Speaker
Description
Climate change and frequent episodes of severe drought urge to monitor soil moisture changes at global scale. To answer this demand the European Space Agency (ESA) and the National Aeronautics and Space Administration (NASA) launched two satellite missions: Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP); consisting of radiometers acquiring data in L-band (1.2-1.4 GHz). Spatial resolution of these passive radiometers (SMAP active radar failed on 07.07.2015) is around 35–50 km which is suitable for global soil moisture studies but not for local hydrological modeling, regional climate models (RCMs), precision farming, land slide monitoring, etc. In this respect Synthetic Aperture Radars (SARs), featuring several meters resolution of a pixel, match the requirements of regional scale applications. This opportunity has been explored by the ESA within projects devoted to soil moisture retrieval from Sentinel-1 SAR C-band (5.4 GHz) imagery (projects: “Biebrza Sentinel-1 Supersite”: ESA EXPRO no. 4000112578/14/NL/MP), and “Exploit-S-1”: ESA/AO/1-8306/15/I-NB).
This study present one of the soil moisture retrieval algorithms developed within the aforementioned projects that utilize NASA-USDA SMAP Global Soil Moisture product to derive linear relationship between soil moisture and Sentinel-1 sigma nought VV backscatter coefficient, separately for every SMAP pixel, for every month (to ensure quasi-stable surface roughness conditions), and for every relative satellite track (to ensure constant incidence angle). This is performed exclusively for pixels marked as agriculture by the CORINE Land Cover classification featuring low (< -15 dB) VH backscatter to eliminate volume scattering occurring in dense canopy. Further, the derived linear regression coefficients are interpolated by means of the ordinary kriging method to match spatial resolution of the final product. Then, Sentinel-1 imagery, screened for non-agriculture and volume scattering pixels, is upscaled to 500-1000 m to reduce the speckle noise. Ultimately, computed sigma nought backscatter at VV polarization is convolved with the regression coefficients. Derived Sentinel-1 soil moisture product together with the SMAP product are validated against in-situ soil moisture measurements distributed by the International Soil Moisture Network (ISMN). Estimated accuracy (see Figure) of the products across TERENO soil moisture network (Germany) reveals moderate agreement with ground data (R2=0.58 for SMAP product and R2=0.47 for Sentinel-1 product).
Numerical implementation of the described method has been performed within the Google Earth engine platform, that merges extensive satellite data archive with sophisticated programming environment. This allows for dissemination of the presented algorithm, embedded in an intuitive graphical user interface (GUI), among wide end-user community.
