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Institute
Remote sensing time series is the collection or acquisition of remote sensing data in a
fixed equally spaced time period over a particular area or for the whole world. Near
daily high spatial resolution data is very much needed for remote sensing applications
such as agriculture monitoring, phenology change detection, environmental
monitoring and so on. Remote sensing applications can produce better and accurate
results if they are provided with dense and accurate time series of data. The current
remote sensing satellite architecture is still not capable of providing near daily
or daily high spatial resolution images to fulfill the needs of the above mentioned
remote sensing applications. Limitations in sensors, high development, operational
costs of satellites and presence of clouds blocking the area of observation are some
of the reasons that makes near daily or daily high spatial resolution optical remote
sensing data highly challenging to achieve. With developments in the optical sensor
systems and well planned remote sensing satellite constellations, this condition
can be improved but it comes at a cost. Even then the issue will not be completely
resolved and thus the growing need for high temporal and high spatial resolution
data cannot be fulfilled entirely. Because the data collection process relies on satellites
which are physical system, these can fail unpredictably due to various reasons
and cause a complete loss of observation for a given period of time making a gap
in the time series. Moreover, to observe the long term trend in phenology change
due to rapidly changing environmental conditions, the remote sensing data from
the present is not just sufficient, the data from the past is also important. A better
alternative solution for this issue can be the generation of remote sensing time series
by fusing data from multiple remote sensing satellite which has different spatial and
temporal resolutions. This approach will be effective and efficient. In this method
a high temporal low spatial resolution image from a satellite such as Sentinel-2 can
be fused with a low temporal and high spatial resolution image from a satellite such
as the Sentinel-3 to generate a synthetic high temporal high spatial resolution data.
Remote sensing time series generation by data fusion methods can be applied to
the satellite images captured currently as well as the images captured by the satellites
in the past. This will provide the much needed high temporal and high spatial
resolution images for remote sensing applications. This approach with its simplistic
nature is cost effective and provides the researchers the means to generate the
data needed for their application on their own from the limited source of data available
to them. An efficient data fusion approach in combination with a well planned
satellite constellation can offer a solution which will ensure near daily time series of
remote sensing data with out any gap. The aim of this research work is to develop
an efficient data fusion approaches to achieve dense remote sensing time series.