This is the algorithm description derived from the prototype implementation provided by the authors and subsequent conversations and emails.

ST-algorithm-description.txt

This is the algorithm description derived from the prototype implementation
provided by the authors and subsequent conversations and emails.


==============================================================================
Algorithm Authors:

    Monica Cook
        Chester F. Carlson Center for Imaging Science
        Rochester Institute of Technology
        mxc7441@rit.edu

    Simon J. Hook
        Research Scientist
        NASA Jet Propulsion Laboratory
        email: Simon.J.Hook@jpl.nasa.gov

    Glynn C. Hulley
        Research Scientist
        NASA Jet Propulsion Laboratory
        email: glynn.hulley@nasa.gov

    Kelly Laraby
        Chester F. Carlson Center for Imaging Science
        Rochester Institute of Technology
        kga1099@rit.edu

    John R. Schott
        Chester F. Carlson Center for Imaging Science
        Rochester Institute of Technology
        schott@cis.rit.edu

==============================================================================
Algorithm Description - Overview:

        1) Create a Landsat emissivity band based on Landsat top of 
           atmosphere reflectance, ASTER emissivity and ASTER NDVI data.
 
        2) Read NARR atmospheric data at grid points over the Landsat scene.

        3) Use the atmospheric data to generate MODTRAN runs for multiple
           elevations and temperature/albedo pairs at each grid point.

        4) Use the MODTRAN results to generate transmission, upwelled 
           radiance, and downwelled radiance at each grid point and elevation.

        5) Interpolate the nearby grid point values to create similar values
           for each pixel at the height of the pixel.

        6) Use all of the previously-generated bands to make a surface
           temperature band.

        7) Use the previously-generated bands and a cloud distance band to
           make a quality band for the surface temperature band.

==============================================================================
Algorithm Description - Inputs:

    The primary sources of input to the algorithm are:

        Landsat L1T thermal band retrieved from LPGS L1T products
        NARR (North American Regional Reanalysis) grib formatted data
        ASTER Emissivity and NDVI data
        Landsat TOA (top of atmosphere) reflectance bands
        GLS2000 derived DEM (same as what Landsat uses)

    The L1T thermal band is used to create a thermal radiance band. 

    The NARR data provides a 32 km grid of atmospheric data at 29 pressure
        levels.  The data that is used includes geopotential height, 
        temperature, and specific humidity at multiple pressure levels.
        The NARR data is used to generate inputs to MODTRAN which is used
        to calculate atmospheric transmission, upwelled radiance, and 
        downwelled radiance at the grid points at multiple elevations.

    The ASTER emissivity and NDVI data, as well as the Landsat TOA data
        from multiple bands, are used to simulate a Landsat emissivity band 
        matching the Landsat scene. 

    The DEM is used to adjust for the correct height during the interpolation
        step.

==============================================================================
Algorithm Description - Detailed:

        1) Read Landsat top of atmosphere reflectance green, red, NIR, SWIR1,
           and brightness temperature bands for the scene.  Generate a 
           Landsat NDVI layer from the red and NIR bands.  Generate NDSI and
           snow layers from the green and SWIR bands.  Get projection 
           information from the brightness temperature band.  

        2) Retrieve ASTER emissivity and NDVI data, and warp them to match
           the Landsat scene. 

        3) Calculate a simulated Landsat emissivity layer using the ASTER 
           emissivity values, ASTER NDVI values, Landsat NDVI band, NDSI band, 
           and snow locations.  Write the simulated Landsat emissivity layer 
           to a file. 

        4) Determine grid of points based on NARR data coverage and Landsat 
           scene coverage plus a buffer to allow points outside the scene to 
           be used since they are needed during later interpolation steps for
           pixels near the scene edges.

        5) Extract NARR data at grid points for multiple pressure levels:

            - temperature
            - geopotential height
            - specific humidity

        6) Convert geopotential height to geometric height.

        7) Convert specific humidity to relative humidity.

        8) Interpolate geometric height and relative humidity before and after 
           the Landsat scene time to match the scene time.

        9) Create MODTRAN "tape5" format input files at each grid point for 
           9 elevations, and 3 temperature/albedo pairs per elevation.  The
           input for each MODTRAN run includes the geometric height, relative 
           humidity, temperature, and pressure.

       10) Run MODTRAN to generate temperature, wavelength, and radiance values.

       11) Read the MODTRAN results.  Also read satellite-specific spectral 
           response files.  Use them to calculate transmission, upwelled
           radiance, and downwelled radiance at each height for each grid
           point.

       12) For each pixel in the Landsat scene, find the closest set of 
	   4 surrounding grid points.  Interpolate the 3 parameters 
           (transmission, upwelled and downwelled radiance) at these
           surrounding grid points to the spatial location and height
           of the Landsat pixel. 

       13) Create band files for atmospheric transmission, upwelled radiance,
           and downwelled radiance based on the computed per-pixel layers. 

       14) Use the Landsat L1T thermal band to derive a thermal radiance
           layer, and write a band file for that layer.

       15) Read the emissivity, thermal radiance, atmospheric transmission, 
           upwelled radiance, and downwelled radiance bands.

       16) Calculate surface_radiance:

           (thermal_radiance - upwelled_radiance) / atmospheric_transmission

       17) Calculate Earth-emitted radiance:

           surface_radiance - (1.0 - emissivity) * downwelled_radiance

       18) Use a satellite-specific brightness temperature lookup table with 
           radiance and temperature to linearly interpolate Earth-emitted
           radiance values to derive surface temperature.

       19) Scale the surface temperature value to include 1/10 degree precision
           in the integer product. 

       20) Use the level 2 quality band (pixel_qa) to identify cloud locations,
           and build a distance to cloud band based on those locations.

       21) Calculate atmospheric uncertainty, instrument uncertainty,
           emissivity uncertainty, cross correlation terms, and unknown
           uncertainty using thermal radiance, atmospheric transmission,
           upwelled radiance, downwelled radiance, emissivity, emissivity
           standard deviation, and cloud distance bands.

       22) Combine uncertainties and use the result to create a surface
           temperature quality band.