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Collection 6 Aerosol Products Becoming Available

Collection 6 Aerosol Products Becoming Available. C6 Aerosol Product Includes: MYD04_L2 Dark Target AOD at 10 km 2 Deep Blue AOD at 10 km 2 Deep-Dark Merged AOD MYD04_3K Dark Target AOD at 3 km 2 .

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Collection 6 Aerosol Products Becoming Available

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  1. Collection 6 Aerosol Products Becoming Available • C6 Aerosol Product Includes: • MYD04_L2 • Dark Target AOD at 10 km2 • Deep Blue AOD at 10 km2 • Deep-Dark Merged AOD • MYD04_3K • Dark Target AOD at 3 km2

  2. Urban Aerosol Retrieval in MODIS Dark Target Algorithm: Implications to Air Quality Monitoring Pawan Gupta1,2 , Rob Levy2, Shana Mattoo2,3, and Leigh Munchak2,3 1GESTAR Universities Space Research Associations 2 NASA Goddard Space Flight Center, Greenbelt, MD, USA 3SSAI Air Quality Applied Sciences Team 6th Semi-Annual Meeting (AQAST 6) January 15-17, 2014

  3. Motivation: Why Urban? • PM2.5 pollution levels in many mega cities exceeds the WHO standards by 5 to 10 times. • Satellite observed aerosol information has been increasingly in use for air quality monitoring efforts at local to regional to global scales. • MODIS Dark Target AOD validation studies have shown a bias over urban areas due to surface assumptions (Oo et al., 2010; Castanho et al., 2007, Munchak et al., 2013, Gupta et al., 2013) • Urban areas comprise 0.5% of the Earth’s surface, but will contain 2/3 of the Earth’s population by 2025, thus addressing urban bias in AOD is critical for obtaining accurate air quality information from space.

  4. City Center Appears as ‘HOT SPOT’ in MODIS DT AOD MODIS AOD Distance (deg) New Delhi, India Gupta et al., 2012

  5. Aerosol and Pollution in Mega Cities Mega cities appeared as hot-spots in MODIS AOD images with high gradient from center to outside the city area.

  6. Mean bias (MODIS 3 km - AERONET) averaged over the campaign duration at each AERONET location Percent of pixels identified as urban in same 15 km box around AERONET station Munchak et al., 2013 Land identified as urban by MODIS land cover product at 500 m resolution

  7. Surface Characterization in MODIS Dark Target (MDT) Retrieval • MDT assumes a relationship between the visible (VIS) and shortwave-IR (SWIR) surface reflectance, based on statistics of dark-target (primarily vegetated) surfaces. RVIS = f (RSWIR, Angles, NDVISWIR) • Over brighter and more variable surfaces (e.g. urban), the assumed VIS/SWIR relationship breaks down (Oo et al., 2010; Castanho et al., 2007) Levy et al., 2007, 2013

  8. Accounting for Urban Bias Here, we use MODIS Land surface product (“MOD09”, Vermote et al.) to derive a new VIS/SWIR surface relationship for urban areas where urban % > 20%. RVIS = f (RSWIR, Angles, NDVISWIR, Urban%) VIS/SWIR ratio versus Urban%

  9. Inter-Comparison with AERONET

  10. DISCOVER-AQ, Houston 3 km2 10 km2

  11. C6, C6_Urban C6 vs C6_Urban – Aqua, April 18, 2010 Chicago (0.41±0.14, 0.26±0.09) Washington DC (0.21±0.02,0.17±0.01) Atlanta (0.18±0.04, 0.15±0.03)

  12. Spring Time Reduction in AOD over Urban Regions

  13. Aerosol Retrieval Improvements over Large Urban Corridors of Eastern USA Spring 2010 Philadelphia / New York

  14. Aerosol Retrieval Improvements over Large Urban Corridors of Eastern USA Spring 2010 Washington DC / Baltimore

  15. Aerosol Retrieval Improvements over Large Urban Corridors of Eastern USA Spring 2010 Atlanta

  16. Implication to Surface PM Air Quality Ancillary Data MODEL Surface PM Satellite AOD Driving surface PM from column AOD measurements is challenging problem, having more reliable AOD over urban areas will improve PM estimation skills of statistical/physical models

  17. Summary • MODIS land surface reflectance and land cover classification data sets have been used to define a VIS/SWIR surface reflectance relationship to be used over urban surfaces (urban percentage > 20%). The standard C6 MODIS Dark-Target surface reflectance relationship was replaced. • Reduced AOD is seen over urban areas. Compared to AERONET observations, these new retrievals remove some of the high bias normally seen over large urban areas. Ongoing/Future Work • Evaluating the urban surface relationship over global cities, testing over longer time series. • Evaluating Aerosol Models used over Urban Areas in the DTA. • Implementing into the MODIS Dark Target Land algorithm? • Exploring impacts of new AOD retrieval on regional and global studies of air quality, PM2.5 and health

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