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\begin{document}

\title{Correction of SMAP (Soil Moisture Active Passive) Satellite Retrieved
Soil Moisture Data using Machine Learning Techniques over North West
Region of Bangladesh}



\author[1]{Hamidul Haque}%
\author[2]{Nasreen Jahan}%
\affil[1]{Bangladesh Univsersity of Engineering and Technology}%
\affil[2]{Bangladesh University of Engineering and Technology}%


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  \date{June 07, 2024}


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\begin{abstract}
Bangladesh, part of Indo-Gangetic-Brahmaputra Plain, is frequently
affected by floods and droughts. As the economy of Bangladesh is still
agriculture based, effective measurement of soil moisture will not only
strengthen the irrigation management but also improve the hydrological
modelling and drought prediction. But only nine agro-meteorological
stations of Bangladesh measure the soil moisture four times a month
which creates a vacuum to scientifically manage her water resources.
SMAP (Soil Moisture Active Passive) satellite of NASA provides an
unprecedented opportunity for full scale measurement of soil moisture
over this region. Field measurements of soil moisture from April 2015
were used to assess the effectiveness of the SMAP's measurement over the
North West Region of Bangladesh which suffers from frequent dry spells.
Initially the Root mean squared error (RMSE) between the SMAP and
observed soil moisture were found to vary between 12.28 to 16.72\% for
the available stations. The results showed a bias in SMAP data and it
was significantly reduced using bias correction. Later multiple linear
regression, based on supplementary climate data in addition to SMAP
observations, was applied to obtain an improved estimate of soil
moisture and the RMSE were reduced to 1.19 to 3.18\%. Lastly, different
machine learning techniques (i.e. ANN, SVR, XGBoost etc.) were used to
reduce the bias further. This study demonstrates a promising potential
of using the SMAP data in soil moisture estimation over Bangladesh for
its effective water resources management.%
\end{abstract}%



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