Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY3B/MWRI data
Abstract: Sea ice concentration (SIC) is one of the most important indicators when monitoring climate changes in the polar region. With the development of the Chinese satellite technology, the Feng Yun (FY) series has been applied to retrieve the sea ice parameters in the polar region. In this paper, to improve the SIC retrieval accuracy from the passive microwave (PM) data of the MWRI (Microwave Radiometer Imager) aboard on the FengYun-3B (FY3B) satellite, the DT-ASI (dynamic tie-point ASI) SIC retrieval algorithm is applied and obtained Arctic SIC data for nearly 10 years (from November 18, 2010 to August 19, 2019). Also, by applying a land spillover correction scheme, the erroneous sea ice along coastlines in melt season is removed. The results of FY3B/DT-ASI are obviously improved compared to that of FY3B/NT2 in both SIC and sea ice extent (SIE), and are highly consistent with the results of similar products of AMSR2/ASI and AMSR2/DT-ASI. Compared with the annual average SIC of FY3B/NT2, our result is reduced by 2.31%. The annual average SIE difference between the two FY3Bs is 1.65×106 km2, of which the DT-ASI algorithm contributes 87.9% and the land spillover method contributes 12.1%. We further select 58 MODIS cloud-free samples in the Arctic region and use the tie-point method to retrieve SIC to verify the accuracy of these SIC products. The root mean square difference (RMSD) and mean absolute difference (MAD) of the FY3B/DT-ASI and MODIS results are 17.2% and 12.7%, which is close to those of two AMSR2 products with 6.25 km resolution and decreased 8% and 7.2% compared with FY3B/NT2. Further, FY3B/DT-ASI has the most significant improvement where the SIC is lower than 60%. A high-quality SIC product can be obtained by using the DT-ASI algorithm and our work will be beneficial to promote the application of Fengyun Satellite.
Figure 6. Time series of comparison of SIEs from different datasets (a), differences SIEs with those from SSMI/NT2 (b), differences SIEs with those from AMSR2/ASI (c), time series of comparison of SIAs from different datasets (d), differences SIAs with those from AMSR2/ASI (e). The color coding of the lines in the different plots is the same as for the respective SIE and SIA plots. The brown dotted line is FY3B/DT-ASI (V0) which represents without using the land spillover method.
10. SICs corresponding to the three selected samples. MODIS SIC with a resolution of 250 m (a), MODIS SIC with a resolution of 6.25 km (b), FY3B/NT2 with a resolution of 12.5 km (NSMC) (c), FY3B/DT-ASI with a resolution of 12.5 km (d), AMSR2/DT-ASI with a resolution of 6.25 km (e), AMSR2/ASI with a resolution of 6.25 km (UB) (f).
Table 1. Algorithms, data sources, resolutions, and time ranges of the main products for Arctic SIC
Algorithm Data source Issued Resolution/km Time range Bootstrap SMMR/SSM-I/SSMIS National Snow and Ice Data Center (NSIDC) 25 1979 to present AMSR-E/AMSR2 University of Bremen (UB) 12.5 2002–2011;2012 to present Enhance NSAS Team (NT2) AMSR-E National Snow and Ice Data Center (NSIDC) 12.5 2002–2011 MWRI National Satellite Meteorological Center (NSMC) 12.5 2011–2019 AMSR2 Japan Aerospace Exploration Agency (JAXA) 10 2012 to present OSI SAF Hybrid Dynamic
SSMIS European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) 10 1979 to present AMSR2 10 2012 to present Technical University of Denmark (TUD) AMSR2 European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) 10 2012 to present Dual-Polarized Ratio (DPR) AMSR-E/AMSR2 Ocean University of China (OUC) 10 2002–2011;2012 to present ARTSIST Sea Ice (ASI) AMSR-E/AMSR2 University of Bremen (UB) 6.25 2002–2011;2012 to present Dynamic Tie Point ASI
AMSR-E/AMSR2 Ocean University of China (OUC) 6.25 2002–2011;2012 to present
Table 2. Statistics the differences between the SICs from different data sets with MODIS (%).
FY3B/DT-ASI FY3B/NT2 AMSR2/DT-ASI AMSR2/ASI Total MD 0.5 16.5 –0.9 –0.4 MAD 12.7 19.9 11.2 10.8 RMSD 17.2 25.2 14.8 14.7 SIC≥90% MD –6.4 0.78 –8 –4 MAD 10.4 8.8 10.1 7.7 RMSD 12.3 9.6 12.2 9.9 60%≤SIC <90% MD –2.3 10.5 –3.1 0.3 MAD 14 15.3 13.6 12.6 RMSD 17 17.4 16.4 15.7 SIC<60% MD 6.3 33.6 4 1.4 MAD 17.8 34.9 16 15.2 RMSD 21.6 37.6 19.9 19.3
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