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QUantitative and Automatic Atmospheric Correction (QUAAC): Application and Validation.

Shumin Liu1, Yunli Zhang1,2, Limin Zhao2

  • 1School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China.

Sensors (Basel, Switzerland)
|May 20, 2022
PubMed
Summary
This summary is machine-generated.

A new QUantitative and Automatic Atmospheric Correction (QUAAC) method uses geostationary satellite aerosol data for efficient atmospheric correction of high-spatial-resolution images. This automated approach improves surface reflectance accuracy compared to existing methods.

Keywords:
FLAASHaerosol optical thicknessatmospheric correctionradiative transfer modelsurface reflectance

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Area of Science:

  • Remote Sensing
  • Atmospheric Science
  • Geospatial Analysis

Background:

  • Atmospheric correction of satellite imagery is crucial for accurate surface reflectance retrieval.
  • Acquiring synchronized atmospheric parameters, particularly aerosol optical depth (AOD), remains a significant challenge for radiative transfer model-based methods.
  • Existing methods lack full automation and high efficiency for large-scale atmospheric monitoring using geostationary satellites.

Purpose of the Study:

  • To develop a fully automatic and efficient atmospheric correction method for high-spatial-resolution (HSR) satellite images.
  • To leverage geostationary satellite data for improved atmospheric parameter retrieval, specifically AOD.
  • To enhance the accuracy and reliability of surface reflectance (SR) products.

Main Methods:

  • Proposed a QUantitative and Automatic Atmospheric Correction (QUAAC) method.
  • Utilized geostationary satellite aerosol products to obtain synchronized AOD based on HSR image temporal and spatial information.
  • Integrated obtained atmospheric parameters into the correction process for SR retrieval.

Main Results:

  • The QUAAC method demonstrated efficient atmospheric correction for HSR satellite images.
  • QUAAC achieved fully automatic operation without manual intervention.
  • QUAAC-corrected surface spectral curves showed good coherence with field measurements, outperforming FLAASH in some aspects.

Conclusions:

  • The developed QUAAC method effectively addresses the challenge of acquiring accurate AOD for atmospheric correction.
  • QUAAC offers a fully automatic and efficient solution for processing HSR satellite imagery.
  • The method shows promise for large-scale atmospheric monitoring and accurate surface reflectance estimation.