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Related Experiment Video

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Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

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PACO: Python-Based Atmospheric COrrection.

Raquel de Los Reyes¹, Maximilian Langheinrich¹, Peter Schwind¹

¹German Aerospace Center (DLR), Earth Observation Center, Remote Sensing Technology Institute, Photogrammetry and Image Analysis, Oberpfaffenhofen, 82234 Wessling, Germany.

Sensors (Basel, Switzerland)

|March 11, 2020

Summary

This summary is machine-generated.

A new Python-based software, PACO, performs atmospheric correction for satellite images from Sentinel-2, Landsat-8, and hyperspectral sensors. Validation confirms its accuracy for generating surface reflectance products.

Keywords:

DESIS Landsat-8 Sentinel-2 aerosol optical thickness atmospheric correction remote sensing surface reflectance water vapor

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Area of Science:

Earth and Planetary Sciences
Remote Sensing
Atmospheric Science

Background:

Atmospheric correction is crucial for accurate satellite image analysis.
The ATCOR software package is a well-established tool for this purpose.
New hyperspectral sensors require advanced processing capabilities.

Purpose of the Study:

To introduce PACO, a novel Python-based atmospheric correction software.
To enable processing of data from Sentinel-2, Landsat-8, and hyperspectral sensors (DESIS, EnMAP).
To validate PACO's performance using in situ data.

Main Methods:

Development of PACO based on the ATCOR algorithm.
Application of PACO to Sentinel-2 L1C data.
Comparison of PACO-generated L2A products with AERONET and RadCalNet in situ data.

Main Results:

PACO successfully generates Level 2A (L2A) products.
Validation shows good agreement between PACO products and in situ measurements.
The software covers the VNIR-SWIR spectral range.

Conclusions:

PACO is a versatile and accurate tool for atmospheric correction.
It supports a range of optical and hyperspectral satellite sensors.
PACO facilitates reliable surface reflectance retrieval for remote sensing applications.