Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/9791
AuthorsPierdicca, N.* 
Pulvirenti, L.* 
Bignami, C.* 
Ticconi, F.* 
TitleMonitoring Soil Moisture in an Agricultural Test Site Using SAR Data: Design and Test of a Pre-Operational Procedure
Issue Date2013
Series/Report no.3/6 (2013)
DOI10.1109/JSTARS.2012.2237162
URIhttp://hdl.handle.net/2122/9791
KeywordsSAR
soil moisture
Subject Classification03. Hydrosphere::03.02. Hydrology::03.02.04. Measurements and monitoring 
03. Hydrosphere::03.02. Hydrology::03.02.07. Instruments and techniques 
AbstractAn algorithm for pre-operational high resolution soil moisture mapping using Synthetic Aperture Radar (SAR) data is presented. It has been conceived to be inserted in the operational weather alert system of the Italian Department of Civil Protection. The Maximum A Posteriori (MAP) probability criterion is applied to retrieve soil moisture by inverting a forward backscattering model, and ancillary data such as optical images and land cover maps are also used to identify areas in which the retrieval can be carried out. The well-established semiempirical water cloud model is adopted to correct for the effect of vegetation on SAR data. In anticipation of the use of the algorithm in an operational system, in which the SAR-derived high resolution soil moisture product can be assimilated within weather prediction models or hydrological ones, an uncertainty index is associated to each estimate. The algorithm has been tested on a dataset consisting of ground data gathered for seven years (2003–2010) on an agricultural test site in Northern Italy and radar data provided by the C-band ENVISAT/ ASAR instrument. A comparison, performed at field scale, between estimated and in situ soil moisture data has shown that, by discarding the estimates with the largest uncertainty, the correlation coefficient can exceed 0.80 and the root mean square estimation error is less than 0.05 m /m . Moreover, the uncertainty index has turned out to be fairly correlated to the actual estimation error.
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