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dc.contributor.authorStamenkovic J
dc.contributor.authorGuerriero L
dc.contributor.authorFerrazzoli P
dc.contributor.authorNotarnicola C
dc.contributor.authorGreifeneder F
dc.contributor.authorThiran J-P
dc.date.accessioned2019-03-25T11:26:11Z
dc.date.available2019-03-25T11:26:11Z
dc.date.issued2017
dc.identifier.issn0196-2892
dc.identifier.urihttp://dx.doi.org/10.1109/TGRS.2017.2687421
dc.identifier.urihttp://hdl.handle.net/10863/9120
dc.description.abstractIn this paper, we address the problem of retrieving soil moisture over a grassland alpine area from Synthetic Aperture Radar (SAR) data using a statistical algorithm trained by simulations of a physical model. A time series of C-band VV-polarized Wide Swath images acquired by Envisat Advanced SAR (ASAR) in the snow-free periods of 2010 and 2011 was simulated using a discrete radiative transfer model (RTM). The test area was located in the Mazia valley, South Tyrol (Italy), where the main land types are meadows and pastures. Soil moisture was collected from five meteorological stations, two of which situated in meadows and the rest in pastures. The smallest and the highest RMSEs of the RTM simulations were 0.78 dB and 1.91 dB, respectively. After backscattering simulation, the top soil moisture was estimated using Gaussian Process Regression (GPR). GPR was trained with the backscatter model simulations (including terrain features) for 2010, and then used to predict moisture from radar observations acquired in 2011. The relative importance of different input features was also assessed. The RMSE of the predicted soil moisture for the largest training data set (including aspect as a terrain feature) was 5.6% Vol. and the corresponding correlation coefficient was 0.84.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.relation
dc.rights
dc.titleSoil moisture estimation by SAR in Alpine fields using Gaussian Process Regressor trained by model simulationsen_US
dc.typeArticleen_US
dc.date.updated2019-03-25T10:56:45Z
dc.language.isiEN-GB
dc.journal.titleIEEE Transactions on Geoscience and Remote Sensing
dc.description.fulltextnoneen_US


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