Synergistic use of SMOS and Sentinel-3 for retrieving spatiotemporally estimates of surface soil moisture and evaporative fraction

Earth Observation (EO) makes it possible to obtain information on key parameters characterizing interactions among Earth’s system components, such as evaporative fraction (EF) and surface soil moisture (SSM). Notably, techniques utilizing EO data of land surface temperature (Ts) and vegetation index (VI) have shown promise in this regard. The present study presents an implementation of a downscaling method that combined the soil moisture product from SMOS and the Fractional Vegetation Cover provided by Sentinel 3 ESA platform.

The applicability of the investigated technique is demonstrated for a period of two years (2017-2018) using in-situ data acquired from five CarboEurope sites and from all the sites available in the REMEDHUS soil moisture monitoring network, representing a variety of climatic, topographic and environmental conditions. Predicted parameters were compared against co-orbital ground measurements acquired from several European sites belonging to the CarboEurope ground observational network.

Results indicated a close agreement between all the inverted parameters and the corresponding in-situ data. SSM maps predicted from the “triangle”  SSM showed a small bias, but a large scatter. The results of this study provide strong supportive evidence of the potential value of the investigated herein methodology in accurately deriving estimates of key parameters characterising land surface interactions that can meet the needs of fine-scale hydrological applications. Moreover, the applicability of the presented approach demonstrates the added value of the synergy between ESA’s operational products acquired from different satellite sensors, namely in this case SMOS & Sentienl-3. As it is not tight to any particular sensor can also be implemented with technologically advanced EO sensors launched recently or planned to be launched.

In the present work Dr Petropoulos participation has received funding from the European Union’s Horizon 2020 research and innovation programme ENViSIoN under the Marie Skłodowska-Curie grant agreement No 752094.