A model for downscaling SMOS soil moisture using Sentinel-1 SAR data


Li, J., Wang, S., Gunn, G., Joosse, P., Russell, H.A.J. (2018). A model for downscaling SMOS soil moisture using Sentinel-1 SAR data. International Journal of Applied Earth Observation and Geoinformation, [online] 72 109-121. http://dx.doi.org/10.1016/j.jag.2018.07.012

Plain language summary

Continuous observations of soil moisture over large areas are important in many earth science applications. Current soil moisture estimates derived from the passive radar Soil Moisture Ocean Salinity (SMOS) satellite provide global coverage over a 2-3 day period but have coarse resolution (~40km) which limits their use for applications such as localized drought monitoring and watershed modelling where 1-10 km resolution estimates are needed. Active radar such as Synthetic Aperture Radar (SAR) can observe the earth’s surface at high resolution and is sensitive to soil moisture changes. Therefore, the combination of SMOS soil moisture with SAR data could offer a way to increase the resolution of global soil moisture estimates. This study developed a model for increasing (or downscaling) the resolution of SMOS soil moisture data by using high resolution SAR data. The model was tested in the Southern Ontario region of Canada to downscale 40 km resolution SMOS soil moisture to 1.25 and 2.5 km resolution using 50 m resolution Sentinel-1 SAR images. The downscaled results show good agreement with soil moisture measurements made in the field. The model is not limited to the SMOS soil moisture and Sentinel-1 SAR data. It can be extended to other passive microwave (e.g. Soil Moisture Active Passive (SMAP)) soil moisture products and other SAR sensors such as Canada’s Radarsat-2 and Radarsat Constellation Mission (RCM). The RCM data will become available in 2018 and will provide a short revisit time (<4 days). Therefore, this model provides potential to fuse RCM data with SMOS/SMAP soil moisture to operationally map soil moisture at higher resolutions over large areas for use in more local applications.


A 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.

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