TASI data processing

Radiometric corrections Radiometric corrections of measured data are carried out in RadCorr (Itres Ltd) program. For the TASI-600 sensor there are two basic possibilities for radiometric data calibration. The first option is to perform radiometric calibration using laboratory-determined calibration parameters. This method is used if auxiliary data was scanned from only one black calibration body during the flight. The second option is to use the calibration coefficients specified for each flight line separately, using data from two calibration black bodies scanned during the flight. The latter option is usually used. The values ​​of the final image data are given in radiometric units [μW cm-2 sr-1 nm-1] multiplied by 1000. The reason for usage of the multiply coefficient 1000 is better utilization of the unsigned integer range. Spectral channels 1-5 and 28-32 were omitted from further processing due to excessive noise. The increased noise is due to the low signal intensity at the wavelengths in question and these channels are excluded from further processing as standard. The processed TASI data thus contains 22 spectral channels.

During the radiometric corrections, additional data corrections are made for effects that negatively affect the scanned hyperspectral data e.g.


Bad Pixel Interpolation. The value of the defective pixels is replaced by the interpolated value from the surrounding spatial pixels. The interpolation of defective pixels is performed in two steps. First, the sensor manufacturer’s procedure is applied. After the radiometric corrections, the procedure developed at CzechGlobe is applied.


Residual Correction. Additional corrections for effects that could not be picked up during standard processing are based on image data analysis and correction of pixels that deviate from the surrounding data.


On top of the ITRES recommended procedure to interpolate bad pixels in TASI, an additional two-step correction is applied to remove noise. In the first step, stripe noise is corrected in the spectral domain by in-house developed script. In the second step, stochastic noise in the data is removed by spatial interpolation (He et al., 2019).


Atmospheric corrections and calculations of temperature characteristics Radiometric calibrations deliver image data containing radiation from the surface ε B(T), attenuated by atmosphere, plus radiation from the atmosphere along the line of sight. Thus, the measured radiance at sensor level L consists mainly of radiance emitted from the land surface ε B(T), down-welling atmospheric radiance  L↓atm  reflected by the surface and the atmospheric upwelling radiance L↑atm. The sum of all these components is expressed by a radiative transfer equation (RTE) as follows:

L = τ ε B(T) + τ (1 – ε) L↓atm + L↑atm,

B(T) is the radiance of the surface at temperature T according to the Planck’s law, ε is the surface’s emissivity and τ is atmospheric transmittance. It is important to emphasize that all elements in the equation are wavelength dependent but notation for this is omitted for the sake of clarity. Since the sensor is of finite bandwidth, quantities in RTE equation are replaced by band-effective equivalents. Kirchhoff’s law of thermal radiation implies that reflectivity can be rewritten as (1 – ε) for opaque materials. RTE can be used under the assumption of cloud-free atmosphere under local thermodynamic equilibrium.

The quantities L↓atmL↑atm and τ are modelled using the MODTRAN 5.3 radiation transfer model, which is usually parameterized by means of reanalysis ERA5. Compensating for atmospheric transmittance and up-welling atmospheric radiance led to land‑leaving radiance (LLL):

LLL = ε B(T) + (1 – ε) L↓atm,

LLL is the sum of the radiance emitted by the surface and the reflected radiance. Taking the down‑welling atmospheric radiation L↓atm into account, it is not possible without knowing emissivity of the surface. Eliminating the influence of down-welling atmospheric radiance is part of the calculation of temperature  T (kinetic) and emissivity ε of the surface – it is performed by the Temperature and Emissivity Separation algorithm (TES). From TES the noisiest bands are excluded so that final products have 24 bands. Using the radiance leaving the surface LLL, it is possible to calculate the brightness temperature which approximates the temperature T. The brightness temperature calculation was based on the inversion of the Planck law assuming emissivity equals one. Brightness temperature was calculated from the average of all the spectral bands and is therefore less affected by noise.


Georeferencing Georeferencing was carried out by means of a parametric geocoding method using data acquired by the GNSS/IMU unit and the digital terrain model in GeoCor (Itres ltd.) program. In one step, geometric corrections, orthorectification and data georeferencing are performed. For the resampling of the data into the coordinate system, the nearest neighbor method was used. Hyperspectral data are usually georeferenced into the UTM coordinate system (zone 33N, ETRS-89).


Standard Outputs:

  • Image data of kinetic temperature (T); LST [K] (land surface temperature)
  • Image data of emissivity; LSE [-] (land surface emissivity)
  • Image data of radiation leaving the surface of the body LLL; LLL [W m-2 sr-1 m-1]  (land leaving radiance)
  • Image data of brightness temperature; BBT [K] (broadband brightness temperature)

Some examples of our data you can find on MapServer.

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