Dr. Hai Zhang Profile

Dr. Hai Zhang

at I M Systems Group Inc

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SPIE Journal Paper | 8 October 2018 Open Access

Evaluation of VIIRS dust detection algorithms over land

Hai Zhang, Pubu Ciren, Shobha Kondragunta, Istvan Laszlo

JARS, Vol. 12, Issue 04, 042609, (October 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.042609

KEYWORDS: Detection and tracking algorithms, RGB color model, Aerosols, Evolutionary algorithms, Algorithm development, Reflectivity, Infrared imaging, Satellites, Atmospheric modeling, Reflection

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This paper compares three dust detection algorithms over land that were developed for operational, near-real-time processing using the Suomi National Polar Orbiting Partnership Visible Infrared Imaging Radiometer Suite instrument. The three algorithm approaches use different spectral bands, namely deep blue bands, infrared (IR)-visible bands, and IR bands, and are applied for dust observed over dark as well as bright surfaces. The evaluations are performed both using case studies and AERONET matchup data over western CONUS-Mexico region and North Africa-Arabian Peninsula region. The deep blue-based algorithm is found to have the most false detections and its detection performance depends on the Sun-satellite geometries. Simulation analysis shows that there are three causes of this problem: surface reflectance, air mass factors, and phase functions in different geometries. The algorithm based on IR-visible bands has much less false detection than the deep blue bands-based algorithm and has better true positive detection than the IR-based algorithm. The IR bands-based algorithm performs well in the case studies over CONUS–Mexico region, but it fails to detect most of the dust cases over North Africa–Arabian Peninsula region. The results suggest that the IR-visible algorithm is the most suitable for the dust detection of the three algorithms with a small modification. Because the IR-visible algorithm is not able to detect all the dust pixels, detections from the deep blue algorithm only and those from the IR-visible algorithm with relaxed criteria are also provided but are distinguished with a lower quality.

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Proceedings Article | 12 August 2009 Paper

Development of IDEA product for GOES-R aerosol data

Hai Zhang, Raymond Hoff, Shobha Kondragunta

Proceedings Volume 7456, 74560O (2009) https://doi.org/10.1117/12.828095

KEYWORDS: MODIS, Aerosols, Satellites, Artificial intelligence, Atmospheric modeling, Atmospheric particles, Satellite imaging, Clouds, Data modeling, Reflectivity

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The NOAA GOES-R Advanced Baseline Imager (ABI) will have nearly the same capabilities as NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) to generate multi-wavelength retrievals of aerosol optical depth (AOD) with high temporal and spatial resolution, which can be used as a surrogate of surface particulate measurements such as PM2.5 (particulate matter with diameter less than 2.5 μm). To prepare for the launch of GOES-R and its application in the air quality forecasting, we have transferred and enhanced the Infusing satellite Data into Environmental Applications (IDEA) product from University of Wisconsin to NOAA NESDIS. IDEA was created through a NASA/EPA/NOAA cooperative effort. The enhanced IDEA product provides near-real-time imagery of AOD derived from multiple satellite sensors including MODIS Terra, MODIS Aqua, GOES EAST and GOES WEST imager. Air quality forecast guidance is produced through a trajectory model initiated at locations with high AOD retrievals and/or high aerosol index (AI) from OMI (Ozone Monitoring Instrument). The product is currently running at http://www.star.nesdis.noaa.gov/smcd/spb/aq/. The IDEA system will be tested using the GOES-R ABI proxy dataset, and will be ready to operate with GOES-R aerosol data when GOES-R is launched.

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