A Fast Parametric Model of Estimating Atmospheric Parameters for Landsat 8 Thermal Infrared Sensor

The traditional methods used in the atmospheric correction depend on the empirical relationships or atmospheric radiative transfer model. However, both of them have some deficiencies. For example, as the empirical method depends highly on the training data, it will be applicable under certain conditions. On the other hand, the method that based on the atmospheric radiative transfer model has to run the code each time, which is not an appropriate choice for operational correction of the atmospheric effects. In this paper, a fast parametric model of estimating atmospheric parameters for Landsat 8 thermal infrared sensor is proposed. The results show that the RMSE (Root Mean Squared Error) of the total transmission is 0.003, the RMSE of for both the atmospheric upward and downward radiances are 0.0004. Therefore, the proposed model could be used without the help of any atmospheric radiative transfer model. That is, this model would have a better application market.

[1]  G. Ritchie,et al.  Radiation in the Atmosphere , 2017 .

[2]  A. Karnieli,et al.  A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region , 2001 .

[3]  José A. Sobrino,et al.  Satellite-derived land surface temperature: Current status and perspectives , 2013 .

[4]  Paul E. Lewis,et al.  MODTRAN5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options , 2004, SPIE Defense + Commercial Sensing.

[5]  Alan R. Gillespie,et al.  Autonomous atmospheric compensation (AAC) of high resolution hyperspectral thermal infrared remote-sensing imagery , 2000, IEEE Trans. Geosci. Remote. Sens..

[6]  J. Sobrino,et al.  A generalized single‐channel method for retrieving land surface temperature from remote sensing data , 2003 .

[7]  S. J. Young,et al.  An in‐scene method for atmospheric compensation of thermal hyperspectral data , 2002 .

[8]  Simon J. Hook,et al.  Validation of a New Parametric Model for Atmospheric Correction of Thermal Infrared Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Ning Wang,et al.  Land surface emissivity retrieval from satellite data , 2013 .

[10]  K. I. Kondratʹev.  Radiation in the atmosphere , 1969 .

[11]  Fabio Del Frate,et al.  Review of Thermal Infrared Applications and Requirements for Future High-Resolution Sensors , 2016, IEEE Transactions on Geoscience and Remote Sensing.