Volume 40 Issue 1
Feb.  2021
Turn off MathJax
Article Contents
Haihua Chen, Lele Li, Lei Guan. Cross-calibration of brightness temperature obtained by FY-3B/MWRI using Aqua/AMSR-E data for snow depth retrieval in the Arctic[J]. Acta Oceanologica Sinica, 2021, 40(1): 43-53. doi: 10.1007/s13131-021-1717-2
Citation: Haihua Chen, Lele Li, Lei Guan. Cross-calibration of brightness temperature obtained by FY-3B/MWRI using Aqua/AMSR-E data for snow depth retrieval in the Arctic[J]. Acta Oceanologica Sinica, 2021, 40(1): 43-53. doi: 10.1007/s13131-021-1717-2

Cross-calibration of brightness temperature obtained by FY-3B/MWRI using Aqua/AMSR-E data for snow depth retrieval in the Arctic

doi: 10.1007/s13131-021-1717-2
Funds:  The National Key Research and Development Program of China under contract Nos 2019YFA0607001 and 2016YFC1402704; the Global Change Research Program of China under contract No. 2015CB9539011.
More Information
  • Corresponding author: E-mail: leiguan@ouc.edu.cn
  • Received Date: 2020-09-25
  • Accepted Date: 2020-10-22
  • Available Online: 2021-04-21
  • Publish Date: 2021-01-25
  • This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A. The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression. The snow depth in case of seasonal ice was calculated by using parameters of the cross-calibration of data from the MWRI Tb. The correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than 0.97. The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI. Data from the MWRI Tb were cross-calibrated to the AMSR-E baseline. Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration, the standard deviations decreased significantly in the range of 1.32 K to 2.57 K, and the correlation coefficients were as high as 99%. An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well calibrated. Furthermore, the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz, 23.8 GHz, and 36.5 GHz (V polarization), and at 89 GHz (H/V polarization), and were applied to the snow depths retrieval in the Arctic. The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average Tb. The results of the snow depths were compared with those of the calibrated MWRI and AMSR-E products. Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths, with the standard deviations ranging from 4.19 cm to 4.80 cm.
  • loading
  • [1]
    Abdalati W, Steffen K, Otto C, et al. 1995. Comparison of brightness temperatures from SSMI instruments on the DMSP F8 and FII satellites for Antarctica and the Greenland ice sheet. International Journal of Remote Sensing, 16(7): 1223–1229. doi: 10.1080/01431169508954473
    [2]
    Cavalieri D J, Comiso J, Markus T. 2014. AMSR-E/Aqua Daily L3 12.5 km Brightness Temperature, Sea Ice Concentration, & Snow Depth Polar Grids, Version 3. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center
    [3]
    Cavalieri D J, Parkinson C L. 2012. Arctic sea ice variability and trends, 1979–2010. The Cryosphere, 6: 881–889. doi: 10.5194/tc-6-881-2012
    [4]
    Cavalieri D J, Parkinson C L, DiGirolamo N, et al. 2012. Intersensor calibration between F13 SSMI and F17 SSMIS for global sea ice data records. IEEE Geoscience and Remote Sensing Letters, 9(2): 233–236. doi: 10.1109/LGRS.2011.2166754
    [5]
    Cavalieri D J, Parkinson C L, Vinnikov K Y. 2003. 30-year satellite record reveals contrasting Arctic and Antarctic decadal sea ice variability. Geophysical Research Letters, 30(18): 1970
    [6]
    Chander G, Hewison T J, Fox N, et al. 2013. Overview of intercalibration of satellite instruments. IEEE Transactions on Geoscience and Remote Sensing, 51(3): 1056–1080. doi: 10.1109/TGRS.2012.2228654
    [7]
    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
    [8]
    Comiso J C, Parkinson C L, Gersten R, et al. 2008. Accelerated decline in the Arctic sea ice cover. Geophysical Research Letters, 35(1): L01703
    [9]
    Das N N, Colliander A, Chan S K, et al. 2014. Intercomparisons of brightness temperature observations over land from AMSR-E and WindSat. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 452–464. doi: 10.1109/TGRS.2013.2241445
    [10]
    Derksen C, Walker A E. 2003. Identification of systematic bias in the cross-platform (SMMR and SSM/I) EASE-grid brightness temperature time series. IEEE Transactions on Geoscience and Remote Sensing, 41(4): 910–915. doi: 10.1109/TGRS.2003.812003
    [11]
    Du Jinyang, Kimball J S, Shi Jiancheng, et al. 2014. Inter-calibration of satellite passive microwave land observations from AMSR-E and AMSR2 using overlapping FY3B-MWRI sensor measurements. Remote Sensing, 6(9): 8594–8616. doi: 10.3390/rs6098594
    [12]
    Gao Shuo, Li Zhen, Chen Quan, et al. 2019. Inter-sensor calibration between HY-2B and AMSR2 passive microwave data in land surface and first result for snow water equivalent retrieval. Sensors, 19(22): 5023. doi: 10.3390/s19225023
    [13]
    Hu Tongxi, Zhao Tianjie, Shi Jiancheng, et al. 2016. Inter-calibration of AMSR-E and AMSR2 brightness temperature. Remote Sensing Technology and Application (in Chinese), 31(5): 919–924
    [14]
    Huang Wei, Hao Yanling, Wang Jin, et al. 2013. Brightness temperature data comparison and evaluation of FY-3B microwave radiation imager with AMSR-E. Periodical of Ocean University of China (in Chinese), 43(11): 99–111
    [15]
    Jezek K C, Merry C J, Cavalieri D J. 1993. Comparison of SMMR and SSM/I passive microwave data collected over Antarctica. Annals of Glaciology, 17: 131–136. doi: 10.3189/S0260305500012726
    [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]
    Kelly R E, Chang A T, Tsang L, et al. 2003. A prototype AMSR-E global snow area and snow depth algorithm. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 230–242. doi: 10.1109/TGRS.2003.809118
    [18]
    Li Lele, Chen Haihua, Guan Lei. 2019. Retrieval of snow depth on sea ice in the arctic using the FengYun-3B microwave radiation imager. Journal of Ocean University of China, 18(3): 580–588. doi: 10.1007/s11802-019-3873-y
    [19]
    Li Qin, Zhong Ruofei. 2011. Multiple surface parameters retrieval of simulated AMSR-E data. Remote Sensing for Land and Resources (in Chinese), 23(1): 42–47
    [20]
    Liu Qingquan, Ji Qing, Pang Xiaoping, et al. 2018. Inter-calibration of passive microwave satellite brightness temperatures observed by F13 SSM/I and F17 SSMIS for the retrieval of snow depth on Arctic first-year sea ice. Remote Sensing, 10(1): 36
    [21]
    Lu Zhou, Stroeve J, Xu Shiming, et al. 2020. Inter-comparison of snow depth over sea ice from multiple methods. The Cryosphere Discussions, preprint, https://doi.org/10.5194/tc-2020-65
    [22]
    Markus T, Cavalieri D J. 1998. Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data. In: Jeffries M O, ed. Antarctic Sea Ice: Physical Processes, Interactions and Variability. Washington, DC: American Geophysical Union, 19–40
    [23]
    Markus T, Cavalieri D J. 2008. AMSR-E algorithm theoretical basis document supplement: Sea ice products. Greenbelt, MD, USA: Hydrospheric and Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, 1–9
    [24]
    Maslowski W, Kinney J C, Higgins M, et al. 2012. The future of arctic sea ice. Annual Review of Earth and Planetary Sciences, 40(1): 625–654. doi: 10.1146/annurev-earth-042711-105345
    [25]
    Massom R A, Harris P T, Michael K J, et al. 1998. The distribution and formative processes of latent-heat polynyas in East Antarctica. Annals of Glaciology, 27: 420–426. doi: 10.3189/1998AoG27-1-420-426
    [26]
    Meier W N, Khalsa S J S, Savoie M H. 2011. Intersensor calibration between F-13 SSM/I and F-17 SSMIS near-real-time sea ice estimates. IEEE Transactions on Geoscience and Remote Sensing, 49(9): 3343–3349. doi: 10.1109/TGRS.2011.2117433
    [27]
    Nihashi S, Ohshima K I, Tamura T, et al. 2009. Thickness and production of sea ice in the Okhotsk Sea coastal polynyas from AMSR-E. Journal of Geophysical Research, 114(C10): C10025. doi: 10.1029/2008JC005222
    [28]
    Parkinson C L, Cavalieri D J. 2008. Arctic sea ice variability and trends, 1979–2006. Journal of Geophysical Research: Oceans, 113(C7): C07003
    [29]
    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
    [30]
    Stroeve J, Maslanik J, Li Xiaoming. 1998. An intercomparison of DMSP F11- and F13-derived sea ice products. Remote Sensing of Environment, 64(2): 132–152. doi: 10.1016/S0034-4257(97)00174-0
    [31]
    Svendsen E, Kloster K, Farrelly B, et al. 1983. Norwegian remote sensing experiment: Evaluation of the nimbus 7 scanning multichannel microwave radiometer for sea ice research. Journal of Geophysical Research: Oceans, 88(C5): 2781–2791. doi: 10.1029/JC088iC05p02781
    [32]
    Yang Hu, Weng Fuzhong, Lv Liqing, et al. 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.2011.2148200
    [33]
    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
    [34]
    Zhang Shugang. 2012. An algorithm to detect arctic sea ice edge using microwave brightness temperature. Periodical of Ocean University of China (in Chinese), 42(11): 1–7
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(4)

    Article Metrics

    Article views (333) PDF downloads(8) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return