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\begin{document}

\title{Estimating Soil Moisture at High Spatial Resolution with Three
Radiometric Satellite Products: A Study from a South-Eastern Australian
Catchment}



\author[1]{I.P. Senanayake}%
\author[2]{In-Young Yeo}%
\author[2]{Natthachet Tangdamrongsub}%
\author[2]{Garry R. Willgoose}%
\author[2]{Greg R. Hancock}%
\author[2]{Tony Wells}%
\author[3]{Bin Fang}%
\author[3]{Venkat Lakshmi}%
\affil[1]{Affiliation not available}%
\affil[2]{University of Newcastle}%
\affil[3]{University of South Carolina}%


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  \date{May 17, 2024}


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\begin{abstract}
Long-term soil moisture datasets at high spatial resolution are
important in agricultural, hydrological, and climatic applications. The
soil moisture estimates can be achieved using satellite remote sensing
observations. However, the satellite soil moisture data are typically
available at coarse spatial resolutions (\textasciitilde{} several tens
of km), therefore require further downscaling. Different satellite soil
moisture products have to be conjointly employed in developing a
consistent time-series of high resolution soil moisture, while the
discrepancies amongst different satellite retrievals need to be
resolved. This study aims to downscale three different satellite soil
moisture products, the Soil Moisture and Ocean Salinity (SMOS, 25 km),
the Soil Moisture Active Passive (SMAP, 36 km) and the SMAP-Enhanced (9
km), and to conduct an inter-comparison of the downscaled results. The
downscaling approach is developed based on the relationship between the
diurnal temperature difference and the daily mean soil moisture content.
The approach is applied to two sub-catchments (Krui and Merriwa River)
of the Goulburn River catchment in the Upper Hunter region (NSW,
Australia) to estimate soil moisture at 1 km resolution for 2015. The
three coarse spatial resolution soil moisture products and their
downscaled results will be validated with the in-situ observations
obtained from the Scaling and Assimilation of Soil Moisture and
Streamflow (SASMAS) network. The spatial and temporal patterns of the
downscaled results will also be analysed. This study will provide the
necessary insights for data selection and bias corrections to maintain
the consistency of a long-term high resolution soil moisture dataset.
The results will assist in developing a time-series of high resolution
soil moisture data over the south-eastern Australia.%
\end{abstract}%



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