Downscaling SMAP and SMOS soil moisture retrievals over the Goulburn River Catchment , Australia

Soil moisture is an important variable in a number of environmental processes – specifically the hydrological cycle, in the water-limited environments. Therefore, soil moisture data is important as an input variable in hydrologic, climatic modelling and agricultural applications. Many of these applications require high-resolution soil moisture data. However, most of the available soil moisture measurements are rarely available at high resolution, therefore unable to capture the spatial heterogeneity of soil moisture with required accuracy levels. Thus, upscaling or downscaling of soil moisture observations to higher spatial resolution is an essential requirement for these multidisciplinary applications. A long-term high-resolution soil moisture dataset is useful for planning and decision making in agriculture, climatology and hydrology. Developing a historic soil moisture dataset at high spatial resolution over a long period requires the use of different satellite soil moisture products. However, the use of different satellite products results in incompatibilities among each other due to discrepancies in overpass times, the wavelengths used, retrieval algorithms, orbital parameters and sensor errors. Therefore, validation and comparison of soil moisture retrievals from different satellite sensors and their downscaled products is important in evaluating the consistency of a long-term time series dataset of high-resolution soil moisture. This study focusses on a downscaling algorithm based on the thermal inertia theory at two sub-catchments of the Goulburn River in south-eastern Australia, Krui and Merriwa River catchments. The goal is to downscale the radiometric soil moisture retrievals of Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions along with validation using established in-situ observation networks. A linear regression model was developed between the daily surface temperature difference and daily mean soil moisture values from the in-situ observations of the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) project. This relationship is modulated by the vegetation cover and soil attributes. The MODerateresolution Imaging Spectroradiometer (MODIS) derived land surface temperature difference values were fitted into the lookup algorithms to estimate surface soil moisture at fine spatial resolution at 1 km. The coarseresolution SMAP (36 km) and SMOS (25 km) radiometric soil moisture products were downscaled to 1 km. The coarse-resolution SMAP and SMOS soil moisture datasets were compared with each other, and then against the SASMAS in-situ measurements. SMAP 36 km datasets show a reasonable agreement with the insitu data with RMSEs of 0.07 and 0.05 cm/cm over two SMAP pixels. However, SMOS 25 km soil moisture products show a general underestimation as compared to SMAP and SASMAS datasets. Therefore, the SMOS data were calibrated with SMAP data. Subsequently, the SMAP, SMOS and adjusted SMOS datasets over the Krui and Merriwa River catchments for the year 2015 were downscaled and compared. The results show that the accuracy of the downscaled soil moisture datasets are highly influenced by the accuracy of the coarse-resolution satellite soil moisture products. The downscaled data were compared with in-situ data of five SASMAS monitoring stations. The downscaled SMAP, SMOS and adjusted SMOS datasets respectively showed average RMSEs of 0.10 (standard deviation, σ= 0.05), 0.19 (σ= 0.07) and 0.13 (σ= 0.02) cm/cm with the SASMAS in-situ measurements. The three downscaled datasets of SMAP, SMOS and adjusted SMOS show consistent soil moisture pattern over the study catchments. The downscaled adjusted SMOS data displayed a better agreement with downscaled SMAP soil moisture data compared to the nonadjusted SMOS data.