The environment of Arctic is very important for the global environment and human society because it is sensitive as sea
ice changes and keeps the Earth’s cool or warm climate. So we need continuous monitoring of Arctic sea ice to
understand and predict the process of climate changes. Satellite remote sensing is a useful tool for monitoring sea ice.
Thus, this study analyzed the time-series of Arctic sea ice changes using satellite remote sensing data with a time-series
statistical method for last ten years from 2003 and predicted the sea ice extent in the near future. Especially, we used the
Multivariate SARIMA(Seasonal Autoregressive Integrated Moving Average) model that reflects multiple meteorological
variables and seasonality. It was carried out to convert daily to monthly data of sea ice products because optical sensors
have high spatial and temporal resolution than passive microwave sensors, but have difficulty observing the sea ice
because of clouds. The result showed that minimum area of sea ice was a decrease trend during the study period and the
explanatory power of the constructed Multivariate SARIMA model was about 0.71. It is thought of as a remarkable
outcome because there are no studies for the Multivariate SARIMA analysis showing high explanatory power for the
changes of sea ice extent. To improve the explanatory power of our model, it will be necessary as a future work to set the
optimal thresholds of algorithm for estimating monthly sea ice extent and to increase the accuracy of climate factors data.
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