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Advancements in Aerosol Remote Sensing: MERIS and MODIS Contributions

This document explores the development and future of aerosol remote sensing using MERIS and MODIS data. It discusses recommendations from the October 2003 MAVT workshop, including increasing aerosol model numbers and expanding product spatial coverage. The relationship between surface reflectance and spectral indices like ARVI is analyzed, alongside seasonal biome transitions based on Terra/MODIS surface albedo data. Key topics include aerosol product performance, retrieval challenges, and future directions for improving surface albedo maps and validating aerosol products.

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Advancements in Aerosol Remote Sensing: MERIS and MODIS Contributions

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  1. Ramon, D.1 and Santer, R.2 HYGEOS, France LISE, Université du Littoral, France Aerosol over land with MERIS, present and future

  2. Where did we start after Salzburg • MERIS level 2 products • Reprocessing #2 after MAVT Workshop oct. 2003 recommendations • Increase aerosol model number (Angstrom coeff. from 0 to 2.5 by step of 0.1) • Extend the aerosol product spatial cover • From Dense Dark Vegetation to Land Aerosol Remote Sensing target

  3. LARS concept • Surface reflectance varies linearly with some spectral index, here ARVI 100x100 RR around Alta Floresta DARVI=0 DARVI=-0.2 DARVI=-0.5

  4. LARS Reflectance for a biome slope Initial DDV Domain Seasonal Initial DDV

  5. Biomes

  6. Biome transition on level 3

  7. Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data • Operational MODIS surface albedo data product (MOD43B3/MCD43B3) • 0.47, 0.56, 0.67, 0.86, 1.24, 1.64, 2.1 µm , 0.3-5.0, 0.7-5.0, 0.3-0.7 µm • 1 km spatial resolution (sinusoidal projection stored as 10° x 10° tiles) • 16-day periodicity (001, 017, …, 353) Michael D. King1, Eric G. Moody1,2, Steven Platnick1, and Crystal Schaaf3 1NASA Goddard Space Flight Center 2L3-Communications GSI 3Boston University

  8. 16 days period starting from julian day r0(l1) 12'x12' box fit goodness of fit

  9. 12' resolution, day 177, slope at 659 nm Black: no fit White: no good fit

  10. 12' resolution, day 177, albedo at 659 nm for ARVI=0.6 0.08 0

  11. C443 1.5 0

  12. C670 1.5 0

  13. New RO_LARS LUTs from MODIS • Aerosol product: AOT_865 and alpha

  14. Remaining issues? • See biome transitions on AOT_865 • Lot of alpha out of range

  15. Flags Clouds(White); water (Black),LARS with good aerosol retrieval(Green); Bad aerosol retrieval (Red) LARS in cloud vicinity(Blue)

  16. AOT_865

  17. RO_LARS_670 DDV albedo (red) corrected LARS albedo (magenta)

  18. Rayleigh corrected reflectance above LARS at 443nm

  19. Rayleigh corrected reflectance above LARS at 670 nm

  20. What aerosol product? RO_AER_440 is correct but improvements are possible RO_AER_670 is questionable Laerosol _440 is OK AOT_440 instead of AOT_865 (no compromise at 670 nm) What about a and the associated Junge size distribution

  21. The 3 possible algorithms • Simultaneous retrieval of AOT and alpha • AOT for model no 36 (a=1) • Use AOT for model no 36 as back up only

  22. Spatial & temporal binning (nov.)

  23. Spatial & temporal binning (sept.)

  24. Conclusion • Separate AOT retrieval and alpha retrieval • Coherence with AOT over water • No real biome transition found, but need to consolidate with L3 • Better cloud rejection at level 2 is mandatory • Less problem with a better surface albedo map !

  25. What next? • How to make a good surface albedo map for dark pixels? • Utility of a level 2 aerosol product? • On a pixel by pixel basis? • A daily level 3 which is not a simple bining • Utility of a daily level 3 aerosol product? • Validation • Atmospheric correction • Upper level

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