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Aerosol optical depth retrieval over land using data from AGRI onboard FY-4A

  • role： First author第一作者
  • Affiliation：

    State Environment Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：xieyq@radi.ac.cn
  • Introduction：谢艳清，研究方向为大气定量遥感。E-mail： xieyq@radi.ac.cn
XIE Yanqing,  
  • Affiliation：

    State Environment Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LI Zhengqiang,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Environment Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：houwz@radi.ac.cn
  • Introduction：侯伟真，研究方向为气溶胶偏振遥感及最优化反演。E-mail： houwz@radi.ac.cn
HOU Weizhen*

Resümee

FY-4A, as the latest generation of China’s geostationary meteorological satellite, has been launched on December 11, 2016. The Advanced Geosynchronous Radiation Imager (AGRI) is the primary payload onboard FY-4A, and it can image China and its surrounding areas 205 times per day. The AGRI with high-frequency observation capabilities can provide sufficient data support for aerosol monitoring, but few aerosol products are developed using AGRI. The accuracy of the available FY-4A AOD datasets is also inferior to that of the Himawari-8 aerosol product, which can also cover China. In this study, an aerosol optical depth (AOD) retrieval algorithm based on the database of ratio of surface reflectance of different channels is proposed to develop high-accuracy FY-4A AOD dataset.This algorithm involves four steps: (1) detect and remove cloud pixels in FY-4A L1 data; (2) perform gas absorption correction on FY-4A L1 data using the reanalysis data released by European Center for Medium-Range Weather Forecasts; (3) select the sub-dark pixels for each month from FY-4A data after gas absorption correction, and perform atmospheric correction on these sub-dark pixels using the background AOD (i.e., AOD at 550 nm is 0.02) to obtain the surface reflectance of VIS06 and NIR08 channels, obtain the ratio of surface reflectance of these two channels, and perform the abovementioned operations for all the pixels in the study area to complete the construction of surface reflectance ratio database of VIS06 and NIR08 channels; (4) retrieve AOD using FY-4A L1 data after gas absorption correction based on the constructed surface reflectance ratio database.The algorithm has been applied to aerosol retrieval over Beijing–Tianjin–Hebei region from May 2019 to October 2019. Comparison of FY-4A AOD retrieval results with MODIS AOD dataset released by NASA shows that the two AOD datasets have a consistent spatial distribution trend. Validation result of MODIS AOD dataset, the official Himawari-8 AOD dataset released by the Japan Meteorological Agency, and FY-4A AOD dataset against ground-based AOD data provided by Aerosol Robotic Network shows that the accuracy of FY-4A AOD dataset is better than that of Himawari-8 AOD dataset and MODIS AOD data. The root mean square error, mean absolute error, correlation coefficient with ground-based data, and percentage of retrieval results with error within ±(0.05+0.15AODAERONET) of FY-4A AOD dataset are 0.12, 0.09, 0.91, and 65.86%, respectively.Although the signal-to-noise ratio of FY-4A/AGRI is lower than that of Himawari-8/AHI, the absolute and relative errors of FY-4A AOD dataset are better than those of Himawari-8 AOD dataset. The statistical parameters of FY-4A AOD dataset are also slightly better than those of MODIS AOD dataset, which is one of the widely used AOD datasets with high accuracy. Therefore, the FY-4A AOD dataset developed in this study has high accuracy.

Schlüsselwort

aerosol optical depth;remote sensing retrieval;FY-4A;advanced geosynchronous radiation imager;geostationary satellite

References

  1. 1.
    Burton S P, Ferrare R A, Hostetler C A, Hair J W, Rogers R R, Obland M D, Butler C F, Cook A L, Harper D B and Froyd K D. 2012. Aerosol classification using airborne High Spectral Resolution Lidar measurements - methodology and examples. Atmospheric Measurement Techniques, 5(1): 73-98
  2. 2.
    Fukuda S, Nakajima T, Takenaka H, Higurashi A, Kikuchi N, Nakajima T Y and Ishida H. 2013. New approaches to removing cloud shadows and evaluating the 380 nm surface reflectance for improved aerosol optical thickness retrievals from the GOSAT/TANSO-Cloud and Aerosol Imager. Journal of Geophysical Research, 118(24): 13520-13531
  3. 3.
    Giles D M, Sinyuk A, Sorokin M G, Schafer J S, Smirnov A, Slutsker I, Eck T F, Holben B N, Lewis J R, Campbell J R, Welton E J, Korkin S V and Lyapustin A I. 2019. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database - automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmospheric Measurement Techniques, 12(1): 169-209
  4. 4.
    Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, de Chiara G, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan R J, Hólm E, Janisková M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, de Rosnay P, Rozum I, Vamborg F, Villaume S and Thépaut J N. 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730): 1999-2049
  5. 5.
    Hsu N C, Tsay S C, King M D and Herman J R. 2006. Deep blue retrievals of asian aerosol properties during ACE-Asia. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3180-3195
  6. 6.
    Jiang T T, Chen B, Chan K K Y and Xu B. 2019. Himawari-8/AHI and MODIS aerosol optical depths in China: evaluation and comparison. Remote Sensing, 11(9): 1011
  7. 7.
    Knapp K R, Frouin R, Kondragunta S and Prados A. 2005. Toward aerosol optical depth retrievals over land from GOES visible radiances: determining surface reflectance. International Journal of Remote Sensing, 26(18): 4097-4116
  8. 8.
    Kaufman Y. J., D. Tanré, L. A. Remer, E. F. Vermote, A. Chu, and B. N. Holben. 1997. “Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer.” Journal of Geophysical Research: Atmospheres 102 (D14):17051-67.
  9. 9.
    Kolmonen P, Sogacheva L, Virtanen T H, de Leeuw G and Kulmala M. 2016. The ADV/ASV AATSR aerosol retrieval algorithm: current status and presentation of a full-mission AOD dataset. International Journal of Digital Earth, 9(6): 545-561
  10. 10.
    Levy R C, Mattoo S, Munchak L A, Remer L A, Sayer A M, Patadia F and Hsu N C. 2013. The Collection 6 MODIS aerosol products over land and ocean. Atmospheric Measurement Techniques, 6(11): 2989-3034
  11. 11.
    Li Y, Xue Y, Guang J, She L, Fan C and Chen G L. 2018. Ground-level PM2.5 concentration estimation from satellite data in the Beijing area using a specific particle swarm extinction mass conversion algorithm. Remote Sensing, 10(12): 1906
  12. 12.
    Liu X Y, Chen Q L, Che H Z, Zhang R J, Gui K, Zhang H and Zhao T L. 2016. Spatial distribution and temporal variation of aerosol optical depth in the Sichuan basin, China, the recent ten years. Atmospheric Environment, 147: 434-445
  13. 13.
    Ma X Y, Chen Z H, Su X, Yu H Y, Jia D D and Yao H M. 2020. GF-4 aerosol retrieval study of enhanced surface reflectance library support algorithm. Journal of Remote Sensing, 24(5): 578-595
  14. 14.
    Min M, Wu C Q, Li C, Liu H, Xu N, Wu X, Chen L, Wang F, Sun F L, Qin D Y, Wang X, Li B, Zheng Z J, Cao G Z and Dong L X. 2017. Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series. Journal of Meteorological Research, 31(4): 708-719
  15. 15.
    Mishra M K, Rathore P S, Misra A and Kumar R. 2020. Atmospheric correction of multispectral VNIR remote sensing data: algorithm and inter-sensor comparison of aerosol and surface reflectance products. Earth and Space Science, 7(9): e2019EA000710
  16. 16.
    Prados A I, Kondragunta S, Ciren P and Knapp K R. 2007. GOES Aerosol/Smoke Product (GASP) over North America: comparisons to AERONET and MODIS observations. Journal of Geophysical Research, 112(D15): D15201
  17. 17.
    Samset B H, Sand M, Smith C J, Bauer S E, Forster P M, Fuglestvedt J S, Osprey S and Schleussner C F. 2018. Climate impacts from a removal of anthropogenic aerosol emissions. Geophysical Research Letters, 45(2): 1020-1029
  18. 18.
    Sayer A M, Hsu N C, Bettenhausen C and Jeong M J. 2013. Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data. Journal of Geophysical Research, 118(14): 7864-7872
  19. 19.
    Vermote E, Tanré D, Deuzé J, Herman M, Morcrette J J and Kotchenova S Y. 2006. Second simulation of a satellite signal in the solar spectrum-vector (6SV).
  20. 20.
    Vermote E F, Tanre D, Deuze J L, Herman M and Morcrette J J. 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 675-686
  21. 21.
    Xie Y Q, Xue Y, Guang J, Mei L L, She L, Li Y, Che Y H and Fan C. 2020. Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: GOES-16, MSG-1, MSG-4, and Himawari-8. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 1538-1549
  22. 22.
    Xu M J, Bao Y S, Xu D, Lu Q F and Zhang X Y. 2020. Retrieval of aerosol optical depth based on FY-4A satellite data and its analysis and application. China Environmental Science, 40(12): 5162-5168
  23. 23.
    Xu Q Q, Chen X L, Yang S B, Tang L L and Dong J D. 2021. Spatiotemporal relationship between Himawari-8 hourly columnar aerosol optical depth (AOD) and ground-level PM2.5 mass concentration in mainland China. Science of the Total Environment, 765: 144241
  24. 24.
    Xue Y, Li Y, Guang J, Tugui A, She L, Qin K, Fan C, Che Y H, Xie Y Q, Wen Y N and Wang Z X. 2020. Hourly PM2.5 estimation over central and eastern China based on himawari-8 data. Remote Sensing, 12(5): 855
  25. 25.
    Yang F K, Wang Y, Tao J H, Wang Z F, Fan M, de Leeuw G and Chen L F. 2018. Preliminary investigation of a new ahi Aerosol Optical Depth (AOD) retrieval algorithm and evaluation with multiple source AOD measurements in China. Remote Sensing, 10(5): 748
  26. 26.
    Yang J, Zhang Z Q, Wei C Y, Lu F and Guo Q. 2017. Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bulletin of the American Meteorological Society, 98(8): 1637-1658
  27. 27.
    Yoshida M, Kikuchi M, Nagao T M, Murakami H, Nomaki T and Higurashi A. 2018. Common retrieval of aerosol properties for imaging satellite sensors. Journal of the Meteorological Society of Japan, 96B: 193-209
  28. 28.
    Zhang P, Guo Q, Chen B Y and Feng X. 2016. The Chinese next-generation geostationary meteorological satellite FY-4 compared with the Japanese Himawari-8/9 satellites. Advances in Meteorological Science and Technology, 6(1): 72-75
  29. 29.
    Zhang W H, Xu H and Zhang L L. 2019. Assessment of Himawari-8 AHI aerosol optical depth over land. Remote Sensing, 11(9): 1108
  30. 30.
    Zhu S Y, Li J M, Xiang J M and Wang S S. 2020. Research on aerosol optical depth inversion based on FY4A AGRI data. Geography and Geo-information Science, 36(4): 26-32

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