Documentation and user guides for the Deep Blue data sets, along with links to the data sets themselves, can be found on the Data tab of this site.
A series of webinars was put together around the time of the MODIS Collection 6 data product suite release, to familiarize existing data users with updates to the data sets, and to provide information for new users. These were recorded and can be viewed, together with slide show packages, via the AEROCENTER website; those specific to the Deep Blue aerosol products can be viewed here and here. NASA's Applied Remote Sensing Training (ARSET) page also lists opportunities to register for in-person and online data use training, and provides resources including recordings of previous training webinars.
The focus of the table below is on papers involving the Deep Blue team concerning algorithm development, validation, and intercomparisons, as opposed to investigative applications of the data by other researchers. They are organized by year of publication, and then alphabetically by first author surname within a given year, and include a summary of what the paper is about.
What is it about?
Sayer, A. M., N. C. Hsu, C. Bettenhausen, J. Lee, W. V. Kim, and A. Smirnov (2018), Satellite Ocean Aerosol (SOAR) Algorithm Extension to S-NPP VIIRS as Part of the “Deep Blue” Aerosol Project, J. Geophys. Res. Atmos., 123, doi:10.1002/2017JD027412.
This study describes updates to the SOAR over-ocean aerosol retrieval algorithm, and performs validation against Maritime Aerosol Network observations. This paper describes the algorithm used for ocean retrievals in the version 1 VIIRS Deep Blue data set.
Hsu, N. C., J. Lee, A. M. Sayer, N. Carletta, S.-H. Chen, C. J. Tucker, B. N. Holben, and S.-C. Tsay (2017), Retrieving near-global aerosol loading over land and ocean from AVHRR, J. Geophys. Res. Atmos., 122, doi:10.1002/2017JD026932.
This paper describes the modification of our Deep Blue (over-land) and SOAR (over-water) aerosol retrieval algorithms for application to AVHRR satellite measurements.
This study was selected as a Research Spotlight by AGU, featured in EOS here.
Lee, J., N. C. Hsu, A. M. Sayer, C. Bettenhausen, and p. Yang (2017), AERONET-based nonspherical dust optical models and effects on the VIIRS Deep Blue/SOAR over water aerosol product. J. Geophys. Res. Atmos., 122. doi:10.1002/2017JD027258.
This paper describes new nonspherical dust aerosol models, and illustrates the effect of implementing them in the SOAR VIIRS algorithm. Note the same models are also used in our SOAR AVHRR data. It includes further examples and quantification of the well-known biases which can result if spherical dust is instead assumed.
Sayer, A. M., N. C. Hsu, J. Lee, N. Carletta, S.-H. Chen, and A. Smirnov (2017), Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements, J. Geophys. Res. Atmos., 122, doi:10.1002/2017JD026934.
This study evaluates our new AVHRR Deep Blue/SOAR demonstration aerosol data set by comparison against AERONET, Maritime Aerosol Network, and other satellite aerosol products.
Sayer, A. M., N. C. Hsu, C. Bettenhausen, R. E. Holz, J. Lee, G. Quinn, and P. Veglio (2017), Cross-calibration of S-NPP VIIRS moderate-resolution reflective solar bands against MODIS Aqua over dark water scenes, Atmos. Meas. Tech., 10, 1425-1444, doi:10.5194/amt-10-1425-2017.
The standard calibration of S-NPP VIIRS's reflective solar bands is thought to be biased at the present time. This study performs a cross-calibration against MODIS (Aqua), which makes the two radiometrically-consistent, and decreases the errors in the SOAR aerosol retrieval over ocean. This is an important step toward multi-sensor data consistency.
The cross-calibration results described in this study will be applied in our Deep Blue/SOAR VIIRS data processing.
Lee, J., N. C. Hsu, C. Bettenhausen, A. M. Sayer, C. J. Seftor, M.-J. Jeong, S.-C. Tsay, E. J. Welton, S.-H. Wang, and W,-N. Chen (2016), Evaluating the Height of Biomass Burning Smoke Aerosols Retrieved from Synergistic Use of Multiple Satellite Sensors over Southeast Asia, AAQR 16(11), 2831-2842, doi:10.4209/aaqr.2015.08.0506.
Case studies of the aerosol single-scattering albedo and height estimation (ASHE) algorithm, making use of the validation data available during springtime field campaign studies in South-East Asia.
Sayer, A. M., N. C. Hsu, T.-C. Hsiao, P. Pantina, F. Kuo, C.-F. Ou-Yang, B. N. Holben, S. Janjai, S. Chantara, S.-H. Wang, A. M. Loftus, N.-H. Lin, and S-C. Tsay (2016), In-Situ and Remotely-Sensed Observations of Biomass Burning Aerosols at Doi Ang Khang, Thailand during 7-SEAS/BASELInE 2015, AAQR 16(11), doi:10.4209/aaqr.2015.08.0500.
Evaluation of Collection 6 MODIS Deep Blue data at the mountain site of Doi Ang Khang, Thailand during the spring 2015 biomass burning season. Deep Blue data are also used in combination with other remote sensing and in situ data for analysis of mass, chemical, and optical properties related to biomass burning emissions.
Sayer A. M., N. C. Hsu, C. Bettenhausen, J. Lee, J. Redemann, B. Schmid, and Y. Shinozuka (2016), Extending “Deep Blue” aerosol retrieval coverage to cases of absorbing aerosols above clouds: Sensitivity analysis and first case studies, J. Geophys. Res. Atmos., 121,doi:10.1002/2015JD024729.
Algorithm for the retrieval of properties of absorbing aerosols above clouds (AACs), such as smoke and dust, from sensors like SeaWiFS, MODIS, and VIIRS. The paper describes the theory, simulations, and demonstration case studies with validation data from the Ames Airborne Tracking Sunphotmeter (AATS). The paper serves as a basis for an algorithm which will eventually be included in the Deep Blue data product suites.
Lee, J., N. C. Hsu, C. Bettenhausen, A. M. Sayer, C. J. Seftor, and M.-J. Jeong (2015), Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations, J. Geophys. Res. Atmos., 120, 8372–8388, doi:10.1002/2015JD023567.
Extension of the aerosol single-scattering albedo and height estimation (ASHE) algorithm as applied to VIIRS, OMPS, and CALIOP observations to nonspherical dust aerosols, as well as smoke aerosols.
The paper includes several updates made to the algorithm, case studies, and extensive uncertainty estimates.
Sayer, A. M., N. C. Hsu, C. Bettenhausen, M.-J. Jeong, and G. Meister (2015), Effect of MODIS Terra radiometric calibration improvements on Collection 6 Deep Blue aerosol products: Validation and Terra/Aqua consistency, J. Geophys. Res. Atmos., 120, doi:10.1002/2015JD023878.
Description of the calibration improvements made to MODIS Terra data included in the Collection 6 reprocessing of the Deep Blue aerosol products.
Terra data are stable over the mission to date to within a precision of approximately 0.01 in AOD, and show strong stability, and global and regional consistency, when compared to the Aqua record. This relies on the extensive efforts of the MCST and OBPG in improving and maintaining the high radiometric quality of the source MODIS data.
Sayer, A. M., N. C. Hsu, and C. Bettenhausen (2015), Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation, Atmos. Meas. Tech., 8, 5277-5288, doi:10.5194/amt-8-5277-2015.
Description and illustration of the effects that the `bow-tie distortion', which is an across-track sampling distortion due to the scan geometry of MODIS and the curvature of the Earth's surface, has on AOD retrievals using Deep Blue and other algorithms. The paper also provides some potential techniques for the mitigation of this distortion's effects on these data sets.
This issue is becoming more relevant with the application of Deep Blue and other algorithms to multiple sensors, such as VIIRS, which suffer different (and generally smaller) across-track distortions. It affects the spatial characteristics of the data sets differently, which has implications for data set continuity and comparability.
Sayer, A. M., L. A. Munchak, N. C. Hsu, R. C. Levy, C. Bettenhausen, and M.-J. Jeong (2014), MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations, J. Geophys. Res. Atmos., 119, 13,965–13,989, doi:10.1002/2014JD022453
Validation and three-way comparison between MODIS Collection 6 Deep Blue, Dark Target, and AERONET data. Also describes the construction of, and validates, the 'merged' Deep Blue/Dark Target data set provided within Collection 6 data products. Results for MODIS Aqua only.
Banks, J.R., H. E. Brindley, C. Flamant, M. J. Garay, N. C. Hsu, O. V. Kalashnikov, L. Kluser, and A. M. Sayer (2013), Intercomparison of satellite dust retrieval products over the west African Sahara during the Fennec campaign in June 2011. Remote Sensing of Environment, 136, 99-116, doi:10.1016/j.rse.2013.05.003
Intercomparison of validation of satellite AOD data products, including MODIS Deep Blue, over the Sahara during the Fennec field campaign.
The analyses in this work involve Deep Blue data for both Collection 5 and 6, so provide one example of the differences between the two data versions in this region.
Huang, J., N. C. Hsu, S.-C. Tsay, Z. Liu, M.-J. Jeong, R. A. Hansell, and J. Lee (2013), Use of spaceborne lidar for the evaluation of thin cirrus contamination and screening in the Aqua MODIS Collection 5 aerosol products, J. Geophys. Res., 118 (12), 6444-6453, doi:10.1002/jgrd.50504
Enhanced cirrus cloud screening techniques developed in this paper are applied in MODIS Collection 6 and VIIRS data processing.
Hsu, N. C., M.-J. Jeong, C. Bettenhausen, A. M. Sayer, R. Hansell, C. S. Seftor, J. Huang, and S.-C. Tsay (2013), Enhanced Deep Blue aerosol retrieval algorithm: The second generation, J. Geophys. Res. Atmos., 118, 9296–9315, doi:10.1002/jgrd.50712
Description for the enhanced Deep Blue algorithm as applied to MODIS Collection 6 and SeaWiFS (versions 3, 4) over-land data sets. This includes both updates to the original (Hsu et al. 2004, 2006) surface treatment over bright surfaces (e.g. deserts), and additionally a description of the new surface model used over darker land surfaces (e.g. vegetated areas).
Levy, R. C., S. Mattoo, L. A. Munchak, L. A. Remer, A. M. Sayer, F. Patadia, and N. C. Hsu (2013), The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989-3034, doi:10.5194/amt-6-2989-2013
This paper focuses on updates to the Dark Target land and ocean AOD algorithms for MODIS Collection 6. However, it also contains a brief description of the 'merged' Deep Blue/Dark Target data set included within these files, and updates to the Level 3 aggregation strategy adopted in MODIS Atmospheres Collection 6 processing.
Sayer, A. M., N. C. Hsu, C. Bettenhausen, and M.-J. Jeong (2013), Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data, J. Geophys. Res. Atmos., 118, 7864–7872, doi:10.1002/jgrd.50600
Validation of MODIS Deep Blue Collection 6 aerosol retrievals against AERONET. Description of how the uncertainty estimates for AOD at 550 nm provided within Collection 6 data products were generated. Note that the discussion refers to MODIS Aqua only, although the same method was applied to MODIS Terra data as well.
Shi, Y., J. Zhang, J. S. Reid, E. J. Hyer, and N. C. Hsu (2013), Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa, Atmos. Meas. Tech., 6(4), 949-969, doi:10.5194/amt-6-949-2013
Validation and bias correction methodology for MODIS Deep Blue data over North Africa. The analyses focuses on Collection 5 data, although some Collection 6 results are also shown.
Carboni, E., G. E. Thomas, A. M. Sayer, R. Siddans, C. A. Poulsen, R. G. Grainger, C. Ahn, D. Antoine, S. Bevan, R. Braak, H. Brindley, S. DeSouza-Machado, J. L. Deuzé, D. Diner, F. Ducos, W. Grey, C. Hsu, O. V. Kalashnikova, R. Kahn, P. R. J. North, C. Salustro, A. Smith, D. Tanré, O. Torres, and B. Veihelmann (2012), Intercomparison of desert dust optical depth from satellite measurements, Atmos. Meas. Tech., 5, 1973-2002, doi:10.5194/amt-5-1973-2012
Multi-sensor intercomparison and validation of satellite AOD products for Saharan dust during March 2006.
Hsu, N. C., R. Gautam, A. M. Sayer, C. Bettenhausen, C. Li, M.-J. Jeong, S.-C. Tsay, and B. N. Holben (2012), Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010, Atmos. Chem. Phys., 12, 8037-8053, doi:10.5194/acp-12-8037-2012
AOD trend analysis using the SeaWiFS Deep Blue (land and ocean) version 3 data set.
Sayer, A. M., N. C. Hsu, C. Bettenhausen, Z. Ahmad, B. N. Holben, A. Smirnov, G. E. Thomas, and J. Zhang (2012), SeaWiFS Ocean Aerosol Retrieval (SOAR): Algorithm, validation, and comparison with other data sets, J. Geophys. Res., 117, D03206, doi:10.1029/2011JD016599
Covers the SeaWiFS over-ocean aerosol retrieval as applied in version 3 of the SeaWiFS Deep Blue data set. Contains an algorithm description, validation against AERONET and the Maritime Aerosol Network, and comparison against MODIS, MISR, AATSR, and MERIS data.
Note that the algorithm applied over ocean in the version 4 SeaWiFS data set is essentially the same as this, albeit with some additional tests to identify and remove turbid water pixels, as the version 3 tests did not perform well in some situations.
Sayer, A. M. N. C. Hsu, C. Bettenhausen, M.-J. Jeong, B. N. Holben, and J. Zhang (2012), Global and regional evaluation of over-land spectral aerosol optical depth retrievals from SeaWiFS, Atmos. Meas. Tech., 5, 1761-1778, doi:10.5194/amt-5-1761-2012
Validation of the over-land version 3 SeaWiFS Deep Blue data set against AERONET. Comparison against MODIS and MISR aerosol products.
Jeong, M.-J., N. C. Hsu, E. J. Kwiatkowska, B. A. Franz, G. Meister, and C. Salustro (2011), Impacts of Cross-Platform Vicarious Calibration on the Deep Blue Aerosol Retrievals for Moderate Resolution Imaging Spectroradiometer Aboard Terra, IEEE Trans. Geosci. Remote Sens., 49 (12), 4877-4888, doi:10.1109/TGRS.2011.2153205
Describes the application of the vicarious calibration correction to MODIS Terra data in the Collection 5 Deep Blue data set. The calibration methodology was developed by the Ocean Biology Processing Group at NASA GSFC. These corrections account for residual calibration errors in band gains, detector response vs. scan angle (RVS), and polarization sensitivity of the sensor.
Note that a similar, updated methodology is also applied to MODIS Terra measurements for Collection 6.
Jeong, M.-J., and N. C. Hsu (2008), Retrievals of aerosol single-scattering albedo and effective aerosol layer height for biomass-burning smoke: Synergy derived from ‘‘A-Train’’ sensors, Geophys. Res. Lett., 35, L24801, doi:10.1029/2008GL036279
Retrieval of aerosol single-scattering albedo and height information from a combined use of MODIS, OMI, and CALIOP observations. The algorithm provides the height of smoke aerosols over broad areas, which were not previously available from single passive sensors.
Hansell, R. A., S. C. Ou, K.-N. Liou, J. K. Roskovensku, S.-C. Tsay, C. Hsu, and Q. Ji (2007), Simultaneous detection/separation of mineral dust and cirrus clouds using MODIS thermal infrared window data, Geophys. Res. Lett., 34 (11), L11808, doi:10.1029/2007GL029388
Describes the D* test, one of the methods used to separate mineral dust and cirrus clouds in the Deep Blue algorithm. This test is not applied in the SeaWiFS version of the algorithm as that sensor lacks thermal infrared bands.
Hsu, N. C., S.-C. Tsay, M. D. King, and J. R. Herman (2006), Deep Blue retrievals of Asian aerosol properties during ACE-Asia, IEEE Trans. Geosci. Remote Sens., 44, 3180–3195, doi:10.1109/TGRS.2006.879540
Use of the Deep Blue algorithm, applied to SeaWiFS and MODIS Terra data, to investigate aerosol properties during the ACE-Asia field campaign (spring 2001).
Hsu, N. C., S. -C. Tsay, M. D. King, and J. R. Herman (2004), Aerosol properties over bright-reflecting source regions, IEEE Trans. Geosci. Remote Sens., 42(3), 557–569, doi:10.1109/TGRS.2004.824067
Original Deep Blue algorithm paper, providing an overview and example application to MODIS data over northern Africa and the Arabian Peninsula.