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CPS1887 Sahidan A. et al.
4. Discussion and Conclusion
There are many missing values of the data used. One of the main cause of
missing data from the satellite is cloud. In order to solve the missing issue, we
indicated the missing value as zero. Even though, the missing data problems
and the fluctuation of LST data, another issue of using cubic spline function to
extract the annual LST seasonality was the optimal number of the knots. This
study, we applied cubic spline model to extract the seasonality and trend in
MODIS LST time series data in Taiwan. The results show that by using an
optimal number of knots and their placement were a high potential for using
a cubic spline function with annual periodic boundary conditions. The study
model, we fit spline which consists of eight knots. The results illustrate
smoothest trend and seasonality of the data used.
The result from the day LST seasonal patterns for super-region, we can
notice that the seasonal patterns for different sub-regions in the same region
were quite similar pattern for every year. Even there were many missing data
in the data sets, the cubic spline function satisfactorily extracted the seasonal
pattern and trend. The result from the trend within the same region graph in
northern and central part of Taiwan show stable trend in some super regions
but, there is an accelerate increase in both part. Moreover, the trend look
accelerate decrease in some northern part of Taiwan. In addition, cubic spline
able to be used in every LST data in any coverage area and time. Moreover, it
is one of an alternative method that could be considered to describe the
seasonality by using satellite and climate data.

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