remotior_sensus.tools.preprocess_products module

Perform the preprocessing of products.

remotior_sensus.tools.preprocess_products.create_product_table(input_path, metadata_file_path=None, product=None, nodata_value=None, sensor=None, acquisition_date=None)
remotior_sensus.tools.preprocess_products.perform_preprocess(product_table, output_path, dos1_correction=False, output_prefix='', n_processes: int | None = None, available_ram: int | None = None, progress_message=True) → OutputManager

Preprocess products.

Perform image conversion to reflectance of several products.

Can calculate DOS1 corrected reflectance (Sobrino, J. et al., 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, Elsevier, 90, 434-440) approximating path radiance to path reflectance for level 1 data: TOA reflectance = DN * reflectance_scale + reflectance_offset path reflectance p = DNm - Dark Object reflectance = DNm * reflectance_scale + reflectance_offset - 0.01 land surface reflectance = TOA reflectance - p = (DN * reflectance_scale) - (DNm * reflectance_scale - 0.01)

Landsat’s data Collection 1 and 2 Level 1T Landsat 8-9 TOA reflectance proportional to exo-atmospheric solar irradiance in each band and the Earth-Sun distance (USGS, 2021. Landsat 8-9 Calibration and Validation (Cal/Val) Algorithm Description Document (ADD). Version 4.0. Department of the Interior U.S. Geological Survey, South Dakota) TOA reflectance with correction for the sun angle = DN * Reflectance multiplicative scaling factor + Reflectance additive scaling factor / sin(Sun elevation) Level 2S Surface reflectance = DN * Reflectance multiplicative scaling factor + Reflectance additive scaling factor

Sentinel-2 data Level 1C TOA reflectance = DN / QUANTIFICATION VALUE + OFFSET Level 2S Surface reflectance = DN / QUANTIFICATION VALUE + OFFSET

Parameters:

  • product_table –

  • output_path –

  • dos1_correction –

  • output_prefix –

  • n_processes –

  • available_ram – number of megabytes of RAM available to processes.

  • progress_message –

Returns:

object OutputManager()

remotior_sensus.tools.preprocess_products.preprocess(input_path, output_path, metadata_file_path=None, product=None, nodata_value=None, sensor=None, acquisition_date=None, dos1_correction=False, output_prefix='', n_processes: int | None = None, available_ram: int | None = None, progress_message=True)