Yang Zhongdong, Liu Jian. A review of visible infrared imaging radiometer on meteorological satellite. J Appl Meteor Sci, 2016, 27(5): 592-603. DOI:  10.11898/1001-7313.20160508.
Citation: Yang Zhongdong, Liu Jian. A review of visible infrared imaging radiometer on meteorological satellite. J Appl Meteor Sci, 2016, 27(5): 592-603. DOI:  10.11898/1001-7313.20160508.

A Review of Visible Infrared Imaging Radiometer on Meteorological Satellite

DOI: 10.11898/1001-7313.20160508
  • Received Date: 2016-06-27
  • Rev Recd Date: 2016-07-15
  • Publish Date: 2016-09-30
  • The development of visible infrared imaging radiometer that payload on environmental and meteorological satellites for 50 years are reviewed. 12 kinds of instruments are selected as typical representatives from nearly 100 sets of instruments run in orbit at different period. An analysis is done combined instrument functional performance specifications with application requirement. The analysis can be done from the basic strands of historical development, trend of main operational in the future and the direction of innovation and development. The development process can be divided into three stages. The first stage is the early exploration period. It is the first generation of remote sensing instrument on meteorological satellite that createds a precedent for earth observation. The second stage is the initial application period, it basically forms a stable preliminary application situation for three decades. At the same time, Europe and China begin to develop their own environmental meteorological optical remote sensing instruments. The third one is development and stable application stage. It appears a new generation visible infrared optical imaging radiometer. These instruments have some common characteristics, such as more than 20 spectrum channels with narrow bandwidth spectrum. The spectral range covers 0.4-15 μm and radiometricis accuracy. Their spatial resolution is between 200 and 1000 meters in general. Advanced instruments represent trends of visible infrared imaging radiometeron polar orbit meteorological satellite in the future. The visible infrared optical imaging radiometers on geostationary orbit meteorological satellite are characterized by about 15 typical spectral channels with narrow spectral bandwidth and the coverage of spectral range from 0.4 μm to 15 μm. The radiometric is also very high. The spatial resolution is between 500 and 2000 meters. The disk image forming speed can reach minute level and the regional area scanning can be faster.
  • Table  1  The spectral and radiometric characterization of AVHRR/3

    中心波长/μm 光谱区间/μm SNR/NEdT
    0.630 0.580~0.68 9 @ 0.5%反照率
    0.862 0.725~1.00 9 @ 0.5%反照率
    1.610 1.58~1.64 20 @ 0.5%反照率
    3.740 3.55~3.93 0.12 K @ 300 K
    10.80 10.3~11.3 0.12 K @ 300 K
    12.00 11.5~12.5 0.12 K @ 300 K
    DownLoad: Download CSV

    Table  2  The spectral and radiometric characterization of IMAGER

    中心波长/μm 光谱区间/μm SNR/NEdT
    0.65 0.55~0.75 250 @ 100%反照率
    3.90 3.80~4.00 0.11 K @ 300 K
    6.55 5.80~7.30 0.14 K @ 300 K
    10.70 10.2~11.2 0.09 K @ 300 K
    13.35 13.0~13.7 0.70 K @ 300 K
    DownLoad: Download CSV

    Table  3  The spectral and radiometric characterization of MODIS (from reference [56])

    主要用途 波段 波段范围* 光谱辐射/
    (W·m-2·sr-1·μm-1)
    SNR/NEdT/K
    陆地/云/边界层气溶胶 1 620~670 21.8 128
    2 841~876 24.7 201
    陆地/云/气溶胶特性 3 459~479 35.3 243
    4 545~565 29.0 228
    5 1230~1250 5.4 74
    6 1628~1652 7.3 275
    7 2105~2155 1.0 110
    海洋水色/浮游生物/
    生物地球化学
    8 405~420 44.9 880
    9 438~448 41.9 838
    10 483~493 32.1 802
    11 526~536 27.9 754
    12 546~556 21.0 750
    13 662~672 9.5 910
    14 673~683 8.7 1087
    15 743~753 10.2 586
    16 862~877 6.2 516
    大气水汽 17 890~920 10.0 167
    18 931~941 3.6 57
    19 915~965 15.0 250
    地表/云温度 20 3.660~3.840 0.45(300 K) 0.05
    21 3.929~3.989 2.38(335 K) 2.00
    22 3.929~3.989 0.67(300 K) 0.07
    23 4.020~4.080 0.79(300 K) 0.07
    大气温度 24 4.433~4.498 0.17(250 K) 0.25
    25 4.482~4.549 0.59(275 K) 0.25
    卷云水汽 26 1.360~1.390 6.00 150(SNR)
    27 6.535~6.895 1.16(240 K) 0.25
    28 7.175~7.475 2.18(250 K) 0.25
    云特征 29 8.400~8.700 9.58(300 K) 0.05
    臭氧 30 9.580~9.880 3.69(250 K) 0.25
    地表/云温度 31 10.780~11.280 9.55(300 K) 0.05
    32 11.770~12.270 8.94(300 K) 0.05
    云顶高度 33 13.185~13.485 4.52(260 K) 0.25
    34 13.485~13.785 3.76(250 K) 0.25
    35 13.785~14.085 3.11(240 K) 0.25
    36 14.085~14.385 2.08(220 K) 0.35
      注:*波段1~19光谱单位:nm; 波段20~36光谱单位:μm。
    DownLoad: Download CSV

    Table  4  The main characterizations and usages of 3MI (from reference [64])

    3MI 中心波
    长/μm
    光谱带
    宽/nm
    SNR@xx.x/
    (W·m-2·sr-1·μm-1)
    极化 主要用途
    可见光近红外谱段 0.410 20 100 @ 35.4 Y 吸收气溶胶,火山灰云
    0.443 20 100 @ 48.9 Y 吸收气溶胶
    0.490 20 100 @ 55.6 Y 气溶胶,地表反照率,云反射率,光学厚度
    0.555 20 100 @ 55.2 Y 地表反照率
    0.670 20 100 @ 44.1 Y 气溶胶光学特性
    0.754 20 200 @ 36.6 N 云,气溶胶高度
    0.763 10 200 @ 36.1 N 云,气溶胶高度
    0.865 40 100 @ 28.2 Y 植被,气溶胶,云,地表特征
    0.910 20 200 @ 25.2 N 水汽,大气校正
    短波红外谱段 0.910 20 200 @ 25.2 N 水汽,大气校正
    1.370 40 100 @ 10.7 Y 卷云,水汽图像
    1.650 40 100 @ 6.8 Y 气溶胶反演中的地表特性
    2.130 40 100 @ 2.9 Y 气溶胶反演中的地表特性,云微物理
    DownLoad: Download CSV
  • [1]
    Wikipedia.Television Infrared Observation Satellite.Wikipedia, 2016.
    [2]
    WMO.WMO OSCAR.List of all Instruments.2015.
    [3]
    Rao P K, Holmes S J, Anderson R K, 等编. 许健民, 方宗义, 徐建平, 等译. 气象卫星——系统、资料及其在环境中的应用. 北京: 气象出版社, 1994.
    [4]
    Schmidt M, King E A, McVicar T R.A method for operational calibration of AVHRR reflective time series data.Remote Sens Environ, 2008, 112(3):1117-1129. doi:  10.1016/j.rse.2007.07.015
    [5]
    Trishchenko A P, Li Z.A method for the correction of AVHRR onboard IR calibration in the event of short-term radiative contamination.Int J Remote Sens, 2001, 22(17):3619-3624. doi:  10.1080/01431160152609362
    [6]
    Mittaz J, Harris A.A physical method for the calibration of the AVHRR/3 thermal IR channels.Part Ⅱ:An in-orbit comparison of the AVHRR longwave thermal IR channels on board metop-A with IASI.J Atmos Ocean Technol, 2011, 28(9):1072-1087. doi:  10.1175/2011JTECHA1517.1
    [7]
    Raja M, Wu X Q, Yu F F.Extended Inter-comparison of collocated MetOp-A AVHRR-IASI brightness temperature data and its implication for AVHRR calibration.Atmospheric and Environmental Remote Sensing Data Processing and Utilization Ⅵ:Readiness for GEOSS Ⅳ, 2010, 781107, doi: 10.1117/12.861265.
    [8]
    Rossow W B, Garder L C.Validation of ISCCP cloud detections.J Climate, 1993, 6(12):2370-2393. doi:  10.1175/1520-0442(1993)006<2370:VOICD>2.0.CO;2
    [9]
    Rossow W B, Schiffer R A.ISCCP cloud data products.Bull Am Meteor Soc, 1991, 72(1):2-20. doi:  10.1175/1520-0477(1991)072<0002:ICDP>2.0.CO;2
    [10]
    Seze G, Rossow W B.Time-cumulated visible and infrared radiance histograms used as descriptors of surface and cloud variations.Int J Remote Sens, 1991, 12(5):877-920. doi:  10.1080/01431169108929702
    [11]
    Rossow W B.Measuring cloud properties from space:A review.J Climate, 1989, 2(3):201-213. doi:  10.1175/1520-0442(1989)002<0201:MCPFSA>2.0.CO;2
    [12]
    Rossow W, Mosher F.ISCCP cloud algorithm intercomparison.J Climate Appl Meteor, 1985, 24(9):877-903. doi:  10.1175/1520-0450(1985)024<0887:ICAI>2.0.CO;2
    [13]
    Saunders R W, Kriebel K T.An improved method for detecting clear sky and cloudy radiances from AVHRR data.Int J Remote Sens, 1988, 9(1):123-150. doi:  10.1080/01431168808954841
    [14]
    Stowe L L, Davis P, McClain E P.Evaluating the CLAVR (clouds from AVHRR) phase I-cloud cover experimental product.Adv Space Res, 1995, 16(10):21-24. doi:  10.1016/0273-1177(95)00374-N
    [15]
    Stowe L L, Vemury S K, Rao A V.AVHRR clear sky radiation data sets at NOAA/NESDIS.Adv Space Res, 1994, 14(1):113-116. doi:  10.1016/0273-1177(94)90358-1
    [16]
    Stowe L L, Davis P A, Mcclain E P.Scientific basis and initial evaluation of the CLAVR-1 global clear/cloud classification algorithm for the advanced very high resolution radiometer.J Atmos Ocean Technol, 1999, 16(6):656-681. doi:  10.1175/1520-0426(1999)016<0656:SBAIEO>2.0.CO;2
    [17]
    Heidinger A K.CLAVR-x Cloud Mask Algorithm Theoretical Basis Document (ATBD), NOAA/NESDIS/Office of Research and Applications, Washington, DC, USA, 2004.
    [18]
    Vemury S, Stowe L L, Anne V R.AVHRR pixel level clear-sky classification using dynamic threshold (CLAVR-3).J Atmos Ocean Technol, 2001, 18(2):169-186. doi:  10.1175/1520-0426(2001)018<0169:APLCSC>2.0.CO;2
    [19]
    Gao B C, Goetz A F H, Wiscombe W J.Cirrus cloud detection from airborne imaging spectrometer data using the 1.38 micron water vapor band.Geophys Res Lett, 1993, 20(4):301-304. doi:  10.1029/93GL00106
    [20]
    Baum B A, Wielicki B A.Cirrus cloud retrieval using infrared sounding data:Multilevel cloud errors.J Appl Meteorol, 1994, 33(1):107-117. doi:  10.1175/1520-0450(1994)033<0107:CCRUIS>2.0.CO;2
    [21]
    Nakajima T, King M D.Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements.Part Ⅰ:Theory.Journal of the Atmospheric Sciences, 1990, 47(15):1878-1893. doi:  10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2
    [22]
    Ou S C, Liou K N, Takano Y, et al.Remote sounding of cirrus cloud optical depths and ice crystal sizes from AVHRR data:Verification using FIRE Ⅱ IFO measurements.J Atmos Sci, 1995, 52(23):4143-4158. doi:  10.1175/1520-0469(1995)052<4143:RSOCCO>2.0.CO;2
    [23]
    Nakajima T Y, Nakajma T.Wide-area determination of cloud microphysical properties from NOAA AVHRR measurements for FIRE and ASTEX Regions.J Atmos Sci, 1995, 52(23):4043-4059. doi:  10.1175/1520-0469(1995)052<4043:WADOCM>2.0.CO;2
    [24]
    Ackerman S A, Moeller C C, Strabala K I, et al.Retrieval of effective microphysical properties of clouds:A wave cloud case study.Geophys Res Lett, 1998, 25(8):1121. doi:  10.1029/98GL00042
    [25]
    Baum B A, Yang P, Nasiri S, et al.Bulk scattering properties for the remote sensing of ice clouds.Part Ⅲ:High-resolution spectral models from 100 to 3250 cm-1.J Appl Meteorol Climatol, 2007, 46(4):423-434. doi:  10.1175/JAM2473.1
    [26]
    Baum B A, Yang P, Heymsfield A J, et al.Bulk scattering properties for the remote sensing of ice clouds.Part Ⅱ:Narrowband models.J Appl Meteorol, 2005, 44(12):1896-1911. doi:  10.1175/JAM2309.1
    [27]
    Baum B A, Heymsfield A J, Yang P, et al.Bulk scattering properties for the remote sensing of ice clouds.Part Ⅰ:Microphysical data and models.J Appl Meteorol, 2005, 44(12):1885-1895. doi:  10.1175/JAM2308.1
    [28]
    Ou S C, Liou K N, Caudill T R.Remote sounding of multilayer cirrus cloud systems using AVHRR data collected during FIRE-Ⅱ-IFO.J Appl Meteorol, 1998, 37(3):241-254. doi:  10.1175/1520-0450-37.3.241
    [29]
    Kawamoto K, Nakajima T.A global determination of cloud microphysics with AVHRR remote sensing.J Climate, 2001, 14(9):2054-2068. doi:  10.1175/1520-0442(2001)014<2054:AGDOCM>2.0.CO;2
    [30]
    赵凤生, 丁强, 孙同明, 等.利用NOAA-AVHRR观测数据反演云辐射特性的一种迭代方法.气象学报, 2002, 60(5):594-601. doi:  10.11676/qxxb2002.070
    [31]
    Liu Jian, Dong Chaohua.Using satellite data to analyze properties of cloud particles size on the top of cloud.J Infrared Millim Waves, 2002, 21(2):4-8. http://www.oalib.com/paper/1597681
    [32]
    Liu Jian, Zhu Yuanjing, Zhao Bolin, et al.Appl ication study on detecting multilayer cloud's properties by satellite data.J Infrared Millim Waves, 2004, 23(6):408-412. https://www.researchgate.net/publication/287026472_Application_study_on_detecting_multilayer_cloud's_properties_by_satellite_data
    [33]
    刘健.FY-2云检测中动态阈值提取技术改进方法研究.红外与毫米波学报, 2010, 29(4):288-292. http://www.cnki.com.cn/Article/CJFDTOTAL-HWYH201004012.htm
    [34]
    傅云飞.利用卫星双光谱反射率算法反演的云参数及其应用.气象学报, 2014, 72(5):1039-1053. doi:  10.11676/qxxb2014.087
    [35]
    McClain E P, Pichel W G, Walton C C, et al.Multi-channel improvements to satellite-derived global sea surface temperatures.Adv Space Res, 1982, 2(6):43-47. http://citeseerx.ist.psu.edu/showciting?cid=1905732
    [36]
    McClain E P, Pichel W G, Walton C C.Comparative performance of AVHRR-based multichannel sea surface temperatures.J Geophys Res Ocean, 1985, 90(C6):11587-11601. doi:  10.1029/JC090iC06p11587
    [37]
    Walton C C.Nonlinear multichannel algorithms for estimating sea surface temperature with AVHRR satellite sata.J Appl Meteorol, 1988, 27(2):115-124. doi:  10.1175/1520-0450(1988)027<0115:NMAFES>2.0.CO;2
    [38]
    Walton C C, Pichel W G, Sapper J F, et al.The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites.J Geophys Res, 1998, 103(C12):27999. doi:  10.1029/98JC02370
    [39]
    Kilpatrick K A, Podestá G P, Evans R.Overview of the NOAA/NASA advanced very high resolution radiometer pathfinder algorithm for sea surface temperature and associated matchup database.J Geophys Res, 2001, 106(C5):9179. doi:  10.1029/1999JC000065
    [40]
    Geogdzhayev I V, Mishchenko M I, Rossow W B, et al.Global two-channel AVHRR retrievals of aerosol properties over the ocean for the period of NOAA-9 observations and preliminary retrievals using NOAA-7 and NOAA-11 data.J Atmos Sci, 2002, 59(3):262-278. doi:  10.1175/1520-0469(2002)059<0262:GTCARO>2.0.CO;2
    [41]
    Mishchenko M I, Geogdzhayev I V, Liu L, et al.Aerosol retrievals from AVHRR radiances:Effects of particle nonsphericity and absorption and an updated long-term global climatology of aerosol properties.J Quant Spectrosc Radiat Transf, 2003, 79-80:953-972. doi:  10.1016/S0022-4073(02)00331-X
    [42]
    Mishchenko M I, Liu L, Geogdzhayev I V, et al.Aerosol retrievals from channel-1 and-2 AVHRR radiances:Long-term trends updated and revisited.J Quant Spectrosc Radiat Transf, 2012, 113(15):1974-1980. doi:  10.1016/j.jqsrt.2012.05.006
    [43]
    Dong C, Yang J, Zhang W, et al.An overview of new Chinese weather satellite FY-3A.Bull Am Meteorol Soc, 2009, 90(10):1531-1544. doi:  10.1175/2009BAMS2798.1
    [44]
    Gao C, Zhao Y, Li C, et al.An investigation of a novel cross-calibration method of FY-3C/VIRR against NPP/VIIRS in the Dunhuang test site.Remote Sensing, 2016, 8(1):77-98. https://www.researchgate.net/publication/291527400_An_investigation_of_a_novel_cross-calibration_method_of_FY-3CVIRR_against_NPPVIIRS_in_the_Dunhuang_test_site
    [45]
    Xu N, Chen L, Hu X, et al.Assessment and correction of on-orbit radiometric calibration for FY-3 VIRR thermal infrared channels.Remote Sens, 2014, 6(4):2884-2897. doi:  10.3390/rs6042884
    [46]
    Yu F, Wu X.An integrated method to improve the GOES Imager visible radiometric calibration accuracy.Remote Sens Environ, 2015, 164:103-113. doi:  10.1016/j.rse.2015.04.003
    [47]
    Wang L, Cao C, Goldberg M.Intercalibration of GOES-11 and GOES-12 water vapor channels with MetOp IASI hyperspectral measurements.J Atmos Ocean Technol, 2009, 26(9):1843-1855. doi:  10.1175/2009JTECHA1233.1
    [48]
    Meng Z, Zhang Y.On the squall lines preceding landfalling tropical cyclones in China.Mon Wea Rev, 2012, 140:445-470. doi:  10.1175/MWR-D-10-05080.1
    [49]
    Mecikalski J R, Bedka K M.Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery.Mon Wea Rev, 2006, 134:49-78. doi:  10.1175/MWR3062.1
    [50]
    Mecikalski J R, Bedka K M, Paech S J, et al.A statistical evaluation of GOES cloud-top properties for nowcasting convective initiation.Mon Wea Rev, 2008, 136:4899-4914. doi:  10.1175/2008MWR2352.1
    [51]
    刘健, 蒋建莹.FY-2C高时间分辨率扫描数据在强对流云团监测中的应用研究.大气科学, 2013, 37(4):873-880. doi:  10.3878/j.issn.1006-9895.2012.12062
    [52]
    Yang X, Fei J, Huang X, et al.Characteristics of mesoscale convective systems over China and its vicinity using geostationary satellite FY2.J Clim, 2015, 28(12):4890-4907. doi:  10.1175/JCLI-D-14-00491.1
    [53]
    Xiong X, Sun J, Xie X, et al.On-orbit calibration and performance of Aqua MODIS reflective solar bands.IEEE Trans Geosci Remote Sens, 2010, 48(1):535-546. doi:  10.1109/TGRS.2009.2024307
    [54]
    Xiong X, Chiang K, Sun J, et al.NASA EOS Terra and Aqua MODIS on-orbit performance.Adv Space Res, 2009, 43(3):413-422. doi:  10.1016/j.asr.2008.04.008
    [55]
    Xiong X, Wu A, Cao C.On-orbit calibration and inter-comparison of Terra and Aqua MODIS surface temperature spectral bands.Int J Remote Sens, 2008, 29(17-18):5347-5359. doi:  10.1080/01431160802036300
    [56]
    [57]
    Yang Z, Lu N, Shi J, et al.Overview of FY-3 payload and ground application system.IEEE Trans Geosci Remote Sens, 2012, 50(12):4846-4853. doi:  10.1109/TGRS.2012.2197826
    [58]
    Xu H, Xu N, Hu X.Inter-Calibration of Infrared Bands of FY-3C MERSI and VIRR Using Hyperspectral Sensor CrIS and IASI.Proc.SPIE 9264, Earth Observing Missions and Sensors:Development, Implementation, and Characterization Ⅲ, 92640B, 2014. https://www.researchgate.net/profile/Xiuqing_Hu/publication/290534157_Inter-calibration_of_infrared_bands_of_FY-3C_MERSI_and_VIRR_using_hyperspectral_sensor_CrIS_and_IASI/links/574cdac408ae061b3301e86c.pdf?inViewer=0&pdfJsDownload=0&origin=publication_detail
    [59]
    Uprety S, Cao C.Suomi NPP VIIRS reflective solar band on-orbit radiometric stability and accuracy assessment using desert and Antarctica Dome C sites.Remote Sens Environ, 2015, 166:106-115. doi:  10.1016/j.rse.2015.05.021
    [60]
    Cao C, Xiong J, Blonski S, et al.Suomi NPP VIIRS sensor data record verification, validation, and long-term performance monitoring.J Geophys Res Atmos, 2013, 118(20):11664-11678. doi:  10.1002/2013JD020418
    [61]
    Okuyama A, Andou A, Date K, et al.Preliminary validation of Himawari-8/AHI navigation and calibration.SPIE, 2015, 9607:96072E. http://adsabs.harvard.edu/abs/2015SPIE.9607E..2EO
    [62]
    Da C.Preliminary assessment of the advanced Himawari imager (AHI) measurement onboard Himawari-8 geostationary satellite.Remote Sens Lett, 2015, 6(8):637-646. doi:  10.1080/2150704X.2015.1066522
    [63]
    Manolis I, Grabarnik S, Caron J, et al.The MetOp Second generation 3MI Instrument.Proc SPIE, 2013, 5:88890J. https://www.researchgate.net/publication/260967393_The_MetOp_Second_Generation_3MI_instrument
    [64]
    Nazionale C, Aeronautica C, Pratica V, et al.METOP-SG 3MI (Multi-viewing Multi-channel Multi-polarization Imaging), a Powerful Observing Mission for Future Operational Applications.Daniele BIRON.EUMETSAT Meteorological Satellite Conference, Vienna, 2013.
  • 加载中
  • -->

Catalog

    Tables(4)

    Article views (2583) PDF downloads(446) Cited by()
    • Received : 2016-06-27
    • Accepted : 2016-07-15
    • Published : 2016-09-30

    /

    DownLoad:  Full-Size Img  PowerPoint