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Global evaluation and uncertainty analysis of MODIS Deep Blue and MAIAC aerosol products over bright surfaces

  • Ping Zhang
  • , Leiku Yang*
  • , Xin Pei
  • , Muhammad Bilal
  • , Yuxuan Wang
  • , Yizhe Fan
  • , Xiaoran Lv
  • , Xiaoxia Xue
  • , Weiqian Ji
  • , Xiaofeng Lu
  • , Xiaoqian Cheng
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides aerosol optical depth (AOD) over bright surfaces using the Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. However, a systematic long-term comparison of the two AOD products over global bright-surface regions is still limited. Therefore, this study presents a comprehensive evaluation and uncertainty analysis of the MODIS DB Collection 6.1 (C6.1) and MAIAC C6.1 aerosol products using ground-based AOD measurements from AERONET/CARSNET sites during 2002–2024. The analysis focuses on seven major bright-surface regions worldwide, including North Africa, Southern Africa, the Middle East, Australia, Northwestern China, Western North America, and Western South America. Results indicate that both products show good correlation with ground-based measurements and generally meet the expected error accuracy criteria. However, they exhibit systematic underestimations, with mean biases of −0.014 for DB and −0.042 for MAIAC. The underestimation of MAIAC product is primarily attributable to Northwestern China, North Africa, and the Middle East. The underestimation of DB product mainly originates from Northwestern China, with a certain degree of overestimation under low-AOD conditions over the Middle East and North Africa. Error analysis further indicates that retrieval uncertainty increases under conditions of medium to high aerosol loading, coarse aerosol particle size, low NDVI, high surface reflectance, and large scattering angles. This increased uncertainty may stem from algorithmic limitations, such as inaccurate assumptions about aerosol types and surface reflectance estimation, as well as the reduced sensitivity of top-of-atmosphere reflectance to AOD variations over high-reflectivity surfaces. This study provides valuable insights into the selection of DB and MAIAC products and potential algorithm improvements for applications over bright surfaces, such as deserts.

Original languageEnglish
Article number121991
JournalAtmospheric Environment
Volume375
DOIs
StatePublished - 15 Jun 2026

Bibliographical note

Publisher Copyright:
© 2026 Elsevier Ltd

Keywords

  • AOD
  • Bright surface
  • DB
  • MAIAC
  • Uncertainty analysis
  • Validation

ASJC Scopus subject areas

  • General Environmental Science
  • Atmospheric Science

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