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PACO: Python-Based Atmospheric Correction

Authors
  • de los Reyes, Raquel1
  • Langheinrich, Maximilian1
  • Schwind, Peter1
  • Richter, Rudolf1
  • Pflug, Bringfried
  • Bachmann, Martin
  • Müller, Rupert1
  • Carmona, Emiliano1
  • Zekoll, Viktoria1
  • Reinartz, Peter1
  • 1 (P.R.)
Type
Published Article
Journal
Sensors
Publisher
MDPI AG
Publication Date
Mar 05, 2020
Volume
20
Issue
5
Identifiers
DOI: 10.3390/s20051428
PMID: 32151105
PMCID: PMC7085641
Source
PubMed Central
Keywords
License
Green

Abstract

The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric correction software, capable of processing data acquired by many different optical satellite sensors. Based on this well established algorithm, a new Python-based atmospheric correction software has been developed to generate L2A products of Sentinel-2, Landsat-8, and of new space-based hyperspectral sensors such as DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program). This paper outlines the underlying algorithms of PACO, and presents the validation results by comparing L2A products generated from Sentinel-2 L1C images with in situ (AERONET and RadCalNet) data within VNIR-SWIR spectral wavelengths range.

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