浏览全部资源
扫码关注微信
纸质出版日期： 2022-05-07 ，
扫 描 看 全 文
谢艳清，李正强，侯伟真.2022.FY-4A AGRI陆地气溶胶光学厚度反演.遥感学报，26（5）： 913-922
Xie Y Q， Li Z Q and Hou W Z. 2022. Aerosol optical depth retrieval over land using data from AGRI onboard FY-4A. National Remote Sensing Bulletin， 26（5）：913-922
风云四号A星（FY-4A）是中国第二代静止气象卫星的首颗星，多通道扫描成像辐射计AGRI（Advanced Geosynchronous Radiation Imager）是搭载在FY-4A上的主要光学载荷之一。AGRI具有高频率观测特点（每天观测205次），在大气气溶胶的遥感高频监测方面具有良好应用潜力，但目前官方还未发布相应的气溶胶数据集。本文旨在针对AGRI数据的特点开发基于地表反射率比值库的反演算法以生产高精度的AGRI气溶胶数据集。本文首先基于再分析数据对去云后的AGRI L1级数据进行气体吸收订正；然后利用背景气溶胶光学厚度AOD（Aerosol Optical Depth）对一个月内的“次暗像元”进行大气校正以获取AGRI 0.65 μm 和 0.83 μm 通道的地表反射率，进而获取这两个通道的地表反射率的比值，完成每个月的地表反射率比值库的构建；最后便可以基于已构建的地表反射率比值库实现地气解耦，完成气溶胶的遥感反演。该算法已被应用于2019年5—10月京津冀地区的气溶胶反演，AGRI AOD反演结果与美国国家航天局发布的MODIS（Moderate-resolution Imaging Spectroradiometer）AOD数据集、日本气象厅发布的AHI（Advanced Himawari Imager）AOD数据集的对比结果显示它们具有基本一致的空间分布趋势。利用AERONET（Aerosol Robotic Network）数据验证的结果显示AGRI AOD数据集具有较高的精度，且其精度要优于AHI AOD数据集和MODIS AOD数据集。AGRI AOD数据集的平均绝对误差，均方根误差，与地基数据的相关系数和误差落在期望误差
<math id="M1"><mo>±</mo><mo stretchy="false">(</mo><mn mathvariant="normal">0.05</mn><mo>+</mo><mn mathvariant="normal">0.15</mn><mi mathvariant="normal">A</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">D</mi><mmultiscripts><mrow><mo stretchy="false">)</mo></mrow><mprescripts/><mrow><mi mathvariant="normal">A</mi><mi mathvariant="normal">E</mi><mi mathvariant="normal">R</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">N</mi><mi mathvariant="normal">E</mi><mi mathvariant="normal">T</mi></mrow><mrow/></mmultiscripts></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=28624994&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=28624993&type=
33.78200150
3.89466691
范围内的反演结果所占的比例分别是0.09%，0.12%，0.91%和65.86%。上述验证结果表明基于地表反射率比值库的方法反演AGRI AOD具有可行性，且反演结果具有较高的精度。
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+0.15AOD
AERONET
) 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.
气溶胶光学厚度遥感反演风云四号多通道扫描成像辐射计静止卫星
aerosol optical depthremote sensing retrievalFY-4Aadvanced geosynchronous radiation imagergeostationary satellite
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 [DOI: 10.5194/amt-5-73-2012http://dx.doi.org/10.5194/amt-5-73-2012]
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 [DOI: 10.1002/2013jd020090http://dx.doi.org/10.1002/2013jd020090]
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 [DOI: 10.5194/amt-12-169-2019http://dx.doi.org/10.5194/amt-12-169-2019]
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 [DOI: 10.1002/qj.3803http://dx.doi.org/10.1002/qj.3803]
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 [DOI: 10.1109/TGRS.2006.879540http://dx.doi.org/10.1109/TGRS.2006.879540]
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 [DOI: 10.3390/rs11091011http://dx.doi.org/10.3390/rs11091011]
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 [DOI: 10.1080/01431160500099329http://dx.doi.org/10.1080/01431160500099329]
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. [DOI: https://doi.org/10.1029/96JD03988https://doi.org/10.1029/96JD03988]
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 [DOI: 10.1080/17538947.2015.1111450http://dx.doi.org/10.1080/17538947.2015.1111450]
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 [DOI: 10.5194/amt-6-2989-2013http://dx.doi.org/10.5194/amt-6-2989-2013]
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 [DOI: 10.3390/rs10121906http://dx.doi.org/10.3390/rs10121906]
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 [DOI: 10.1016/j.atmosenv.2016.10.008http://dx.doi.org/10.1016/j.atmosenv.2016.10.008]
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
马小雨, 陈正华, 宿鑫, 于会泳, 贾丹丹, 姚焕玫. 2020. GF-4增强型地表反射率库支持法的气溶胶光学厚度反演. 遥感学报, 24(5): 578-595 [DOI: 10.11834/jrs.20208341http://dx.doi.org/10.11834/jrs.20208341]
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 [DOI: 10.1007/s13351-017-6161-zhttp://dx.doi.org/10.1007/s13351-017-6161-z]
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 [DOI: 10.1029/2019ea000710http://dx.doi.org/10.1029/2019ea000710]
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 [DOI: 10.1029/2006jd007968http://dx.doi.org/10.1029/2006jd007968]
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 [DOI: 10.1002/2017gl076079http://dx.doi.org/10.1002/2017gl076079]
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 [DOI: 10.1002/jgrd.50600http://dx.doi.org/10.1002/jgrd.50600]
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).
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 [DOI: 10.1109/36.581987http://dx.doi.org/10.1109/36.581987]
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 [DOI: 10.1109/tgrs.2019.2944949http://dx.doi.org/10.1109/tgrs.2019.2944949]
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
许梦婕, 鲍艳松, 许丹, 陆其峰, 张兴赢. 2020. 基于FY-4A卫星数据反演气溶胶光学厚度及分析应用. 中国环境科学, 40(12): 5162-5168
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 [DOI: 10.1016/j.scitotenv.2020.144241http://dx.doi.org/10.1016/j.scitotenv.2020.144241]
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 [DOI: 10.3390/rs12050855http://dx.doi.org/10.3390/rs12050855]
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 [DOI: 10.3390/rs10050748http://dx.doi.org/10.3390/rs10050748]
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 [DOI: 10.1175/bams-d-16-0065.1http://dx.doi.org/10.1175/bams-d-16-0065.1]
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 [DOI: 10.2151/jmsj.2018-039http://dx.doi.org/10.2151/jmsj.2018-039]
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
张鹏, 郭强, 陈博洋, 冯绚. 2016. 我国风云四号气象卫星与日本Himawari-8/9卫星比较分析. 气象科技进展, 6(1): 72-75
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 [DOI: 10.3390/rs11091108http://dx.doi.org/10.3390/rs11091108]
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
祝善友, 李佳敏, 向嘉敏, 王诗圣. 2020. 基于FY4A AGRI数据的气溶胶光学厚度反演研究. 地理与地理信息科学, 36(4): 26-32
相关作者
相关机构
京公网安备11010802024621