Radiative transfer in the midwave infrared applicable to full spectrum atmospheric characterization

The compensation for atmospheric effects in the VNIR/SWIR has reached a mature stage of development with many algorithms available for application (ATREM, FLAASH, ACORN, etc.). Compensation of LWIR data is the focus of a number of promising algorithms. A gap in development exists in the MWIR where little or no atmospheric compensation work has been done yet an increased interest in MWIR applications is emerging. To obtain atmospheric compensation over the full spectrum (visible through LWIR), a better understanding of the radiative effects in the MWIR is needed. The MWIR is characterized by a unique combination of reduced solar irradiance and low thermal emission (for typical emitting surfaces), both providing relatively equal contributions to the daytime MWIR radiance. In the MWIR and LWIR, the compensation problem can be viewed as two interdependent processes: compensation for the effects of the atmosphere and the uncoupling of the surface temperature and emissivity. The former requires calculations of the atmospheric transmittance due to gases, aerosols, and thin clouds and the path radiance directed towards the sensor (both solar scattered and thermal emissions in the MWIR). A framework for a combined MWIR/LWIR compensation approach is presented where both scattering and absorption by atmospheric particles and gases are considered

[1]  J. W. Snow,et al.  Characterization and delineation of plumes, clouds and fires in hyperspectral images , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[2]  F. Becker,et al.  The impact of spectral emissivity on the measurement of land surface temperature from a satellite , 1987 .

[3]  M. Griffin,et al.  Compensation of Hyperspectral Data for Atmospheric Effects , 2003 .

[4]  D. C. Robertson,et al.  MODTRAN cloud and multiple scattering upgrades with application to AVIRIS , 1998 .

[5]  W. Malkmus,et al.  Random Lorentz band model with exponential-tailed S-1 line-intensity distribution function , 1967 .

[6]  C. Justice,et al.  Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation , 1997 .

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

[8]  Eva Rubio,et al.  Thermal band selection for the PRISM instrument: 1. Analysis of emissivity‐temperature separation algorithms , 1997 .

[9]  E. Shettle,et al.  Models for the aerosols of the lower atmosphere and the effects of humidity variations on their optical properties , 1979 .

[10]  A. Cantor Optics of the atmosphere--Scattering by molecules and particles , 1978, IEEE Journal of Quantum Electronics.

[11]  K. Stamnes,et al.  Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. , 1988, Applied optics.