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TitleA model for downscaling SMOS soil moisture using Sentinel-1 SAR data
AuthorLi, J; Wang, S; Gunn, G; Joosse, P; Russell, H A J
SourceInternational Journal of Applied Earth Observation and Geoinformation vol. 72, 2018 p. 109-121,
Alt SeriesNatural Resources Canada, Contribution Series 20190009
PublisherElsevier BV
Mediapaper; on-line; digital
File formatpdf
ProgramGroundwater Geoscience, Aquifer Assessment & support to mapping
Released2018 07 20
AbstractA model for downscaling SMOS (Soil Moisture Ocean Salinity) soil moisture products is developed by using multi-temporal dual-polarized (HH+HV) C-band SAR data. In this model, the effect of vegetation on soil moisture retrieval from SAR data is minimized by using the water-cloud model (WCM), in which vegetation contribution is quantified using the backscatter coefficient of HV polarization. The wavelet transform is used to fuse high resolution Sentinel-1A SAR backscatter with low resolution SMOS soil moisture, where the difference in spatial heterogeneity between scales is also accounted for. The influence of soil surface roughness is eliminated by using multi-temporal data. The multi-temporal SMOS soil moisture and dual-pol Sentinel-1/SAR data are the only inputs of this downscaling model. The model is tested in southern Ontario, Canada to downscale 40?km resolution SMOS soil moisture to 1.25?km and 2.5?km resolutions. The downscaled results show good agreements with the in-situ soil moisture collected in May and July of 2016 with an unbiased root-mean-square-error (RMSE) of 0.045?m3/m3 and 0.047?m3/m3 and a coefficient of determination (R2) of 0.54 and 0.70 at 1.25?km and 2.5?km resolutions respectively. The results suggest that the model can be applied for C-band at regional scales to provide continuous soil moisture mapping at higher resolutions. The high revisit frequency of the up-coming Radarsat Constellation Mission (RCM) combined with its large areal coverage characteristics are ideal for the generation of downscaled products.
Summary(Plain Language Summary, not published)
Current soil moisture products derived from passive radar data of SMOS/ SMAP satellites have coarse resolutions (~40km), which limits them in the applications (e.g. localized drought monitoring and hydro-climatological application), where 1-10 km resolution is needed. Synthetic Aperture Radar (SAR) is sensitive to soil moisture¿s change at high resolution. Therefore, the combination of SMOS/SMAP soil moisture with SAR data could offer a way to downscale SMOS/SMAP soil moisture products to the higher resolution. This study developed a model for increasing resolution of SMOS soil moisture by using high resolution Sentinel-1 SAR data. The downscaled results show good agreements with the in-situ soil moisture. The model can be extended to other passive microwave (e.g. SMAP) soil moisture products and other SAR sensors such as Canada¿s Radarsat-2 and Radarsat Constellation Mission (RCM) for continuous soil moisture mapping over large area.