FY-3B/MWRI北极海冰密集度ASI算法反演研究
Retrieval of the Arctic sea ice concentration from FY-3B/MWRI using ASI algorithm
- 2022年26卷第11期 页码：2121-2135
DOI： 10.11834/jrs.20210131
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摘要
近年来，由于“北极放大”的气候效应，使得北极海冰变化受到了越来越多的关注。而作为海冰被动微波遥感的主要参数，海冰密集度SIC（Sea Ice Concentration）能够表征海冰的主要状态，可用于指导极区走航以及进行不同尺度的海冰变化研究。通过该参数还可以计算出海冰面积、海冰范围等信息，对极区冰情预测以及气候变化研究具有重要意义。本研究探讨了如何利用FY-3B/MWRI（FY-3B/MicroWave Radiometer Imager）较高分辨率通道数据来反演北极地区海冰密集度。基于ASI（ARTIST（Arctic Radiation and Turbulence Interaction STudy）Sea Ice）算法，本研究通过改进算法系点值的方法反演了北极地区海冰密集度，并将反演结果与MWRI海冰密集度产品进行了对比。首先利用Aqua/MODIS（Moderate Resolution Imaging Spectroradiometer）反射率数据获得的海冰密集度对二者进行了验证。结果表明，本研究选用的新系点值ASI算法在全部数据集范围内的平均偏差与MWRI海冰密集度产品相当，但标准偏差和均方根误差均较之明显降低，且在海冰密集度低于95%时精度远高于MWRI产品；然后将二者与不莱梅大学的SIC_UB（Sea Ice Concentration from University of Bremen）海冰密集度产品进行了对比，其中本研究反演海冰密集度与SIC_UB产品的平均偏差和标准偏差分别为3.3%和10.6%，低于MWRI产品与SIC_UB产品之间的5.9%和16.4%；最后，对本研究反演结果、MWRI产品、NSIDC/AMSR-E（National Snow and Ice Data Center/Advanced Microwave Scanning Radiometer-EOS）产品以及SIC_UB产品的日均海冰密集度和海冰面积、海冰范围进行了时间序列对比，结果表明本研究反演海冰密集度的数值在3种统计方式下均显著低于MWRI产品，且较之更接近NSIDC/AMSR-E和SIC_UB产品。本研究利用国产卫星亮温数据反演的北极地区海冰密集度具有较高空间分辨率和较高精度，有利于北极地区气候变化的长时间序列研究。
Abstract
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.
关键词
海冰密集度ASI算法FY-3B/MWRIAqua/AMSR-EAqua/MODIS北极
Keywords
sea ice concentrationASI algorithmFY-3B/MWRIAqua/AMSR-EAqua/MODISArctic
references
Beitsch A. 2014. Uncertainties of a Near 90 GHz Sea Ice Concentration Retrieval Algorithm. Hamburg: University of Hamburg: 56
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 [DOI: 10.1016/S0034-4257(01)00333-9http://dx.doi.org/10.1016/S0034-4257(01)00333-9]
Carsey F D. 1992. Microwave Remote Sensing of Sea Ice. Washington: American Geophysical Union：1-3
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 [DOI: 10.5067/AMSR-E/AE_SI12.003http://dx.doi.org/10.5067/AMSR-E/AE_SI12.003]
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
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 [DOI: 10.3189/S0022143000034791http://dx.doi.org/10.3189/S0022143000034791]
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 [DOI: 10.1109/TGRS.2006.878445http://dx.doi.org/10.1109/TGRS.2006.878445]
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 [DOI: 10.1109/TGRS.2010.2046495http://dx.doi.org/10.1109/TGRS.2010.2046495]
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 [DOI: 10.1007/s13131-021-1717-2http://dx.doi.org/10.1007/s13131-021-1717-2]
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: 10.1029/JC088iC12http://dx.doi.org/10.1029/JC088iC12p07686]
Comiso J C. 1986. Characteristics of Arctic winter sea ice from satellite multispectral microwave observations. Journal of Geophysical Research, 91(C1): 975-994 [DOI: 10.1029/JC091iC01p00975http://dx.doi.org/10.1029/JC091iC01p00975]
Comiso J C. 2002. A rapidly declining perennial sea ice cover in the Arctic. Geophysical Research Letters, 29(20): 1956 [DOI: 10.1029/2002GL015650http://dx.doi.org/10.1029/2002GL015650]
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 [DOI: 10.1109/TGRS.2002.808317http://dx.doi.org/10.1109/TGRS.2002.808317]
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 [DOI: 10.1016/S0034-4257(96)00220-9http://dx.doi.org/10.1016/S0034-4257(96)00220-9]
Comiso J C and Hall D K. 2014. Climate trends in the Arctic as observed from space. WIREs Climate Change, 5(3): 389-409 [DOI: 10.1002/wcc.277http://dx.doi.org/10.1002/wcc.277]
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 [DOI: 10.1175/1520-0442(2000)013<0617:ASIVIT>2.0.CO;2http://dx.doi.org/10.1175/1520-0442(2000)013<0617:ASIVIT>2.0.CO;2]
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 [DOI: 10.1029/GM068p0047http://dx.doi.org/10.1029/GM068p0047]
Francis J A and Vavrus S J. 2012. Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophysical Research Letters, 39(6): L06801 [DOI: 10.1029/2012GL051000http://dx.doi.org/10.1029/2012GL051000]
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 [DOI: 10.5194/tc-11-1987-2017http://dx.doi.org/10.5194/tc-11-1987-2017]
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.
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 [DOI: 10.1029/JC091iC03p03913http://dx.doi.org/10.1029/JC091iC03p03913]
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 [DOI: 10.1007/s13131-015-0659-yhttp://dx.doi.org/10.1007/s13131-015-0659-y]
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 [DOI: 10.1007/s13131-021-1839-6http://dx.doi.org/10.1007/s13131-021-1839-6]
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 [DOI: 10.1109/TGRS.2006.880622http://dx.doi.org/10.1109/TGRS.2006.880622]
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 [DOI: 10.5194/tc-9-1797-2015http://dx.doi.org/10.5194/tc-9-1797-2015]
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 [DOI: 10.1126/science.286.5446.1937http://dx.doi.org/10.1126/science.286.5446.1937]
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 [DOI: 10.1080/07038992.2001.10854892http://dx.doi.org/10.1080/07038992.2001.10854892]
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 [DOI: 10.1038/nature10581http://dx.doi.org/10.1038/nature10581]
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 [DOI: 10.1029/2009JC005312http://dx.doi.org/10.1029/2009JC005312]
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 [DOI: 10.1109/IGARSS.2019.8900152http://dx.doi.org/10.1109/IGARSS.2019.8900152]
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 [DOI: 10.1109/LGRS.2015.2471849http://dx.doi.org/10.1109/LGRS.2015.2471849]
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 [DOI: 10.1109/36.843033http://dx.doi.org/10.1109/36.843033]
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 [DOI: 10.1029/2007GL032043http://dx.doi.org/10.1029/2007GL032043]
MCST (MODIS Characterization Support Team). 2017. MODIS 500 m Calibrated Radiance Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA: [10.5067/MODIS/MYD0HKM.061http://dx.doi.org/10.5067/MODIS/MYD0HKM.061]
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 [DOI: 10.1002/2013RG000431http://dx.doi.org/10.1002/2013RG000431]
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 [DOI: 10.1109/JSTARS.2017.2719624http://dx.doi.org/10.1109/JSTARS.2017.2719624]
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 [DOI: 10.3189/172756407782871170http://dx.doi.org/10.3189/172756407782871170]
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 [DOI: 10.1111/j.1600-0870.2009.00421.xhttp://dx.doi.org/10.1111/j.1600-0870.2009.00421.x]
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 [DOI: 10.3402/polar.v30i0.15787http://dx.doi.org/10.3402/polar.v30i0.15787]
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.
Rees W G. 2006. Remote Sensing of Snow and Ice. Boca Raton: CRC Press：14-19.[DOI:10.1201/9780367801069http://dx.doi.org/10.1201/9780367801069]
Screen J A and Simmonds I. 2010. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature, 464(7293): 1334-1337 [DOI: 10.1038/nature09051http://dx.doi.org/10.1038/nature09051]
Screen J A and Simmonds I. 2013. Exploring links between Arctic amplification and mid-latitude weather. Geophysical Research Letters, 40(5): 959-964 [DOI: 10.1002/grl.50174http://dx.doi.org/10.1002/grl.50174]
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
石立坚, 王其茂, 邹斌, 施英妮, 焦敏. 2014. 利用海洋(HY-2)卫星微波辐射计数据反演北极区域海冰密集度. 极地研究, 26(4): 410-417 [DOI: 10.13679/j.jdyj.2014.4.410http://dx.doi.org/10.13679/j.jdyj.2014.4.410]
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 [DOI: 10.1109/TGRS.2008.2000624http://dx.doi.org/10.1109/TGRS.2008.2000624]
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: 10.1080/01431169608949096http://dx.doi.org/10.1080/01431169608949096]
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 [DOI: 10.1029/2005JC003384http://dx.doi.org/10.1029/2005JC003384]
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
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 [DOI: 10.1080/01431168708954790http://dx.doi.org/10.1080/01431168708954790]
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
田立军, Ackley S, 吴海斌. 2021. 基于同位素示踪降水在两极海冰质量平衡中的贡献. 第四纪研究, 41(3): 766-779 [DOI: 10.11928/j.issn.1001-7410.2021.03.12http://dx.doi.org/10.11928/j.issn.1001-7410.2021.03.12]
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 [DOI: 10.1016/j.asr.2015.07.009http://dx.doi.org/10.1016/j.asr.2015.07.009]
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
王欢欢, Heygster G, 韩树宗, 程斌. 2009. 应用AMSR-E 89GHz遥感数据反演北极多年冰密集度. 极地研究, 21(3): 186-196
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
王晓雨, 管磊, 李乐乐. 2018. FY-3B/MWRI和Aqua/AMSR-E海冰密集度比较及印证. 遥感学报, 22(5): 723-736 [DOI: 10.11834/jrs.20187419http://dx.doi.org/10.11834/jrs.20187419]
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 [DOI: 10.1007/s13131-020-1646-5http://dx.doi.org/10.1007/s13131-020-1646-5]
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
杨虎, 李小青, 游然, 武胜利. 2013. 风云三号微波成像仪定标精度评价及业务产品介绍. 气象科技进展, 3(4): 136-143 [DOI: 10.3969/j.issn.2095-1973.2013.04.014http://dx.doi.org/10.3969/j.issn.2095-1973.2013.04.014]
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 [DOI: 10.1109/TGRS.2011.2148200http://dx.doi.org/10.1109/TGRS.2011.2148200]
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 [DOI: 10.1109/tgrs.2012.2197003http://dx.doi.org/10.1109/tgrs.2012.2197003]
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
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
翟召坤, 卢善龙, 王萍, 马丽娟, 李多, 任玉玉, 武胜利. 2017. 基于NSIDC海冰产品的FY北极海冰数据集优化. 地球信息科学学报, 19(2): 143-151 [DOI: 10.3724/SP.J.1047.2017.00143http://dx.doi.org/10.3724/SP.J.1047.2017.00143]
Zhang H L and Xiao X T. 2021. Review of sea-ice reconstruction approach in the Arctic Ocean. Quaternary Sciences, 41(3): 813-823
张海龙, 肖晓彤. 2021. 北冰洋海冰重建方法研究进展. 第四纪研究, 41(3): 813-823 [DOI: 10.11928/j.issn.1001-7410.2021.03.16http://dx.doi.org/10.11928/j.issn.1001-7410.2021.03.16]
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.
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 [DOI: 10.1007/s10872-012-0167-zhttp://dx.doi.org/10.1007/s10872-012-0167-z]
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 [DOI: 10.1007/s00343-018-7077-xhttp://dx.doi.org/10.1007/s00343-018-7077-x]
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
赵进平, 史久新, 王召民, 李志军, 黄菲. 2015. 北极海冰减退引起的北极放大机理与全球气候效应. 地球科学进展, 30(9): 985-995 [DOI: 10.11867/j.issn.1001-8166.2015.09.0985http://dx.doi.org/10.11867/j.issn.1001-8166.2015.09.0985]
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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