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Apply 6S atmospheric correction to satellite images using Py6S
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- Опубликовано: 6 май 2024
- Atmospheric correction refer to group of procedures to remove the effect of the atmosphere on the reflectance values of images taken by satellite or ariborne sensors. Atmospheric correction methods can be image-based or radiative transfer model-based.
6S (Second Simulation of a Satellite Signal in the Solar Spectrum) is an atmospheric correction method. It uses advanced radiative transfer code designed to simulate the reflection of solar radiation by a coupled atmosphere-surface system for a wide range of atmospheric, spectral, and geometrical conditions.
This video shows how to apply 6S atmospheric correction to Sentinel-2 Level-1C (Top-of-atmosphere Reflectance). The images were taken from Google Earth Engine. The Python implementation of 6S was done in a Jupyter notebook environment in Microsoft Planetary Computer using Py6S modules. The result of corrected Sentinel-2 images was stored as a Google Earth Engine asset.
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Koreksi atmosferik merupakan suatu prosedur untuk menghilangkan atau mengurangi pengaruh kolom atmosfer terhadap energi yang ditangkap sensor penginderaan jauh pada wahana udara atau satelit. Kompensasi pengaruh atmosfer diharapkan dapat menghasilkan nilai reflektans yang mendekati kenyataan. Metode koreksi atmosferik ada yang sifatnya berbasis citra (seperti pengurangan benda hitam) maupun berbasis model atmosfer.
6S (Second SImulation of a Satellite Signal in the Solar Spectrum) adalah salah satu metode koreksi atmosferik berbasis model atmosfer. Metode ini menggunakan kode radiative transfer untuk mensimulasikan pantulan radiasi matahari pada berbagai kondisi.
Video ini menunjukkan cara melakukan prosedur koreksi atmosferik 6S terhadap citra Sentinel-2 Level-1C (Top-of-atmospheric reflectance). Citranya 'ditarik' dari koleksi citra pada Google Earth Engine. Seluruh prosedur dilakukan dengan bahasa pemrograman python pada Jupyter notebook di Microsfot Planetary Computer. Prosedur ini menghasilkan citra terkoreksi yang disimpan kembali sebagai aset di Google Earth Engine untuk kemudian divisualisasikan pada platform tersebut.
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Data: Sentinel-2
Library: py6s (github.com)
Hello, thank you very much for your time, would it be possible to make this correction directly to Planetary computer? Also do you know if it is possible to obtain the saa, sza, vaa and vza, from the sentinel 2 images in Planetary computer
I believe the image can be sourced from local storage or STAC asset as well. Havent tried yet but plan to explore. The only info you'd need from the image is acquisition date, CMIIW.
Please try OCM3 sensor L1C data sir ;; my request
Not familiar with the data. Could you please ellaborate?