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- aggregation classification "A1".
- aggregation creator B438824.
- aggregation creator B438825.
- aggregation creator B438826.
- aggregation creator person.
- aggregation date "2009".
- aggregation format "application/pdf".
- aggregation hasFormat 626569.bibtex.
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- aggregation hasFormat 626569.dc.
- aggregation hasFormat 626569.didl.
- aggregation hasFormat 626569.doc.
- aggregation hasFormat 626569.json.
- aggregation hasFormat 626569.mets.
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- aggregation hasFormat 626569.txt.
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- aggregation hasFormat 626569.yaml.
- aggregation isPartOf urn:issn:1027-5606.
- aggregation language "eng".
- aggregation rights "I have retained and own the full copyright for this publication".
- aggregation subject "Earth and Environmental Sciences".
- aggregation title "Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling".
- aggregation abstract "he objective of the study is to investigate the potential of retrieving superficial soil moisture content (m(v)) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e. g. from 100 to 10 000 km(2)). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution ( e. g. 1530 km2) by point scale hydrologic models ( or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e. g. 0.01 km(2)). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.".
- aggregation authorList BK765588.
- aggregation endPage "356".
- aggregation issue "3".
- aggregation startPage "343".
- aggregation volume "13".
- aggregation aggregates 666387.
- aggregation isDescribedBy 626569.
- aggregation similarTo LU-626569.