Please wait a minute...
img

官方微信

遥感技术与应用  2020, Vol. 35 Issue (4): 797-807    DOI: 10.11873/j.issn.1004-0323.2020.4.0797
甘肃遥感学会专栏     
基于Himawari-8的气溶胶反演研究
韦海宁1,2(),王维真1,3(),黄广辉1,徐菲楠1,2,冯姣姣1,2,董磊磊1,2
1.中国科学院西北生态环境资源研究院,中国科学院黑河遥感试验研究站,甘肃省遥感重点实验室,甘肃 兰州 730000
2.中国科学院大学,北京 100049
3.中国科学院寒旱区陆面过程与气候变化重点实验室,甘肃 兰州 730000
Retrieval of Aerosol Optical Depth based on Himawari-8
Haining Wei1,2(),Weizhen Wang1,3(),Guanghui Huang1,Feinan Xu1,2,Jiaojiao Feng1,2,Leilei Dong1,2
1.Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences, Lanzhou 730000, China
 全文: PDF(7642 KB)   HTML
摘要:

为获取中国区域高时空分辨率、高精度的气溶胶光学厚度(AOD)产品。基于Himawari-8卫星数据和MODIS地表反射率产品,反演了中国区域2018年4月~2019年4月逐10分钟的AOD,该方法可同时对暗像元、亮像元区域进行反演。依托全球气溶胶观测网(AERONET)中国境内的6个观测站数据,对反演结果进行一致性检验,同时将AOD反演结果与Himawari-8官方最新发布的AOD产品(020版)进行对比。结果表明:①红蓝比值法AOD反演结果与AERONET AOD之间相关性很高,除包头站外,其余5个站点的相关系数R在0.728~0.863之间,EE(误差期望)范围内样本点百分比在47.7%~68.6%之间,与Himawari-8 AOD产品相比有很大优势;②红蓝比值法AOD反演结果与AERONET AOD在时间序列走势上基本一致,但在AOD >1时,反演结果较AERONET AOD偏高。Himawari-8 AOD春夏季走势相对于AERONET AOD较为一致,但由于秋冬季Himawari-8 AOD有明显的日变化,且日变化较大,其走势与AERONET AOD偏离较大;③红蓝比值法AOD反演结果与MODIS AOD产品空间分布基本一致,AOD反演结果总体较MODIS AOD略为偏低。在冬季时红蓝比值法反演范围较MOD04_3K AOD的反演范围广;④红蓝比值法AOD在冬春季的北方高反射率地表区域的反演结果精度和反演范围较Himawari-8 AOD产品和MOD04_3K产品有很大优势。

关键词: 气溶胶光学厚度Himawari-8AERONETMODIS验证    
Abstract:

To obtain Aerosol Optical Depth (AOD) products with high spatial and temporal resolution and high precision in China. We enhance the AOD retrieval algorithm by applying the MODIS red and blue surface re?ectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOD over both dark and bright surfaces., we retrieve the 10-minute AOD of China from April 2018 to April 2019. The AOD retrievals from the enhanced red-blue ratio algorithm (RB AOD) were validated by the Level 1.5 AERONET (Aerosol Robotic Network) sunphotometer measurements and MOD04_3K AOD , and the retrieval of AOD were compared with the latest AOD product (version 020) released by Himawari-8.The results show that :(1) The AOD retrievals from the enhanced algorithm agreed well with those from the AERONET. Except Baotou station, the correlation coefficient R of the other five stations is between 0.728-0.863, and the percentage of sample points within the range of EE (Expectation of Error) is between 47.7% and 68.6%, which has great advantages over Himawari-8 AOD products.(2) The RB AOD are basically consistent with AERONET AOD in time series trend. the RB AOD results are higher than those of AERONET AOD when AOD > 1.The spring and summer trend of Himawari-8 AOD is relatively consistent with that of AERONET AOD. However, due to the obvious diurnal change of Himawari-8 AOD in autumn and winter, and the diurnal change is relatively large, its trend deviates greatly from AERONET AOD.(3) The spatial distribution of RB AOD is basically consistent with that of MODIS AOD products, and the retrieved results are slightly lower than those of MODIS AOD.In winter, the inversion range of red-blue ratio method is wider than that of MOD04_3K AOD.(4) the red-blue ratio retrieval algorithm has great advantages over Himawari-8 AOD and MOD04_3K in precision and range of retrieval results of high-reflectance surface area in north China in winter and spring.

Key words: Aerosol Optical Depth(AOD)    Himawari-8    AERONET    MODIS    Evaluation
收稿日期: 2019-09-23 出版日期: 2020-09-15
ZTFLH:  TP79  
基金资助: 中国科学院A类战略性先导科技专项(XDA19040500);国家自然科学基金项目(41671373);中国科学院寒旱区陆面过程与气候变化重点实验室自主研究课题(LPCC2019)
通讯作者: 王维真     E-mail: hainingwei@lzb.ac.cn;weizhen@lzb.ac.cn
作者简介: 韦海宁(1994-),男,广西崇左人,硕士研究生,主要从事大气遥感研究。E?mail:hainingwei@lzb.ac.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
韦海宁
王维真
黄广辉
徐菲楠
冯姣姣
董磊磊

引用本文:

韦海宁,王维真,黄广辉,徐菲楠,冯姣姣,董磊磊. 基于Himawari-8的气溶胶反演研究[J]. 遥感技术与应用, 2020, 35(4): 797-807.

Haining Wei,Weizhen Wang,Guanghui Huang,Feinan Xu,Jiaojiao Feng,Leilei Dong. Retrieval of Aerosol Optical Depth based on Himawari-8. Remote Sensing Technology and Application, 2020, 35(4): 797-807.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.4.0797        http://www.rsta.ac.cn/CN/Y2020/V35/I4/797

站点海拔/m经度/°E纬度/°N数据时间范围
北京106116.31739.933201804~201904
香港40114.11722.483201804~201904
台北50120.21723.000201804~201904
徐州59117.14234.217201804~201904
香河36116.96239.754201804~201904
包头1275109.62940.852201804~201904
表1  AERONET站点信息
图1  红蓝比值法流程图(ρ1和ρ3分别是Himawari-8蓝(0.46μm)、红(0.64μm)波段地表反射率, R为MOD09A1红(0.64μm)蓝(0.46μm)波段地表反射率比值库)
图2  RB AOD和H8 AOD与AERONET AOD精度对比(黑色虚线为误差线EE=±0.05+(0.15×AERONET),红色虚线为拟合曲线)
图3  AERONET AOD与RB AOD和Himawari-8 AOD时间序列对比
图4  RB AOD反演结果和MODIS AOD产品空间分布对比
站点季节NRRRMSERMSE
(RB)(H8)(RB)(H8)
北京春季21150.850.80.230.46
夏季6000.940.90.230.16
秋季13090.920.790.190.37
冬季9510.770.320.180.44
香港春季850.730.610.460.50
夏季320.820.740.120.14
秋季270.780.670.070.19
冬季70.950.950.120.20
台北春季1140.680.210.210.45
夏季150.390.040.140.38
秋季670.670.40.090.20
冬季280.80.740.140.16
徐州春季6230.810.760.250.29
夏季1660.90.90.230.23
秋季420.90.930.160.28
冬季3480.750.690.150.23
香河春季10090.810.720.230.51
夏季1660.960.870.20.19
秋季3950.930.820.230.27
冬季1780.940.60.10.36
包头春季4200.13-0.060.781.37
夏季2420.80.140.131.49
秋季1450.7400.140.87
冬季13-0.41-0.360.131.36
表2  AERONET站点季节检验统计参数
图5  亮地表区域MOD09A1的红、蓝地表反射率和红/蓝比值时间序列对比
图6  Himawari-8 和MODIS 红蓝波段光谱响应函数曲线
1 Zhang Xiaoye, Liao Hong, Wang Fenjuan. The Effects of Aerosols and Clouds on Climate Change and Their Responses[J]. Climate Change Research, 2014,(1): 37-39.
1 张小曳, 廖宏, 王芬娟. 对IPCC第五次评估报告气溶胶-云对气候变化影响与响应结论的解读[J].气候变化研究进展, 2014, (1): 37-39.].
2 Kaufman Y, Wald A, Remer L, et al. Remote Sensing of Aerosol over the Continents with the Aid of a 2.2 m Channel[J]. IEEE Trans. Geosci. Remote Sens,1997,35:1286-1298.
3 Kaufman Y J, Tanre D, Remer L A, et al. Operational Remote Sensing of Tropospheric Aerosol over Land from EOS Moderate Resolution Imaging Spectroradiometer[J]. Journal of Geophysical Research-Atmospheres, 1997, 102(D14): 17051-17067.
4 Levy R, Hsu C. Modis Atmosphere L2 Aerosol Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center[EB/OL]. .
5 Wang Hongbin, Zhang Lei, Jiao Shengming, et al. Evaluation of the MODIS Aerosol Products and Analysis of the Retrieval Errors in China[J].Plateau Meteorology, 2016, 35(3): 810-822.
5 王宏斌, 张镭, 焦圣明, 等. 中国地区MODIS气溶胶产品的验证及反演误差分析[J]. 高原气象, 2016, 35(3): 810-822.
6 Huang G, Huang C, Li Z, et al. Development and Validation of a Robust Algorithm for Retrieving Aerosol Optical Depth over Land from MODIS Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, 2015, 8(3): 1152-1166.
7 Levy R C, Remer L A, Mattoo S, et al. Second-Generation Operational Algorithm: Retrieval of Aerosol Properties over Land from Inversion of Moderate Resolution Imaging Spectroradiometer Spectral Reflectance[J]. Journal of Geophysical Research-Atmospheres, 2007, 112(D13). doi:10.1029/2006 JD007815.
doi: 10.1029/2006 JD007815
8 Hsu N C, Jeong M-J, Bettenhausen C, et al. Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation[J]. Journal of Geophysical Research: Atmospheres, 2013, 118(16): 9296-9315.
9 Hsu N C, Tsay S-C, King M D, et al. Aerosol Properties over Bright-Reflecting Source Regions[J],2004,42(3): 557-569.
10 Hsu N C, Tsay S-C, King M D, et al. Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia[J]. IEEE Transactions on Geoscience Remote Sensing, 2006, 44(11): 3180-3195.
11 Bai L, Xue Y, Cao C, et al. Quantitative Retrieval of Aerosol optical thickness from FY-2 VISSR data[C]∥ Sixth International Symposium on Digital Earth: Models, Algorithms, and Virtual Reality, 2010: 784022.
12 Zhang Junhua, Si Zhaojun, Mao Jietai, et al. Remote Sensing Aerosol Optical Depth over China with GMS -5 Satellite[J].Chinese Journal of Atmospheric Sciences,2003,27(1):23-35.
12 张军华, 斯召俊, 毛节泰, 等. GMS卫星遥感中国地区气溶胶光学厚度[J]. 大气科学, 2003, 27(1): 23-35.
13 Gao Ling, Ren Tong, Li Chengcai, et al.A Retrieval of the Atmospheric Aerosol Optical Depth from MTSAT[J] .Acta Meteorologica Sinica, 2012, 70(3): 598-608.
13 高玲, 任通, 李成才, 等. 利用静止卫星MTSAT反演大气气溶胶光学厚度[J]. 气象学报, 2012, 70(3): 598-608.
14 Yu F F, Wu X Q. Radiometric Inter-calibration between Himawari-8 AHI and S-NPP VIIRS for the Solar Reflective Bands[J]. Remote Sensing, 2016, 8(3): 165.doi:10.3390/ S8030165
doi: 10.3390/ S8030165
15 Yoshida M, Kikuchi M, Nagao T M, et al. Common Retrieval of Aerosol Properties for Imaging Satellite Sensors[J]. Journal of the Meteorological Society of Japan. Ser. II, 2018,96(B):193-209.
16 Holben B N, Eck T F, Slutsker I, et al. AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization[J]. Remote Sensing of Environment, 1998, 66(1): 1-16.
17 Eck T F, Holben B, Reid J, et al. Wavelength Dependence of the Optical Depth of Biomass Burning, Urban, and Desert Dust Aerosols[J]. Journal of Geophysical Research: Atmospheres, 1999, 104(D24): 31333-31349.
18 Bessho K, Date K, Hayashi M, et al. An Introduction to Himawari-8/9-Japan's New-generation Geostationary Meteorological Satellites[J]. Journal of the Meteorological Society of Japan, 2016, 94(2): 151-183.
19 Li S S, Chen L F, Tao J H, et al. Retrieval of Aerosol Optical Depth over Bright Targets in the Urban Areas of North China during Winter[J]. Science China: Earth Science, 2012, 42(8): 1253-1263.
19 李莘莘, 陈良富, 陶金花, 等. 城市与冬季北方亮目标地区气溶胶光学厚度反演[J]. 中国科学:地球科学, 2012, 42(8): 1253-1263.
20 Tian Xinpeng, Sun Lin, Liu Qiang, et al. Retrieval of High-Resolution Aerosol Optical Depth Using Landsat 8 OLI Data over Beijing[J]. Journal of Remote Sensing, 2018, 22(1): 51-63.
20 田信鹏, 孙林, 刘强, 等. 北京地区Landsat 8 OLI高空间分辨率气溶胶光学厚度反演[J]. 遥感学报, 2018, 22(1): 51-63.
21 Zhang H, Kondragunta S, Laszlo I, et al. An Enhanced VIIRS Aerosol Optical Thickness (AOT) Retrieval Algorithm over Land Using a Global Surface Reflectance Ratio Database[J]. Journal of Geophysical Research: Atmospheres, 2016, 121(18): 10717-10738.
[1] 魏石梅, 潘竟虎, 妥文亮. 2015年中国PM2.5浓度遥感估算与时空分布特征[J]. 遥感技术与应用, 2020, 35(4): 845-854.
[2] 李超,李雪梅,田亚林,任瑞. 温度植被干旱指数时空融合模型对比[J]. 遥感技术与应用, 2020, 35(4): 832-844.
[3] 董超,赵庚星. 时序数据集构建质量对土地覆盖分类精度的影响研究[J]. 遥感技术与应用, 2020, 35(3): 558-566.
[4] 龙爽,郭正飞,徐粒,周华真,方伟华,许映军. 基于Google Earth Engine的中国植被覆盖度时空变化特征分析[J]. 遥感技术与应用, 2020, 35(2): 326-334.
[5] 徐玉雯,张浩,陈正超,景海涛. 基于深蓝算法的Sentinel-2数据气溶胶光学厚度反演[J]. 遥感技术与应用, 2020, 35(2): 372-380.
[6] 郑贵洲,熊良超,廖艳雯,王红平. 利用MODIS数据反演南海南部海表温度及时空变化分析[J]. 遥感技术与应用, 2020, 35(1): 132-140.
[7] 赵春亮,许文波,范锦龙. FY-3C中分辨率成像光谱仪数据的窄波段地表反照率验证研究[J]. 遥感技术与应用, 2020, 35(1): 153-162.
[8] 李志鹏,陈健. 基于GOCI卫星的大气细颗粒物PM2.5的遥感反演及其时空分布规律研究[J]. 遥感技术与应用, 2020, 35(1): 163-173.
[9] 王一帆,徐涵秋. 利用MODIS EVI时间序列数据分析福建省植被变化(2000~2017年)[J]. 遥感技术与应用, 2020, 35(1): 245-254.
[10] 徐卫星,薛华柱,靳华安,李爱农. 融合遥感先验信息的叶面积指数反演[J]. 遥感技术与应用, 2019, 34(6): 1235-1244.
[11] 韦海宁,王维真,徐菲楠,冯姣姣. Himawari-8气溶胶产品的验证及应用[J]. 遥感技术与应用, 2019, 34(5): 1005-1015.
[12] 冯艾琳,武晋雯,孟莹,姜鹏,董巍,张璇,方缘,刘斌. 基于MODIS GPP数据产品的辽宁省碳源/汇空间格局分布研究[J]. 遥感技术与应用, 2019, 34(4): 857-864.
[13] 冯婵莹, 汪子豪, 郑成洋. 基于分层线性回归的MODIS反照率产品降尺度方法研究[J]. 遥感技术与应用, 2019, 34(3): 602-611.
[14] 王莹莹, 袁金国, 张莹, 吴朝阳. 中国温带地区植被物候期时空变化特征及对总初级生产力的影响[J]. 遥感技术与应用, 2019, 34(2): 377-388.
[15] 尹思阳, 吴文瑾, 李新武. 基于遥感和气象数据的东南亚森林动态变化分析[J]. 遥感技术与应用, 2019, 34(1): 166-175.