Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China
University Corporation for Polar Research, Beijing, China
Qingdao National Laboratory for Marine Science and Technology, Qingdao 266100, China
Southern Marine Science and Engineering Guangdong Laborator, Zhuhai 519082, China
College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Program of China under contract No. 2016YFC1402704; the Chinese Natural Science Foundation under contract No. 41941012 and 42076228; the Guangdong Basic and Applied Basic Research Foundation under contract No. 2019A1515110295.
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 1. Sea ice concentration in the Canadian Arctic Archipelago on August 8, 2016. Without land spillover correction (a), with land spillover correction (b), satellite imagery from NASA Worldview (https://worldview.earthdata.nasa.gov) (c).
Figure 2. Monthly averaged sea ice concentration in March. FY3B/DT-ASI (a), AMSR2/DT-ASI (b), AMSR2/ASI (c), differences between each two SIC field (d-f).
Figure 3. Monthly averaged sea ice concentration in September. FY3B/DT-ASI (a), AMSR2/DT-ASI (b), AMSR2/ASI (c), differences between each two mean SIC field (d-f).
Figure 4. Arctic sea ice concentration from FY3B/DT-ASI (a) and FY3B/NT2 (b) in May 14, 2016 as well as their difference (c).
Figure 5. Time series of the daily spatial averaged difference of FY3B/DT-ASI and FY3B/NT2, the red line represent the monthly mean (a), the monthly spatial averaged SIC difference of FY3B/DT-ASI and FY3B/NT2 (b).
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.
Figure 7. Monthly averaged SIEs and SIAs of different datasets in March and September. SIE for March (a), SIA for March (b), SIE for September (c), SIA for September (d).
Figure 8. Selection of MODIS broadband TOA reflectance images. The red, green, and blue squares show the positions of samples 10, 29, and 34, respectively.
Figure 9. MODIS broadband TOA reflectance images: sample 10 (a); sample 29 (b); sample 34 (c).
Figure 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).
Figure 11. Average SICs of 58 samples in different data.