Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY3B/MWRI data

Hairui Hao Jie Su Qian Shi Lele Li

Hairui Hao, Jie Su, Qian Shi, Lele Li. Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY3B/MWRI data[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1839-6
Citation: Hairui Hao, Jie Su, Qian Shi, Lele Li. Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY3B/MWRI data[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1839-6

doi: 10.1007/s13131-021-1839-6

Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY3B/MWRI data

Funds: 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.
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  • 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).

    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.

    Table  1.   Algorithms, data sources, resolutions, and time ranges of the main products for Arctic SIC

    AlgorithmData sourceIssuedResolution/kmTime range
    BootstrapSMMR/SSM-I/SSMISNational Snow and Ice Data Center (NSIDC)251979 to present
    AMSR-E/AMSR2University of Bremen (UB)12.52002–2011;2012 to present
    Enhance NSAS Team (NT2)AMSR-ENational Snow and Ice Data Center (NSIDC)12.52002–2011
    MWRINational Satellite Meteorological Center (NSMC)12.52011–2019
    AMSR2Japan Aerospace Exploration Agency (JAXA)102012 to present
    OSI SAF Hybrid Dynamic
    (OSHD)
    SSMISEuropean Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)101979 to present
    AMSR2102012 to present
    Technical University of Denmark (TUD)AMSR2European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)102012 to present
    Dual-Polarized Ratio (DPR)AMSR-E/AMSR2Ocean University of China (OUC)102002–2011;2012 to present
    ARTSIST Sea Ice (ASI)AMSR-E/AMSR2University of Bremen (UB)6.252002–2011;2012 to present
    Dynamic Tie Point ASI
    (DT-ASI)
    AMSR-E/AMSR2Ocean University of China (OUC)6.252002–2011;2012 to present
    下载: 导出CSV

    Table  2.   Statistics the differences between the SICs from different data sets with MODIS (%).

    FY3B/DT-ASIFY3B/NT2AMSR2/DT-ASIAMSR2/ASI
    TotalMD0.516.5–0.9–0.4
    MAD12.719.911.210.8
    RMSD17.225.214.814.7
    SIC≥90%MD–6.40.78–8–4
    MAD10.48.810.17.7
    RMSD12.39.612.29.9
    60%≤SIC <90%MD–2.310.5–3.10.3
    MAD1415.313.612.6
    RMSD1717.416.415.7
    SIC<60%MD6.333.641.4
    MAD17.834.91615.2
    RMSD21.637.619.919.3
    下载: 导出CSV
  • [1] Bjørgo E, Johannessen O M, Miles M W. 1997. Analysis of merged SMMR-SSMI time series of Arctic and Antarctic sea ice parameters 1978–1995. Geophysical Research Letters, 24(4): 413–416. doi: 10.1029/96GL04021
    [2] Cao Meisheng, Jin Rui. 2006. Monitoring sea ice concentration using remote sensing technique. Remote Sensing Technology and Application (in Chinese), 21(3): 259–264
    [3] Cavalieri D J, Gloersen P, Campbell W J. 1984. Determination of sea ice parameters with the Nimbus 7 SMMR. Journal of Geophysical Research: Atmospheres, 89(D4): 5355–5369. doi: 10.1029/JD089iD04p05355
    [4] Cavalieri D J, Gloersen P, Parkinson C L, et al. 1997. Observed hemispheric asymmetry in global sea ice changes. Science, 278(5340): 1104–1106. doi: 10.1126/science.278.5340.1104
    [5] Cavalieri D J, Markus T, Hall D K, et al. 2010. Assessment of AMSR-E Antarctic winter sea-ice concentrations using Aqua MODIS. IEEE Transactions on Geoscience and Remote Sensing, 48(9): 3331–3339. doi: 10.1109/TGRS.2010.2046495
    [6] Cavalieri D J, Parkinson C L, Gloersen P, et al. 1999. Deriving long-term time series of sea ice cover from satellite passive-microwave multisensor data sets. Journal of Geophysical Research: Oceans, 104(C7): 15803–15814. doi: 10.1029/1999JC900081
    [7] Chen Haihua, Li Lele, Guan Lei. 2021. Cross-calibration of brightness temperature obtained by FY-3B/MWRI using Aqua/AMSR-E data for snow depth retrieval in the Arctic. Acta Oceanologica Sinica, 40(1): 43–53. doi: 10.1007/s13131-021-1717-2
    [8] Comiso J C. 1995. SSM/I sea ice concentrations using the bootstrap algorithm. Washington, DC: National Aeronautics and Space Administration, 40
    [9] Comiso J C, Cavalieri D J, Markus T. 2003. Sea ice concentration, ice temperature, and snow depth using AMSR-E data. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 243–252. doi: 10.1109/TGRS.2002.808317
    [10] Comiso J C, Kwok R. 1996. Surface and radiative characteristics of the summer Arctic sea ice cover from multisensor satellite observations. Journal of Geophysical Research: Oceans, 101(C12): 28397–28416. doi: 10.1029/96JC02816
    [11] Comiso J C, Meier W N, Gersten R. 2017. Variability and trends in the Arctic Sea ice cover: results from different techniques. Journal of Geophysical Research: Oceans, 122(8): 6883–6900. doi: 10.1002/2017JC012768
    [12] Cvijanovic I, Caldeira K, MacMartin D G. 2015. Impacts of ocean albedo alteration on Arctic sea ice restoration and Northern Hemisphere climate. Environmental Research Letters, 10(4): 044020. doi: 10.1088/1748-9326/10/4/044020
    [13] Gloersen P, Cavalieri D J. 1986. Reduction of weather effects in the calculation of sea ice concentration from microwave radiances. Journal of Geophysical Research: Oceans, 91(C3): 3913–3919. doi: 10.1029/JC091iC03p03913
    [14] Hall D K, Key J R, Casey K A, et al. 2004. Sea ice surface temperature product from MODIS. IEEE Transactions on Geoscience and Remote Sensing, 42(5): 1076–1087. doi: 10.1109/TGRS.2004.825587
    [15] Hao Guanghua, Su Jie. 2015. A study on the dynamic tie points ASI algorithm in the Arctic Ocean. Acta Oceanologica Sinica, 34(11): 126–135. doi: 10.1007/s13131-015-0659-y
    [16] Kaleschke L, Lüpkes C, Vihma T, et al. 2001. SSM/I sea ice remote sensing for mesoscale ocean-atmosphere interaction analysis. Canadian Journal of Remote Sensing, 27(5): 526–537. doi: 10.1080/07038992.2001.10854892
    [17] Lavelle J, Tonboe R, Tian T, et al. 2016. Product user manual for the OSI SAF AMSR-2 global sea ice concentration. Product OSI-408. Copenhagen, Denmark: Danish Meteorological Institute
    [18] Li Xinqing, Cheng Xiao, Hui Fengming, et al. 2016. Analysis of sea ice conditions in the Arctic northeast passage in summer 2014. Chinese Journal of Polar Research (in Chinese), 28(1): 87–94
    [19] Liu Sen, Zou Bin, Shi Lijian, et al. 2020. Polar sea ice concentration retrieval based on FY-3C microwave radiation imager data. Haiyang Xuebao (in Chinese), 42(1): 113–122
    [20] Markus T, Cavalieri D J. 2000. An enhancement of the NASA Team sea ice algorithm. IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1387–1398. doi: 10.1109/36.843033
    [21] Markus T, Cavalieri D J. 2009. The AMSR-E NT2 Sea Ice Concentration Algorithm : its Basis and Implementation. The Remote Sensing Society of Japan, 29(1): 216–225
    [22] Meier W N, Fetterer F, Stewart J S, et al. 2015. How do sea-ice concentrations from operational data compare with passive microwave estimates? Implications for improved model evaluations and forecasting. Annals of Glaciology, 56(69): 332–340. doi: 10.3189/2015AoG69A694
    [23] Parkinson C L. 1987. Arctic Sea Ice, 1973-1976: Satellite Passive-Microwave Observations. Washington, DC: Scientific and Technical Information Branch, NASA
    [24] Perovich D K, Nghiem S V, Markus T, et al. 2007. Seasonal evolution and interannual variability of the local solar energy absorbed by the Arctic Sea ice-ocean system. Journal of Geophysical Research: Oceans, 112(C3): C03005
    [25] Smith D M. 1996. Extraction of winter total sea-ice concentration in the Greenland and Barents Seas from SSM/I data. International Journal of Remote Sensing, 17(13): 2625–2646. doi: 10.1080/01431169608949096
    [26] Spreen G, Kaleschke L, Heygster G. 2008. Sea ice remote sensing using AMSR-E 89-GHz channels. Journal of Geophysical Research: Oceans, 113(C2): C02S03
    [27] Steffen K, Schweiger A. 1991. NASA team algorithm for sea ice concentration retrieval from Defense Meteorological Satellite Program special sensor microwave imager: comparison with Landsat satellite imagery. Journal of Geophysical Research: Oceans, 96(C12): 21971–21987. doi: 10.1029/91JC02334
    [28] Tang Xiaotong, Chen Haihua, Guan Lei, et al. 2020. Intercalibration of FY-3B/MWRI and GCOM-W1/AMSR-2 brightness temperature over the Arctic. Journal of Remote Sensing (Chinese), 24(8): 1032–1044
    [29] Tonboe R, Lavelle J, Pfeiffer R H, et al. 2016. Product user manual for OSI SAF global sea ice concentration. Product OSI-401-b. Copenhagen, Denmark: Danish Meteorological Institute
    [30] Wang Xiaoyu, Guan Lei, Li Lele. 2018. Comparison and validation of sea ice concentration from FY-3B/MWRI and Aqua/AMSR-E observations. Journal of Remote Sensing (in Chinese), 22(5): 723–736
    [31] Wiebe H, Heygster G, Markus T. 2009. Comparison of the ASI ice concentration algorithm with Landsat-7 ETM+ and SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 47(9): 3008–3015. doi: 10.1109/TGRS.2009.2026367
    [32] Wu Zhankai, Wang Xingdong, Wang Xuemei. 2019. An improved ARTSIST sea ice algorithm based on 19 GHz modified 91 GHz. Acta Oceanologica Sinica, 38(10): 93–99. doi: 10.1007/s13131-019-1482-7
    [33] Wu Zhankai, Wang Xingdong, Wang Feng. 2020. Study on Arctic sea ice concentration based on FY-3 MWRI data. Journal of Glaciology and Geocryology (in Chinese), 42(04): 1135–1144
    [34] Yang Hu, Zou Xiaolei, Li Xiaoqing, et al. 2012. Environmental data records from FengYun-3B microwave radiation imager. IEEE Transactions on Geoscience and Remote Sensing, 50(12): 4986–4993. doi: 10.1109/TGRS.2012.2197003
    [35] Ye Xinxin, Su Jie, Wang Yang, et al. 2011. Assessment of AMSR-E sea ice concentration in ice margin zone using MODIS data. In: 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). Nanjing, China: IEEE, 3869–3873
    [36] Zhai Zhaokun, Lu Shanlong, Wang Ping, et al. 2017. Optimization of FY arctic sea ice dataset based on NSIDC sea ice product. Journal of Geo-information Science (in Chinese), 19(2): 143–151
    [37] Zhang Shugang. 2012. Sea ice concentration algorithm and study on the physical process about sea ice and melt-pond change in central Arctic (in Chinese) [dissertation]. Qingdao: Ocean University of China
    [38] Zhang Shugang, Zhao Jinping, Frey K, et al. 2013. Dual-polarized ratio algorithm for retrieving Arctic sea ice concentration from passive microwave brightness temperature. Journal of Oceanography, 69(2): 215–227. doi: 10.1007/s10872-012-0167-z
    [39] Zhao Jinping, Ren Jingping. 2000. Study on the method to analyze parameters of Arctic sea ice from airborne digital imagery. Journal of Remote Sensing (in Chinese), 21(4): 271–278
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出版历程
  • 收稿日期:  2021-01-27
  • 录用日期:  2021-04-02
  • 网络出版日期:  2021-06-28

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