Cross-calibration of brightness temperature obtained by FY-3B/MWRI using Aqua/AMSR-E data for snow depth retrieval in the Arctic
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Abstract: 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.
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Key words:
- FY-3B /
- AMSR-E /
- brightness temperature (Tb) /
- cross-calibration /
- snow depth /
- Arctic
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Figure 1. Cross-calibration for 10.7–89.0 GHz (H/V polarization, ascending) in January 2011.
Figure 2. Statistical histograms of data on Tb of the MWRI and AMSR-E (before and after calibration) for all channels with H/V polarization (left: original MWRI and AMSR-E; right: corrected MWRI and AMSR-E).
Figure 3. Comparison of daily average values of Tb for the MWRI and AMSR-E (V polarization 89 GHz, on January 1, 2011).
Figure 4. Comparison of the biases, STD, and RMSE in terms of values of Tb between the MWRI and the AMSR-E (channels: V18.7 GHz, V23.8 GHz, V36.5 GHz, H/V 89.0 GHz), before and after cross calibration.
Figure 6. Comparison of distributions of snow depth derived from the MWRI before and after cross-calibration, and those of the AMSR-E level 3 in the Arctic.
Figure 7. Comparison of snow depth obtained by the MWRI and AMSR-E L3 from January 1 to April 30, 2011 (120 d).
Figure 8. The histogram of snow depth biases between the MWRI (before and after cross-calibration) and AMSR-E data from January 1 to April 30, 2011
Figure 9. The Mean absolute error (MAE) of snow depths between the MWRI results and the AMSR-E L3 products from January 1 to April 30, 2011 (120 d).
Table 1. Comparison of AMSR-E and MWRI parameters
Parameter Sensor AMSR-E/AQUA MWRI/FY-3B Band 1 GHz 10.7 18.7 23.8 36.5 89.0 10.65 18.7 23.8 36.5 89.0 Spatial resolution 51 km×29 km 27 km×16 km 32 km×18 km 14 km×8 km 6 km×4 km 51 km×85 km 30 km×50 km 27 km×45 km 18 km×30 km 9 km×15 km Bandwidths/MHz 100 200 400 1 000 3 000 180 200 400 900 4 600 Polarization V/H V/H Equatorial time 13:30 (ascending)
01:30 (descending)13:40 (ascending)
01:40 (descending)Incident angle/(°) 55.0 53.5 Swath width/km 1 450 1 400 
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Table 2. Comparison of statistical parameters before and after the cross-calibration of matching data on values of Tb of the MWRI and AMSR-E
Channel Before cross-calibration/after cross-calibration Ascending Descending Number Bias/K STD/K R Number Bias/K STD/K R 10.7 GHz (V) 27 419 394 –3.814 9/0.004 0 4.965 4/1.714 6 0.997 4/0.999 1 20 619 591 –3.731 0/–0.002 4 5.198 5/1.689 0 0.997 6/0.999 2 10.7 GHz (H) 25 560 857 –0.701 3/0.000 1 4.081 3/2.399 3 0.998 3/0.999 4 19 202 858 0.322 3/0.002 6 3.680 0/2.312 5 0.998 8/0.999 5 18.7 GHz (V) 27 905 067 –1.646 7/–0.0009 4.009 5/1.352 1 0.997 4/0.999 1 21 042 823 –1.644 1/–0.006 3 4.126 6/1.316 5 0.997 7/0.999 2 18.7 GHz (H) 25 962 969 0.858 3/0.000 5 3.006 0/1.909 7 0.998 8/0.999 5 19 544 190 1.504 2/–0.004 9 2.895 4/1.883 1 0.999 0/0.999 5 23.8 GHz (V) 27 975 385 –3.075 7/0.000 3 3.844 0/1.669 8 0.992 7/0.997 5 21 111 826 –2.783 2/–0.000 2 3.973 3/1.624 7 0.993 3/0.997 7 23.8 GHz (H) 25 151 517 –0.164 6/–0.003 4 3.278 1/2.140 5 0.997 6/0.998 9 18 840 245 0.494 6/–0.002 3 3.240 8/2.167 5 0.997 9/0.999 0 36.5 GHz (V) 27 461 876 –2.980 8/0.001 4 5.259 5/2.246 7 0.974 6/0.992 8 20 794 592 –3.277 8/–0.003 7 5.246 0/2.127 9 0.976 9/0.992 7 36.5 GHz (H) 22 491 629 0.869 9/–0.004 6 3.317 3/2.219 8 0.996 8/0.998 5 16 585 284 1.516 8/0.000 5 3.378 8/2.259 7 0.996 9/0.998 5 89.0 GHz (V) 22304767 –1.424 5/0.006 9 2.694 3/2.441 5 0.988 7/0.990 4 18 163 675 –1.108 7/0.003 2 2.626 5/2.393 4 0.988 5/0.990 3 89.0 GHz (H) 15749987 –0.286 0/–0.000 9 2.697 6/2.531 8 0.991 9/0.993 0 11 461 719 0.082 6/–0.008 9 2.747 6/2.567 2 0.990 5/0.991 8
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Table 3. Comparison of statistical parameters before and after cross-calibration between the MWRI and AMSR-E in terms of data on the daily average Tb
Channel Frequency/
GHzBefore cross-calibration/
after cross-calibrationBias/K STD/K RMSE/K V 18.7 –1.98/–0.06 3.06/1.59 4.33/1.64 V 23.8 –3.27/–0.07 2.68/1.87 4.82/1.96 V 36.5 –3.40/–0.24 3.38/2.57 5.90/2.68 V 89.0 –1.39/–0.16 3.41/3.44 3.84/3.46 H 89.0 –0.90/–0.71 5.82/5.90 6.00/5.96
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Table 4. Monthly statistical evaluation of differences in snow depth between the MWRI and the AMSR-E L3 after cross-calibration
Time Number Bias/cm STD/cm RMSE/cm Jan. 2011 1 317 171 0.25 4.80 4.81 Feb. 2011 1 374 930 0.31 4.59 4.60 Mar. 2011 1 554 678 0.38 4.57 4.59 Apr. 2011 1 448 672 0.18 4.19 4.19
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[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 -
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