Task 234

Aerosol Remote Sensing

Principal Investigator(s):

M. Petrenko


C. Ichoku

Last Updated:

October 26, 2012 15:26:00

Description of Problem

The effects of atmospheric aerosols on the air quality, the hydrological cycle, and climate are still poorly understood. During the past decade, there have been increased efforts to employ satellite remote-sensing approaches in measuring aerosols in order to complement measurements from ground-based systems. However, because of the differences in the sensor measurement characteristics and algorithms used for aerosol retrievals, the products are often inconsistent, making it difficult to derive objective measures of aerosol amounts and properties. Therefore, it has become necessary to conduct integrated analysis of aerosol measurements acquired with different types of instrumentation, in order to narrow down the uncertainties that delay improvements in the knowledge of the different aerosol impacts. The purpose of this project is to provide an approach and a unified framework for inter-comparison and validation of aerosol measurements from different sensors and instruments, including ground-based, airborne, and spaceborne, obtained at different locations and time around the globe.

Scientific Objectives and Approach

A Multi-sensor Aerosol Products Sampling System (MAPSS) software and supporting database have been established as a consensus data framework for multi-sensor aerosol validation, inter-comparison, and joint-analysis. A simple web-based service was developed for rapid and efficient access to the database. In addition, a web portal was established to provide for a fast and convenient access to the database, as well as for a customized on-demand retrieval of the data from this database (http://giovanni.gsfc.nasa.gov/mapss/). As of the beginning of the reported phase of the project, MAPSS supported data derived from the AERONET (Aerosol Robotic Network), MODIS (Moderate-resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging Spectroradiometer), OMI (Ozone Monitoring Instrument), and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) sensors.


During the reporting period of March 1, 2011 – February 29, 2012, the MAPSS project has been expanded to include aerosol measurements from SeaWiFS (Sea-viewing Wide Field-of-view Sensor) spaceborne sensor and MAN (Maritime Aerosol Network) network of sensors deployed onboard ocean-bound ships. SeaWiFS provides one of the longest continuous aerosol datasets available, with an extensive coverage of deserts and other bright surfaces. On the other hand, MAN provides well-calibrated in-situ aerosol retrievals over ocean surfaces, complimenting the measurements from the network of ground-based AERONET sensors that are already supported in MAPSS. In this way, both datasets represent a valuable addition to the MAPSS framework, especially since these datasets provide aerosol measurements over regions, where retrievals from other sensors are either scarce, or uncertain.

In addition to supporting these two products, we updated MAPSS to support the recently released new version of the OMI aerosol product. We also collaborated with other research groups to adapt MAPSS for the validation of the novel aerosol products that are currently being developed by these groups. These include the new 3-km aerosol product from the MODIS sensors, aerosol product from the recently launched VIIRS (The Visible Infrared Imager Radiometer Suite) sensoronboard of the NPP satellite, and an experimental Absorption Aerosol Optical Depth and Single Scattering Albedo product from the MODIS sensors.

Furthermore, we continued improving and expanding the WEB interface for interactive plotting and retrieval of MAPSS data. During the reporting period, we have implemented support for interactive Quality Assurance (QA) screening of the data, and have introduced a support for on-line generation of customizable scatter plots (currently, available at http://giovanni.gsfc.nasa.gov/aerostat/). We have also greatly simplified the interface of the tool, in order to make it more accessible to novice users.

Based on the MAPSS data, we have conducted a series of initial studies into the issues of data quality and other sources of uncertainty in aerosol products retrieved by spaceborne sensors. The studies revealed important differences that exist in data quality handling approaches, supported in these products. We are working on a more detailed study that will further characterize these differences, enabling a synergetic use of the investigated products.

Refereed Journal Publications

M. Petrenko, C. Ichoku, and G. Leptoukh, Multi-sensor Aerosol Products Sampling System (MAPSS), in Atmospheric Measurement Techniques Discussions (AMTD), 5, 909-945, doi:10.5194/amtd-5-909-2012, 2012.

Other Publications and Conferences

Conference Presentations:
Maksym Petrenko, Charles Ichoku, Gregory Leptoukh, Effects of Data Quality on the Characterization of Aerosol Properties from Multiple Sensors, 2011 AGU Fall Meeting, 5–9 December 2011, Moscone Convention Center, San Francisco, California, USA.

Arif Albayrak, Jennifer Wei, Maksym Petrenko, and Gregory Leptoukh, MODIS Aerosol Optical Depth Bias Adjustment using Machine Learning Algorithms, 2011 AGU Fall Meeting, 5–9 December 2011, Moscone Convention Center, San Francisco, California, USA.

Jennifer Wei, Maksym Petrenko, Gregory Leptoukh, Chris Lyness, Mahabaleshwara Hegde, and Charles Ichoku, NASA Giovanni Tool for Statistical Intercomparison of Aerosols: AeroStat, 2011 AGU Fall Meeting, 5–9 December 2011, Moscone Convention Center, San Francisco, California, USA.

Jacob Anderson, Jun Wang, Maksym Petrenko, Gregory Leptoukh, Charles Ichoku, Evaluating MODIS Aerosol Data Quality with AERONET for Coastal Regions, 2011 AGU Fall Meeting, 5–9 December 2011, Moscone Convention Center, San Francisco, California, USA.

Multi-Sensor Aerosol Analysis, Climate & Radiation Laboratory Seminar Series, NASA Goddard Space Flight Center, November 16, 2011. Presented by Maksym Petrenko.

Climate and Radiation Laboratory (Code 613), NASA/GSFC: Scientific Leadership Award, January 5, 2012. Presented to Maksym Petrenko.