In this study,the infrared sea surface temperature (SST) data of MODIS-Aqua,MODIS-Terra,and VIIRS in 2016 are used to investigate the SST remote sensing observation capability in the Arctic,including the statistics of infrared SST coverage and effective cover days in January and July,and analyze the accuracy of SST data with Argo.Therefore,the distribution of the Arctic SST error can be obtained intuitively and the SST remote sensing observation capability can be investigated to provide a basis for better understanding of the Arctic region,thus coping with climate change.The results show that the coverage and effective observation days of infrared radiometer SST data in July are higher than those in January,the three sets of infrared SST data did not exhibit a significant difference in January,and the coverage and effective observation days of VIIRS in July are better than those of MODIS-Aqua and MODIS-Terra.The coverage and effective observation days detected by three infrared radiometers are higher than those by a single star.The coverage rate is up to 8% in January and close to 70% in July,indicating that combining different star detection schemes is an efficient method to improve the coverage and effective cover days of SST data in the Arctic.The error of SST data in the Arctic region is generally higher than the global overall level.The E_(rms) value of VIIRS is lower than those of MODIS-Aqua and MODIS-Terra during the daytime and nighttime.On the contrary,the E_(rms) value of MODIS-Aqua during the daytime is higher than that of MODIS-Terra and the E_(rms) value of MODIS-Aqua during the nighttime is lower than that of MODIS Terra.Taken together,the coverage,effective observation days,and matching results with Argo of VIIRS in the Arctic are the optimal among the three infrared radiometers.