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- W3100660276 endingPage "100066" @default.
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- W3100660276 abstract "• Sources of SMC retrieval uncertainty are investigated using a Bayesian framework. • The main uncertainty originates from the SMC references and Sentinel-1. • The contribution of the surface roughness to the uncertainty is relatively small. • Promising results are obtained for operational retrieval of SMC over meadows. Soil moisture content (SMC) retrievals from synthetic aperture radar (SAR) observations do not exactly match with in situ references due to imperfect retrieval algorithms, and uncertainties in the model parameters, SAR observations and in situ references. Information on the uncertainty of SMC retrievals would contribute to their applicability. This paper presents a methodology for deriving the SMC retrieval uncertainty and decomposing this in its constituents. A Bayesian calibration framework was used for deriving the total uncertainty and the model parameter uncertainty. The methodology was demonstrated with the integral equation method (IEM) surface scattering model, which was employed for reproducing Sentinel-1 backscatter ( σ 0 ) observations and the retrieval of SMC over four sparsely vegetated fields in the Netherlands. For two meadows the calibrated surface roughness parameter distributions are remarkably similar between the ascending and the descending Sentinel-1 orbits as well as between the two meadows, and yield consistent SMC retrievals for the calibration and validation periods ( RMSD s of 0.076 m 3 m −3 to 0.11 m 3 m −3 ). These results are promising for operational retrieval of SMC over meadows. In contrast, the surface roughness parameter distributions of two fallow maize fields differ significantly and the surface roughness conditions changing over time result in less consistent SMC retrievals (calibration RMSD s of 0.096 m 3 m −3 and 0.13 m 3 m −3 versus validation RMSD s of 0.26 m 3 m −3 ). The SMC retrieval uncertainty derived with the Bayesian calibration successfully reproduces the uncertainty estimated empirically using in situ references. The main uncertainty originates from the in situ references and the Sentinel-1 observations, whereas the contribution from the surface roughness parameters is relatively small. The presented research yields further insights into the surface roughness of agricultural fields and SMC retrieval uncertainties, and these insights can be used to guide SAR-based SMC product developments." @default.
- W3100660276 created "2020-11-23" @default.
- W3100660276 creator A5010361110 @default.
- W3100660276 creator A5037183827 @default.
- W3100660276 creator A5070844465 @default.
- W3100660276 date "2020-12-01" @default.
- W3100660276 modified "2023-09-29" @default.
- W3100660276 title "Sentinel-1 soil moisture content and its uncertainty over sparsely vegetated fields" @default.
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- W3100660276 doi "https://doi.org/10.1016/j.hydroa.2020.100066" @default.
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