MULTIPLY - Sensor Invariant Atmospheric Correction (SIAC)

This atmospheric correction method uses MODIS MCD43 BRDF product to get a coarse resolution simulation of earth surface. A model based on MODIS PSF is built to deal with the scale differences between MODIS and other sensors, and linear spectral mapping is used to map between different sensors spectrally. We uses the ECMWF CAMS prediction as a prior for the atmospheric states, coupling with 6S model to solve for the atmospheric parameters, then the solved atmospheric parameters are used to correct the TOA reflectances. The whole system is built under Bayesian theory and the uncertainty is propagated through the whole system. Since we do not rely on specific bands’ relationship to estimate the atmospheric states, but instead a more generic and consistent way of inversion those parameters. The code can be downloaded from SIAC github directly and futrher updates will make it more independent and can be installed on different machines.

Development of this code has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687320, under project H2020 MULTIPLY. f this code has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687320, under project H2020 MULTIPLY.

Contents:

• SIAC
  • Introduction
  • Sentinel 2 and Landsat 8 correction examples
• Installation
  • 1. The standard python way
  • 2. Using pip
  • 3. Using anaconda
  • 4. From code repository for developing
• Quickstart
• SIAC data accessing and usage on other sensors
  • SIAC
  • Data access:
  • Run SIAC
• Technical documentation
  • SIAC_S2
  • SIAC_L8
  • Atmospheric parameters Inversion
  • Atmospheric parameters Solver
  • Atmospheric correction

Indices and tables

• Index

• Module Index

• Search Page