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Retrieval of the Arctic sea ice concentration from FY-3B/MWRI using ASI algorithm

  • role： First author第一作者
  • Affiliation：

    Faculty of Information Science & Engineering, Ocean University of China, Qingdao 266100, China

    University Corporation for Polar Research, Beijing 100875, China

  • Email：lilele@oucedu.cn
  • Introduction：李乐乐， 研究方向为极区冰雪遥感。E-mail： lilele@oucedu.cn
LI Lele12,  
  • Affiliation：

    Faculty of Information Science & Engineering, Ocean University of China, Qingdao 266100, China

WANG Xiaoyu1,  
  • Affiliation：

    Faculty of Information Science & Engineering, Ocean University of China, Qingdao 266100, China

    University Corporation for Polar Research, Beijing 100875, China

CHEN Haihua12,  
  • Affiliation：

    University Corporation for Polar Research, Beijing 100875, China

    Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao 266100, China

SU Jie23,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Information Science & Engineering, Ocean University of China, Qingdao 266100, China

    University Corporation for Polar Research, Beijing 100875, China

    Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China

  • Email：leiguan@ouc.edu.cn
  • Introduction：管磊， 研究方向为卫星海洋遥感。E-mail： leiguan@ouc.edu.cn
GUAN Lei124*

Resümee

The change of Arctic sea ice has recently attracted much attention among climate researchers due to the climate effect of “Arctic Amplification”. Sea ice concentration, which is the main parameter of passive microwave remote sensing of sea ice, can characterize the sea ice conditions, which can be used to guide the polar navigation and study the sea ice change in different scales. The sea ice area and sea ice extent can also be calculated by using the sea ice concentration, which is of great significance for the forecast of polar sea ice conditions and the study of climate change. This work discusses how to use the high resolution channels of FY-3B/MWRI (FY-3B/Microwave Radiometer Imager) to retrieve the sea ice concentrations in the Arctic. Based on the ASI (ARTIST [Arctic Radiation and Turbulence Interaction Study] Sea Ice) algorithm, the Arctic sea ice concentration is calculated in this study by improving the tie points of the algorithm. According to the cross calibration of brightness temperatures between the FY-3B/MWRI and the Aqua/AMSR-E (Advanced Microwave Scanning Radiometer-EOS), the differences between the two brightness temperature data are between ±4 K, which will result in a maximum bright temperature difference of 8 K. Accordingly, this study first sets the variation range of the tie points for FY-3B/MWRI centered on the original values of the ASI algorithm to 11.7±8 K for sea ice and 47.0±8 K for open water, separately, with a step length of 1 K. After the combination, 289 series of point value combinations are obtained. Then, the sea ice concentrations corresponding to each set of tie points are compared with those of the AMSR-E L3 product. Meanwhile, the tie points corresponding to the smallest deviation of the two data sets are selected. According to the tie points determined above, this study calculated the Arctic sea ice concentrations based on the FY-3B/MWRI brightness temperatures, hereinafter referred to as the Retrieved Sea Ice Concentration (RSIC). The RSICs in this study are compared with the MWRI Level 2 sea ice concentration product (hereinafter referred to as MWRI). First, the sea ice concentrations obtained from the Aqua/MODIS (Moderate Resolution Imaging Spectroradiometer) reflectivity data from July to September 2011 are used to verify the two data sets. The results show that the bias of RSIC is comparable to that of the MWRI product. However, the standard deviation and root mean square error are significantly reduced. Meanwhile, the accuracy of RSIC is much higher than that of the MWRI product in the areas with a sea ice concentration lower than 95%. Then, the two sea ice concentration data sets are compared with the sea ice concentration product from the University of Bremen (SIC_UB). The bias and the standard deviation between RSIC and SIC_UB are 3.3% and 10.6%, which are lower than the values between the MWRI and the SIC_UB products: 5.9% and 16.4%, respectively. Finally, the time series of the daily averaged sea ice concentration, sea ice area, and sea ice extent from the RSIC, MWRI, SIC_UB, and NSIDC/AMSR-E (National Snow and Ice Data Center/Advanced Microwave Scanning Radiometer-E) sea ice concentration products are compared. The results show that the values of RSIC are significantly lower than those of the MWRI product in three statistical methods and much closer to the AMSR-E and SIC_UB products. In this study, the sea ice concentrations in the Arctic region are retrieved based on the brightness temperatures from the FY-3B/MWRI high frequency channels. The sea ice concentrations in this study have a higher spatial resolution and a better accuracy compared with the FY-3B/MWRI L2 sea ice concentration product, which is conducive to the long-time series study of climate change in the Arctic.

Schlüsselwort

sea ice concentration;ASI algorithm;FY-3B/MWRI;Aqua/AMSR-E;Aqua/MODIS;Arctic

References

  1. 1.
    Beitsch A. 2014. Uncertainties of a Near 90 GHz Sea Ice Concentration Retrieval Algorithm. Hamburg: University of Hamburg: 56
  2. 2.
    Belchansky G I and Douglas D C. 2002. Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data. Remote Sensing of Environment, 81(1): 67-81
  3. 3.
    Carsey F D. 1992. Microwave Remote Sensing of Sea Ice. Washington: American Geophysical Union：1-3
  4. 4.
    Cavalieri D J, Comiso J J and Markus T. 2014. AMSR-E/Aqua Daily L3 12.5 km Brightness Temperature, Sea Ice Concentration, and Snow Depth Polar Grids, Version 3. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center
  5. 5.
    Cavalieri D J, Crawford J, Drinkwater M, Emery W J, Eppler D T, Farmer L D, Goodberlet M, Jentz R, Milman A, Morris A, Onstott C, Schweiger A, Shuchman R, Steffen K, Swift C, Wackerman C and Weaver R L. 1992. NASA Sea Ice Validation Program for the DMSP SSM/I: Final Report. NASA TM 104559. Washington, DC: National Aeronautics and Space Administration：126
  6. 6.
    Cavalieri D J, Germain St K M and Swift C T. 1995. Reduction of weather effects in the calculation of sea-ice concentration with the DMSP SSM/I. Journal of Glaciology, 41(139): 455-464
  7. 7.
    Cavalieri D J, Markus T, Hall D K, Gasiewski A J, Klein M and Ivanoff A. 2006. Assessment of EOS Aqua AMSR-E Arctic sea ice concentrations using Landsat-7 and airborne microwave imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3057-3069
  8. 8.
    Cavalieri D J, Markus T, Hall D K, Ivanoff A and Glick E. 2010. Assessment of AMSR-E Antarctic winter sea-ice concentrations using Aqua MODIS. IEEE Transactions on Geoscience and Remote Sensing, 48(9): 3331-3339
  9. 9.
    Chen H H, Li L L and Guan L. 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
  10. 10.
    Comiso J C. 1983. Sea ice effective microwave emissivities from satellite passive microwave and infrared observations. Journal of Geophysical Research, 88(C12): 7686-7704 [DOI: p07686]
  11. 11.
    Comiso J C. 1986. Characteristics of Arctic winter sea ice from satellite multispectral microwave observations. Journal of Geophysical Research, 91(C1): 975-994
  12. 12.
    Comiso J C. 2002. A rapidly declining perennial sea ice cover in the Arctic. Geophysical Research Letters, 29(20): 1956
  13. 13.
    Comiso J C, Cavalieri D J and 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
  14. 14.
    Comiso J C, Cavalieri D J, Parkinson C L and Gloersen P. 1997. Passive microwave algorithms for sea ice concentration: a comparison of two techniques. Remote Sensing of Environment, 60(3): 357-384
  15. 15.
    Comiso J C and Hall D K. 2014. Climate trends in the Arctic as observed from space. WIREs Climate Change, 5(3): 389-409
  16. 16.
    Deser C, Walsh J E and Timlin M S. 2000. Arctic sea ice variability in the context of recent atmospheric circulation trends. Journal of Climate, 13(3): 617-633
  17. 17.
    Eppler D T, Farmer L D, Lohanick A W, Anderson M R, Cavalieri D J, Comiso J, Gloersen P, Garrity C, Grenfell T C, Hallikainen M, Maslanik J A, MäTzler C, Melloh R A, Rubinstein I and Swift C T. 1992. Passive microwave signatures of sea ice//Carsey F D ed. Microwave Remote Sensing of Sea Ice. Washington: American Geophysical Union
  18. 18.
    Francis J A and Vavrus S J. 2012. Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophysical Research Letters, 39(6): L06801
  19. 19.
    Gabarro C, Turiel A, Elosegui P, Pla-Resina J A and Portabella M. 2017. New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator. The Cryosphere, 11(4): 1987-2002
  20. 20.
    Gloersen P, Willicit T T, Chang T C, Nordberg W and Campbell W J. 1974. Microwave maps of the polar ice of the Earth. Bulletin of the American Meteorological Society, 55: 1442-1448.
  21. 21.
    Gloersen P and 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
  22. 22.
    Hao G H and Su J. 2015. A study on the dynamic tie points ASI algorithm in the Arctic ocean. Acta Oceanologica Sinica, 34(11): 126-135
  23. 23.
    Hao H, Su J, Shi Q and Li L. 2021. Arctic Sea Ice Concentration Retrieval using DT-ASI algorithm based on FY3B/MWRI data, Acta Oceanologica Sinica, 40(9): 1-13
  24. 24.
    Heinrichs J F, Cavalieri D J and Markus T. 2006. Assessment of the AMSR-E sea ice-concentration product at the ice edge using Radarsat-1 and MODIS imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3070-3080
  25. 25.
    Ivanova N, Pedersen L T, Tonboe R T, Kern S, Heygster G, Lavergne T, Sørensen A, Saldo R, Dybkjær G, Brucker L and Shokr M. 2015. Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations. The Cryosphere, 9(5): 1797-1817
  26. 26.
    Johannessen O M, Shalina E V and Miles M W. 1999. Satellite evidence for an Arctic sea ice cover in transformation. Science, 286(5446): 1937-1939
  27. 27.
    Kaleschke L, Lüpkes C, Vihma T, Haarpaintner J, Bochert A, Hartmann J and Heygster G. 2001. SSM/I sea ice remote sensing for mesoscale ocean-atmosphere interaction analysis. Canadian Journal of Remote Sensing, 27(5): 526-537
  28. 28.
    Kinnard C, Zdanowicz C M, Fisher D A, Isaksson E, De Vernal A and Thompson L G. 2011. Reconstructed changes in Arctic sea ice over the past 1,450 years. Nature, 479(7374): 509-512
  29. 29.
    Kwok R, Cunningham G F, Wensnahan M, Rigor I, Zwally H J and Yi D. 2009. Thinning and volume loss of the Arctic Ocean sea ice cover: 2003-2008. Journal of Geophysical Research: Oceans, 114(C7): L07005
  30. 30.
    Li L L, Chen H H, Wang X Y and Guan L. 2019. Study on the retrieval of sea ice concentration from FY3B/MWRI in the Arctic//2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE: 4242-4245
  31. 31.
    Liu T T, Liu Y X, Huang X and Wang Z. 2015. Fully constrained least squares for Antarctic sea ice concentration estimation utilizing passive microwave data. IEEE Geoscience and Remote Sensing Letters, 12(11): 2291-2295
  32. 32.
    Markus T and Cavalieri D J. 2000. An enhancement of the NASA Team sea ice algorithm. IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1387-1398
  33. 33.
    Maslanik J A, Fowler C, Stroeve J, Drobot S, Zwally J, Yi D and Emery W. 2007. A younger, thinner Arctic ice cover: increased potential for rapid, extensive sea-ice loss. Geophysical Research Letters, 34(24): L24501
  34. 34.
    MCST (MODIS Characterization Support Team). 2017. MODIS 500 m Calibrated Radiance Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA: []
  35. 35.
    Meier W N, Hovelsrud G K, Van Oort B E H, Key J R, Kovacs K M, Michel C, Haas C, Granskog M A, Gerland S, Perovich D K, Makshtas A and Reist J D. 2014. Arctic sea ice in transformation: a review of recent observed changes and impacts on biology and human activity. Reviews of Geophysics, 52(3): 185-217
  36. 36.
    Meier W N and Ivanoff A. 2017. Intercalibration of AMSR2 NASA team 2 algorithm sea ice concentrations with AMSR-E slow rotation data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9): 3923-3933
  37. 37.
    Meier W N, Stroeve J C and Fetterer F. 2007. Whither Arctic sea ice? A clear signal of decline regionally, seasonally and extending beyond the satellite record. Annals of Glaciology, 46: 428-434
  38. 38.
    Overland J E and Wang M Y. 2010. Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea ice. Tellus A: Dynamic Meteorology and Oceanography, 62(1): 1-9
  39. 39.
    Overland J E, Wood K R and Wang M Y. 2011. Warm Arctic - cold continents: climate impacts of the newly open Arctic Sea. Polar Research, 30: 15787
  40. 40.
    Ramseier R O. 1991. Sea ice validation//Hollinger J P, ed. DMSP Special Sensor Microwave/Imager Calibration/Validation-Final Report, vol. Ⅱ. Washington: Naval Research Laboratory.
  41. 41.
    Rees W G. 2006. Remote Sensing of Snow and Ice. Boca Raton: CRC Press：14-19.
  42. 42.
    Screen J A and Simmonds I. 2010. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature, 464(7293): 1334-1337
  43. 43.
    Screen J A and Simmonds I. 2013. Exploring links between Arctic amplification and mid-latitude weather. Geophysical Research Letters, 40(5): 959-964
  44. 44.
    Shi L J, Wang Q M, Zou B, Shi Y N and Jiao M. 2014. Arctic sea ice concentration retrieval using HY-2 radiometer data. Chinese Journal of Polar Research, 26(4): 410-417
  45. 45.
    Shokr M, Lambe A and Agnew T. 2008. A new algorithm (ECICE) to estimate ice concentration from remote sensing observations: an application to 85-GHz passive microwave data. IEEE Transactions on Geoscience and Remote Sensing, 46(12): 4104-4121
  46. 46.
    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
  47. 47.
    Spreen G, Kaleschke L and Heygster G. 2008. Sea ice remote sensing using AMSR-E 89-GHz channels. Journal of Geophysical Research, 113(C2): C02S03
  48. 48.
    Su J, Hao G H, Ye X X and Wang W B. 2013. The experiment and validation of sea ice concentration AMSR-E retrieval algorithm in polar region. Journal of Remote Sensing, 17(3):495-513
  49. 49.
    Svendsen E, Matzler C and Grenfell T C. 1987. A model for retrieving total sea ice concentration from a spaceborne dual-polarized passive microwave instrument operating near 90 GHz. International Journal of Remote Sensing, 8(10): 1479-1487
  50. 50.
    Tian L J, Ackley S, Wu H B. 2021. Precipitation contributions in the mass balance of Arctic and Antarctic sea ice based on stable-isotope tracers. Quaternary Sciences, 41(3): 766-779
  51. 51.
    Tikhonov V V, Repina I A, Raev M D, Sharkov E A, Ivanov V V, Boyarskii D A, Alexeeva T A and Komarova N Y. 2015. A physical algorithm to measure sea ice concentration from passive microwave remote sensing data. Advances in Space Research, 56(8): 1578-1589
  52. 52.
    Wang H H, Heygster G, Han S Z and Cheng B. 2009. Arctic multiyear ice concentration retrieval based on AMSR-E 89 GHz data. Chinese Journal of Polar Research, 21(3): 186-196
  53. 53.
    Wang X Y, Guan L and Li L L. 2018. Comparison and validation of sea ice concentration from FY-3B/MWRI and Aqua/AMSR-E observations. Journal of Remote Sensing, 22(5): 723-736
  54. 54.
    Xiu Y R, Li Z J, Lei R B, Wang Q K, Lu P and Leppäranta M. 2020. Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010-2018. Acta Oceanologica Sinica, 39(9): 38-49
  55. 55.
    Yang H, Li X Q, You R and Wu S L. 2013. Environmental data records from FengYun-3B microwave radiation imager. Advances in Meteorological Science and Technology, 3(4): 136-143
  56. 56.
    Yang H, Weng F Z, Lv L Q, Lu N M, Liu G F, Bai M, Qian Q Y, He J K and Xu H X. 2011. The FengYun-3 microwave radiation imager on-orbit verification. IEEE Transactions on Geoscience and Remote Sensing, 49(11): 4552-4560
  57. 57.
    Yang H, Zou X L, Li X Q and You R. 2012. Environmental data records from FengYun-3B microwave radiation imager. IEEE Transactions on Geoscience and Remote Sensing, 50(12): 4986-4993
  58. 58.
    Ye X X, Su J, Wang Y, Hao G H and Hou J Q. 2011. Assessment of AMSR-E sea ice concentration in ice margin zone using MODIS data. International Conference on Remote Sensing, Environment and Transportation Engineering, 3869-3873
  59. 59.
    Zhai Z K, Lu S L, Wang P, Ma L J, Li D, Ren Y Y and Wu S L. 2017. Optimization of FY arctic sea ice dataset based on NSIDC sea ice product. Journal of Geo-information Science, 19(2): 143-151
  60. 60.
    Zhang H L and Xiao X T. 2021. Review of sea-ice reconstruction approach in the Arctic Ocean. Quaternary Sciences, 41(3): 813-823
  61. 61.
    Zhang L, Zhang Z H, Li Q and Wu H D. 2010. Status of the Recent Declining of Arctic Sea Ice Studies. Advances in Polar Science, 21(1): 71-80.
  62. 62.
    Zhang S G, Zhao J P, Frey K and Su J. 2013. Dual-polarized ratio algorithm for retrieving Arctic sea ice concentration from passive microwave brightness temperature. Journal of Oceanography, 69(2): 215-227
  63. 63.
    Zhang S G, Zhao J P, Li M, Liu S X and Zhang S W. 2018. An improved dual-polarized ratio algorithm for sea ice concentration retrieval from passive microwave satellite data and inter-comparison with ASI, ABA and NT2. Journal of Oceanology and Limnology, 36(5): 1494-1508
  64. 64.
    Zhao J P, Shi J X, Wang Z M, Li Z J and Huang F. 2015. Arctic amplification produced by sea ice retreat and its global climate effects. Advances in Earth Science, 30(9): 985-995
  65. 65.
    Zwally H J, Comiso J C, Parkinson C L, Campbell W J, Carsey F D and Gloersen P. 1983. Antarctic Sea Ice, 1973-1976: Satellite Passive-Microwave Observations//NASA Technical Report. Fort Belvoir, VA, USA

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