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\begin{document}

\title{Machine Learning Techniques for Regional Scale Estimation of High-
Resolution Cloud-Free Daily Sea Surface Temperatures from MODIS Data}



\author[1]{RAAJ Ramsankaran}%
\author[1]{Swathy Sunder}%
\author[1]{Balaji Ramakrishnan}%
\affil[1]{Indian Institute of Technology Bombay}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
High-resolution sea surface temperature (SST) estimates are dependent on
satellite-based infrared radiometers, which are proven to be highly
accurate in the past decades. However, the presence of clouds is a big
stumbling block when physical approaches are used to derive SST. This
problem is more prominent across tropical regions such as Arabian
Sea(AS) and Bay of Bengal(BoB), restricting the availability of
high-resolution SST data for ocean applications. The previous studies
for developing daily high-resolution cloud-free SST products mainly
focus on fusion of multiple satellites and in-situ data products that
are computationally expensive and often time consuming. At the same
time, it was observed that the capabilities of data-driven approaches
are not yet fully explored in the estimation of cloud-free
high-resolution SST data. Hence, in this study an attempt has been made
for the first time to estimate daily cloud free SST from a single sensor
(MODIS Aqua) dataset using advanced machine learning techniques. Here,
three distinct machine learning techniques such as Artificial Neural
Networks (ANN), Support Vector Regression (SVR) and Random Forest
(RF)-based algorithms were developed and evaluated over two different
study areas within the AS and BoB using 10 years of MODIS data and
in-situ reference data. Among the developed algorithms, the SVR-based
algorithm performs consistently better. In AS region, while testing, the
SVR-based SST estimates was able to achieve an adjusted coefficient of
determination (R\_adj\^{}2) of 0.82 and root mean square error (RMSE) of
0.71\selectlanguage{ngerman}°C with respect to the in situ data. Similarly, in BoB too, the SVR
algorithm outperforms the other algorithms with R\_adj\^{}2 of 0.78 with
RMSE of 0.88ºC. Further, a spatio-temporal and visual analysis of the
results as well as an inter-comparision with NOAA AVHRR daily optimally
interpolated global SST (a standard SST product available in practice)
the suggest that the proposed SVR-based algorithm has huge potential to
produce operational high-resolution cloud-free SST estimates, even if
there is cloud cover in the image.%
\end{abstract}\selectlanguage{ngerman}%



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\textbf{Hosted file}

\verb`essoar.10502704.1.docx`  available at \url{https://authorea.com/users/533899/articles/598559-machine-learning-techniques-for-regional-scale-estimation-of-high-resolution-cloud-free-daily-sea-surface-temperatures-from-modis-data}

\selectlanguage{english}
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\end{document}

