Fully Constrained Least Squares for Antarctic Sea Ice Concentration Estimation Utilizing Passive Microwave Data

To improve the accuracy of the traditional NASA Team (NT) sea ice concentration (SIC) algorithm, a new SIC estimation method is proposed by combining the NT algorithm and a numerical optimization technique with Special Sensor Microwave/Imager (SSM/I) data. In this method, the noise is taken into consideration to improve the SIC estimation equation, and then, the least squares method is used to further optimize the estimation results from the improved equation. Validation was performed using a comparison between the results from the SSM/I-based SICs (the proposed method, the NT algorithm, and the bootstrap algorithm) and in situ data. The quantitative results show that the proposed method generates a more accurate SIC with smaller bias (-3.2-2.8) and root-mean-square error (7.7-18.4) than the other two algorithms.