Evaluation of the NDVI-Based Pixel Selection Criteria of the MODIS C6 Dark Target and Deep Blue Combined Aerosol Product

Muhammad Bilal, Janet Elizabeth Nichol

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)

Abstract

The moderate resolution and imaging spectroradiometer (MODIS) Collection 6 (C6) level 2 operational aerosol product (MOD04) contains the Dark Target (DT) and Deep Blue (DB) combined aerosol optical depth (AOD) observations (DTB) at 10 km resolution, which is generated using the selection criteria based on the static normalized difference vegetation index (NDVI) as follows: 1) the DT AOD data are used for NDVI > 0.3; 2) the DB AOD data are used for NDVI < 0.2; and 3) the average of both algorithms or AOD data with highest quality flag are used for ≤ 0.2 NDVI ≤ 0.3. The objective of this study is to evaluate the NDVI pixel selection criteria used in the DTB AOD product. For this, the DT, the DB, and the DTB AOD retrievals are evaluated using the Aerosol Robotic Network (AERONET) level 2.0 cloud-screened and quality-controlled AOD data over Beijing from 2002 to 2014, Lahore from 2007 to 2013, and Paris from 2005 to 2014. The DT and DB AOD retrievals considered by the DTB product are tabulated. For comparison purposes, the MODIS level 3 monthly NDVI product (MOD13A3) at 1 km resolution is also tabulated indicating how the NDVI-based pixel selection criteria operate for the DT and DB AOD retrievals used in the DTB product. Results show that the DT AOD retrievals for NDVI ≤ 0.3 are used in the DTB product, and this increases the mean bias and percentage of retrievals above the expected error. These results conclude that the DTB AOD product must follow the dynamic NDVI values for pixel selection criteria.
Original languageEnglish
Article number7911188
Pages (from-to)3448-3453
Number of pages6
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume10
Issue number8
DOIs
Publication statusPublished - 1 Aug 2017

Keywords

  • Aerosol Robotic Network (AERONET)
  • Beijing
  • Dark Target (DT)
  • Deep Blue (DB)
  • Lahore
  • MOD04 C6
  • Paris

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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