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Fast and Reliable Top of Atmosphere (TOA) calculations from Landsat-8 data in Python

Ruas de Pinho

In this tutorial, I will show how to extract reflectance information from Landsat-8 Level-1 Data Product images. If you don't know what I'm talking about:

Standard Landsat 8 data products provided by the USGS EROS Center consist of quantized and calibrated scaled Digital Numbers (DN) representing multispectral image data acquired by both the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). The products are delivered in 16-bit unsigned integer format and can be rescaled to the Top Of Atmosphere (TOA) reflectance and/or radiance using radiometric rescaling coefficients provided in the product metadata file (MTL file), as briefly described below. The MTL file also contains the thermal constants needed to convert TIRS data to the top of atmosphere brightness temperature.

In summary:

  1. The DN is a scaled number of the measured values. We have ways to rescale the DN values into TOA values using coefficients that are found in the metadata files (MTL) that we usually download with the image files (TIF).

  2. Top of Atmosphere (TOA) Reflectance and Brightness Temperature are what we use to study the earth surface material spectrum from multipectral satellite images. There are huge databases of known spectrums for a variety of materials measured in labs and convolved to different spectrometer and multispectral sensors. One good example is the USGS's Spectroscopy Lab, check their work.

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