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

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.

[1]  Michael D. King,et al.  Aerosol properties over bright-reflecting source regions , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[2]  L. Remer,et al.  The Collection 6 MODIS aerosol products over land and ocean , 2013 .

[3]  Qingyang Xiao,et al.  Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia , 2015 .

[4]  Muhammad Bilal,et al.  Validation of MODIS 3 km Resolution Aerosol Optical Depth Retrievals Over Asia , 2016, Remote. Sens..

[5]  Muhammad Bilal,et al.  Validation of Aqua-MODIS C051 and C006 Operational Aerosol Products Using AERONET Measurements Over Pakistan , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Muhammad Bilal,et al.  Evaluation of MODIS aerosol retrieval algorithms over the Beijing‐Tianjin‐Hebei region during low to very high pollution events , 2015 .

[7]  Muhammad Bilal,et al.  A simplified high resolution MODIS aerosol retrieval algorithm (SARA) for use over mixed surfaces , 2013 .

[8]  Jin Huang,et al.  Enhanced Deep Blue aerosol retrieval algorithm: The second generation , 2013 .

[9]  T. Eck,et al.  An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET , 2001 .

[10]  Lorraine Remer,et al.  The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol , 1997, IEEE Trans. Geosci. Remote. Sens..

[11]  Alexander Smirnov,et al.  Cloud-Screening and Quality Control Algorithms for the AERONET Database , 2000 .

[12]  Liangfu Chen,et al.  Comparison and evaluation of the MODIS Collection 6 aerosol data in China , 2015 .

[13]  LI Chengcai,et al.  Validation of MODIS derived aerosol optical depth over the Yangtze River Delta in China , 2010 .

[14]  Michael D. King,et al.  Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[15]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[16]  Robert C. Levy,et al.  MODIS Collection 6 aerosol products: Comparison between Aqua's e‐Deep Blue, Dark Target, and “merged” data sets, and usage recommendations , 2014 .

[17]  Andrew M. Sayer,et al.  Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data , 2013 .

[18]  Muhammad Bilal,et al.  Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms , 2014 .