw:3-2254 Persistent Identifier (URN)
DOI10.3390/rs6087683 
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 The work is publicly available
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Seven years of advanced synthetic aperture radar (ASAR) global monitoring (GM) of surface soil moisture over Africa [10.99 mb]
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Abstract (English)

A surface soil moisture (SSM) product at a 1-km spatial resolution derived from the Envisat Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode data was evaluated over the entire African continent using coarse spatial resolution SSM acquisitions from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Noah land surface model from the Global Land Data Assimilation System (GLDAS-NOAH). The evaluation was performed in terms of relative soil moisture values (%), as well as anomalies from the seasonal cycle. Considering the high radiometric noise of the ASAR GM data, the SSM product exhibits a good ability (Pearson correlation coefficient (R) = 0.6 for relative soil moisture values and root mean square difference (RMSD) = 11% when averaged to 5-km resolution) to monitor temporal soil moisture variability in regions with low to medium density vegetation and yearly rainfall >250 mm. The findings agree with previous evaluation studies performed over Australia and further strengthen the understanding of the quality of the ASAR GM SSM product and its potential for data assimilation. Problems identified in the ASAR GM algorithm over arid regions were explained by azimuthal effects. Diverse backscatter behavior over different soil types was identified. The insights gained about the quality of the data were used to establish a reliable masking of the existing ASAR GM SSM product and the identification of areas where further research is needed for the future Sentinel-1-derived SSM products.

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