The Meteorological Satellite Center of the Japan Meteorological Agency (JMA/MSC) contributes to the Global Space-based Inter-Calibration System (GSICS) and examines ways to improve vicarious calibration for visible and near-infrared (VIS/NIR) bands of instrumentation on geostationary orbit (GEO) satellites. Under the GSICS framework, various ways of validating VIS/NIR-band sensor sensitivity are considered.
One such method, known as ray-matching, involves using the sensor on the Low Earth Orbit (LEO) satellite as reference for comparison against GEO instrument-based observation. As the reference sensor is well calibrated and stable, the bias of GEO observation can be estimated and correction values derived.

JMA/MSC plans to implement validation of Himawari-8/-9 VIS/NIR data based on the ray-matching technique with VIIRS as reference, in addition to the current validation based on radiative transfer simulation (RSTAR) data. This method has the following advantages over the existing approach:

  • No dependence on the radiative transfer model
  • Wide dynamic range with applied targets from low-reflectance (e.g., clear sky ocean) to high-reflectance (e.g., deep convective cloud)
  • Figure 1 outlines the JMA/MSC ray-matching process, and the items are subsequently described in detail.

    Ray-matching process
    Fig. 1: Ray-matching process


    The outcomes of the ray-matching approach are linear regression coefficients, scatter plots for individual bands, statistical CSV files and time-series graphs.

    Coefficients of regression between reference and GEO imager observation

    Regression coefficients are displayed as scatter plots and stored in CSV files (described later). Linear regression via the origin (referred to as force-fit regression) and with offsetting are applied. A regression coefficient (Slope) can be applied in force-fit regression using the equation below to produce corrected AHI reflectance (Rcorr_AHI) in reference to VIIRS reflectance.
    Rcorr_AHI = Rorg_AHI / Slope .

    Here, Rorg_AHI is the original AHI reflectance.

    Scatter plots

    Fig. 2: Relationship between
               Band 03 of AHI and I01 of VIIRS

    Figure 2 shows the reflectance relationship between reference values based on SNPP/VIIRS and HIMAWARI-8/AHI.
    Warm colors (e.g., orange, red) and cold colors (e.g., blue, grey) represent high and low numbers of collocations, respectively, and the coefficients for force-fit and linear regression are shown in the upper left. Values in parentheses represent standard errors. For simplicity, only the force-fit regression line is displayed (red line).

    CSV files

    Ray-matching results are also stored in CSV file format, including slope, offset and statistics based on both regression methods.

    Time-series representation

    Time-series graphs consist of three parts (Fig. 3), with the upper and middle ones showing offset and the slope of linear regression since July 2015, respectively. Red and blue plots represent statistics for t - 14 days to t + 14 days and for t + 0 days (t: date of validity), respectively. The bottom part shows collocation variations, with red bars representing half the number of collocation data gathered during the 29 days and blue bars representing the number of pixels gathered on the specific single day.

    Ray-matching process
    Fig. 3: Time-series representations of slope, offset and numbers of collocations


    VIIRS data are used as reference for ray matching, and the reflectance of the AHI VIS/NIR bands is validated (Fig. 1).


    • Monitoring data: AHI
    • Himawari Standard Data (HSD) resampled with a spatial resolution of approximately 2 km at the sub-satellite point are used, with AHI reflectance calculated as
      Reflectance = Radiance × π d2/ F0 cos(SZA)    (2)
      Here, d is the Earth-sun distance in AU (Astronomical Units) and F0 is solar incident irradiance (see Table 1 of see the Visible and Near-Infrared Vicarious Calibration Guide), and cos(SZA) is the cosine of the solar zenith angle.

    • Reference data: VIIRS
    • S-NPP/VIIRS SDR data from the NOAA class server are used for reference (Table 1: band details). Although the center wavelengths of VIIRS and AHI are similar for each band, their spectral response functions (SRFs) differ slightly. The difference is considered via a spectral band adjustment factor (SBAF) as described later. As the spatial resolutions of VIIRS and AHI data differ, VIIRS data pixels are averaged with surrounding pixels (M-band: 3 x 3 pixels; I-band: 5 x 5 pixels).

    Table 1: Selected VIIRS bands corresponding to Himawari-8/AHI monitored bands
    Band 1
    0.47 µm
    (2.0 km)
    Band 2
    0.51 µm
    (2.0 km)
    Band 3
    0.64 µm
    (2.0 km)
    Band 4
    0.86 µm
    (2.0 km)
    Band 5
    1.6 µm
    (2.0 km)
    Band 6
    2.3 µm
    (2.0 km)
    0.488 µm
    (750 m)
    0.488 µm
    (750 m)
    0.640 µm
    (375 m)
    0.865 µm
    (750 m)
    1.61 µm
    (750 m)
    2.25 µm
    (750 m)


    From AHI and VIIRS data, pixels meeting the geometric conditions (Table 2) are extracted. AHI and VIIRS collocation data are produced by matching data with same positions.

    Table 2: Conditions for AHI-VIIRS Ray-matching
    Condition Threshold
    Observation time difference < 5 min.
    Satellite zenith angle difference < 10 deg.
    Satellite azimuth angle difference < 10 deg.
    Sun glint angle*1 (AHI only) >25 deg.
    Brightness temperature @ 10.4µm (AHI only) < 273.15 K
    STDV of reflectance/Mean of reflectance < 5%

    *1: The angle between vectors A and B, where A has the direction of specular reflection of solar light via the observation point, and B has the direction of the satellite via the observation point

    Spectral band adjustment factors (SBAFs)

    SBAFs are applied in ray-matching to correct slight differences in AHI and VIIRS spectral responses using NASA' web-based SBAF Tool. SBAFs are derived using Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) data and AHI/ VIIRS SRF values. Table 3 shows SBAFs between scaled radiance for AHI and VIIRS with the “Force Fit” and “All-sky Tropical Ocean” conditions set by NASA’s SBAF tool. As AHI Band 06 (2.3µm) is not supported by the tool, the SBAF of this band vs. VIIRS M11 is estimated via radiative transfer simulation using RSTAR.

    Table 3: SBAFs between AHI and VIIRS
    Band 1 vs. M03 Band 2 vs. M03 Band 3 vs. I01 Band 4 vs. M07 Band 5 vs. M10 Band 6 vs. M11
    SBAF 1.022 0.960 0.999 0.997 1.018 1.000

    For each band, SBAF is applied as

    RSBAF = SBAF × Roriginal
    Here, RSBAF is VIIRS reflectance corrected using SBAF, and Roriginal is the original VIIRS reflectance in the SDR.

    Comparison and monitoring

    AHI reflectance is compared to VIIRS reflectance corrected using SBAF via linear regression, and the outcomes described above are derived.


    • Ray-matching
      Doelling D., R. Bhatt, D. Morstad, B. Scarino, 2011: Algorithm Theoretical Basis Document (ATBD) for ray-matching technique of calibrating GEO sensors with Aqua-MODIS for GSICS (PDF, 302 kB)

    • SBAF
      Scarino B. R, D. R. Doelling, P. Minnis, A. Gopalan, T. Chee, R. Bhatt, C. Lukashin, C. O. Haney, 2016: A web-based tool for calculating spectral band difference adjustment factors derived from SCIAMACHY hyperspectral data, IEEE Trans. Geosci. Remote Sens., 54, No. 5, 2,529 - 2,542

    • RSTAR
      Nakajima, T., M. Tanaka, 1986: Matrix formulation for the transfer of solar radiation in a plane-parallel scattering atmosphere, J. Quant. Spectrosc. Radiat. Transfer, 35, 13 - 21
      Nakajima, T., M. Tanaka, 1988: Algorithms for radiative intensity calculations in moderately thick atmospheres using a truncation approximation, J. Quant. Spectrosc. Radiat. Transfer, 40, 51 - 69
      Stamnes, K., S.-C. Tsay, W. Wiscombe, K. Jayaweera, 1988: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Opt., 27, 2,502 - 2,509