Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale

Abstract. Soil moisture estimates at high spatial and temporal resolution are of great value for optimizing water and agricultural management. To fill the gap between local ground observations and coarse spatial resolution remote sensing products we use SMAP and Sentinel-1 data together with a unique dataset of ground based soil moisture estimates by cosmic ray neutron sensors (CRNS) and capacitance probes to test the possibility of downscaling soil moisture to the sub-kilometre resolution. For a study area with diverse land use in Denmark we first show that SMAP soil moisture and Sentinel-1 backscatter time series correlate well with in situ observations of CRNS. Sentinel-1 backscatter in VV and VH polarization both show a strong correlation with CRNS soil moisture at higher spatial resolutions (20 m–400 m) and exhibit distinct and meaningful signals at different land cover types. At a first glance satisfying statistical correlations with CRNS soil moisture time series and capacitance probes are obtained using the SMAP Sentinel-1 downscaling algorithm. However, the spatial distribution of soil moisture pattern is inconclusive. Accounting for different land use in the downscaling algorithm improved the spatial distribution slightly. However, the investigated downscaling algorithm cannot fully account for the vegetation dependency at sub-kilometre resolution. The study implies that further research is needed in the modification of the downscaling algorithm to produce representative soil moisture pattern at fine scale, however backscatter signals appear informative.