Abstract Climate change is amplified in the Arctic region (north of 60°N) relative to elsewhere. By analyzing climate model simulations, it has been found that the largest factor in Arctic Amplification comes from enhanced temperature feedbacks, caused by the different vertical atmospheric warming profiles and a larger temperature change for longwave emission per unit of warming at high- compared to low-latitudes. Radiometers on polar-orbiting satellites offer the best mechanism to derive sea surface temperature (SST) in the Arctic, but given that the retrieval algorithms in the infrared (IR) are designed to compensate for the effects of the atmosphere, mainly water vapor (WV), IR satellite-derived SSTs have larger inaccuracies at high latitudes because the atmosphere can be very dry and cold. So, the motivation of this study is to improve the algorithms to obtain more accurate SSTs which can be used to research the feedback mechanisms. To undertake the study, the 5-year matchup database (MUDB) for MODIS (Moderate resolution Imaging Spectroradiometer) on Aqua has been analyzed to characterize the differences between collocated and simultaneous satellite retrieved skin SSTs (SSTskin) using the R2014 dataset and in situ buoy temperatures, and to identify the main causes of the discrepancies. According to the radiative transfer simulations, the sea surface emissivity is proven to be significant in satellite SSTskin retrievals at high latitudes due to the low atmospheric WV content, especially during winter. An Emissivity-introduced Brightness Temperature Difference (E∆BT) is developed to correct this emissivity effect in the algorithm. Furthermore, the reference temperature as a weighting factor for differences in brightness temperature (BT) measured in MODIS bands 31 and 32 has also been adjusted. The satellite SSTskin mean bias and standard deviation are reduced by 0.35 K and 0.085 K after the corrections. The approaches presented in this paper are capable to make more appropriate atmospheric corrections for MODIS SSTskin retrieval algorithm, leading to regional optimization of the SST retrievals. We report on the progress towards improving the satellite-derived SSTskin with the expectation that the near two-decadal time series of MODIS SSTskin fields will contribute to studying climate change in the Arctic.