Preprints
https://doi.org/10.5194/hess-2021-508
https://doi.org/10.5194/hess-2021-508

  27 Oct 2021

27 Oct 2021

Review status: this preprint is currently under review for the journal HESS.

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

Rena Meyer1,a, Wenmin Zhang1, Søren Julsgaard Kragh1,2, Mie Andreasen2, Karsten Høgh Jensen1, Rasmus Fensholt1, Simon Stisen2, and Majken C. Looms1 Rena Meyer et al.
  • 1Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
  • 2Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
  • anow at: Hydrogeology and Landscape Hydrology Group, Institute for Biology and Environmental Sciences, Carl von Ossietzky University of Oldenburg, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany

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.

Rena Meyer et al.

Status: open (until 22 Dec 2021)

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Rena Meyer et al.

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Short summary
The amount and spatio-temporal distribution of soil moisture, the water in the upper soil, is of high relevance for agriculture and water management. In this study, we investigate whether the established downscaling algorithm combining different satellite products to achieve medium scale soil moisture is applicable to higher resolution and if results can be improved by accounting for land cover types. Original satellite data and downscaled soil moisture are compared to ground observations.