Reading and manipulaing satellite sensor spectral responses and the solar spectrum, to perfom various corrections to VIS and NIR band data

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  • License: GNU General Public License v3 or later (GPLv3+) (GPLv3)
  • Author: Adam Dybbroe
  • Requires: Python >=3.10
  • Provides-Extra: xlrd, trollsift, matplotlib, pandas, tqdm, test, dask
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Pyspectral

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Given a passive sensor on a meteorological satellite Pyspectral provides the relative spectral response (rsr) function(s) and offer you some basic operations like convolution with the solar spectrum to derive the in band solar flux, for instance. The focus is on imaging sensors like AVHRR, VIIRS, MODIS, ABI, AHI, OLCI and SEVIRI. But more sensors are included and if others are needed they can be easily added. With Pyspectral it is possible to derive the reflective and emissive parts of the signal observed in any NIR band around 3-4 microns where both passive terrestrial emission and solar backscatter mix the information received by the satellite. Furthermore Pyspectral allows correcting true color imagery for the background (climatological) atmospheric signal due to Rayleigh scattering of molecules, absorption by atmospheric gases and aerosols, and Mie scattering of aerosols.

Adam Dybbroe May 2021, Norrkoping, Sweden

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Avatar for adybbroe from gravatar.com adybbroe Avatar for dhoese from gravatar.com dhoese Avatar for Martin.Raspaud from gravatar.com Martin.Raspaud

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  • License: GNU General Public License v3 or later (GPLv3+) (GPLv3)
  • Author: Adam Dybbroe
  • Requires: Python >=3.10
  • Provides-Extra: xlrd, trollsift, matplotlib, pandas, tqdm, test, dask
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