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dc.contributor.authorPasolli L
dc.contributor.authorNotarnicola C
dc.contributor.authorBertoldi G
dc.contributor.authorBruzzone L
dc.contributor.authorRemelgado R
dc.contributor.authorGreifeneder F
dc.contributor.authorNiedrist G
dc.contributor.authorDella Chiesa S
dc.contributor.authorTappeiner U
dc.contributor.authorZebisch M
dc.date.accessioned2018-11-05T10:23:24Z
dc.date.available2018-11-05T10:23:24Z
dc.date.issued2015
dc.identifier.issn1939-1404
dc.identifier.urihttp://dx.doi.org/10.1109/JSTARS.2014.2378795
dc.identifier.urihttp://ieeexplore.ieee.org/document/7005430/?reload=true
dc.identifier.urihttp://hdl.handle.net/10863/6973
dc.description.abstractThis paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a focus on mountain areas. The novelties of the paper are: the extension of an already developed method to coarse resolution data (150 m) in mountain environment with high land heterogeneity, with only VV polarization and the proper selection of input features. During the result analysis, several algorithm characteristics were clearly identified: 1) the performances showed to be strongly related to input features such as topography and vegetation indices; 2) the algorithm needs a training phase; 3) the averaging window needs to be proper selected to take into account both the speckle noise and the characteristics of the area under investigation; and 4) the algorithm, being data driven, can be considered as site dependent. The experimental analysis is carried out on images acquired over the Südtirol/Alto Adige Province in Italy during 2010-2011 from the RADARSAT2 and Envisat ASAR in Wide Swath mode. SMC maps were compared with spatially distributed ground measurements, resulting in a root mean squared error (RMSE) value ranging from 0.045 to 0.07 m3/m3. Concerning the multiscale analysis, the results indicated that RADARSAT2 maps are able to detect the spatial heterogeneity and soil moisture dynamics at local scale, while ASAR WS SMC maps are able to identify mainly the two main classes of pasture and meadows. When these estimates are compared with SMC values from meteorological stations a RMSE value of 0.10 m3/m3 for both satellites indicated a reduced capability to follow the temporal dynamics.en_US
dc.language.isoenen_US
dc.rights
dc.subjecthydrological techniquesen_US
dc.subjectMoisture
dc.subjectMoisture measurement
dc.subjectRegression analysis
dc.subjectRemote sensing by radar
dc.subjectSoil
dc.subjectSupport vector machines
dc.subjectSynthetic aperture radar
dc.subjectVegetation mapping
dc.subject
dc.titleEstimation of soil moisture in mountain areas using SVR technique applied to multiscale active radar images at C banden_US
dc.typeArticleen_US
dc.date.updated2018-11-05T09:29:02Z
dc.language.isiEN-GB
dc.journal.titleIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.description.fulltextnoneen_US


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