Sciences and Exploration Directorate

Yingxi Rona Shi

(RESEARCH SCIENTIST)

Yingxi Rona Shi's Contact Card & Information.
Email: yingxi.shi@nasa.gov
Phone: 301.614.5835
Org Code: 613
Address:
NASA/GSFC
Mail Code 613
Greenbelt, MD 20771
Employer: UNIVERSITY OF MARYLAND BALTIMORE CO

Brief Bio


As an atmospheric scientist, Dr. Shi's research is primarily concerned with observing the global aerosol distribution, determining its properties, and furthering our understanding of how aerosols affect the environment, air quality and climate. She is specifically most interested in advanced retrieval algorithms combining machine learning (ML)-based and physical-based for aerosol optical properties; synergistic aerosol products using multi-satellite platforms; application of satellite data for studies on wildfires; and long-range transport of pollutants and dust. Dr. Shi got her PhD in Dec. 2015 and joined GESTAR/USRA to work at NASA Goddard Space Flight Center (GSFC) Climate and Radiation Laboratory (Code 613) as a Post-doctoral Research Associate from 2016 to 2018, and then as an Assistant Research Scientist. She moved to Joint Center of Earth Technology (JCET)/University of Maryland Baltimore County (UMBC) at 2019 as an Assistant Research Scientist and became Associate Research Scientist in 2023.

Research Interests


Aerosol remote sensing

Earth Science: Remote Sensing

Using combined machine learning and physically based algorithms to retrieve aerosol properties such as loading, layer height, and absorptions. Multi-sensor/platforms aerosol synergistic retrievals and products integrations, including data/products from low earth orbit sensors and geostationary sensors.


Aerosol impacts on climate and ecosystem

Earth Science: Aerosols

Retrieve and understand biomass burning emissions properties to further quantify its impact on climate change as well as air qualities, especially over large wildfires sources. Evaluate atmospheric impacts on ocean nourishment using longterm observational and modeling aerosol and ocean biological data.


Artificial Intelligence and its application on Earth observations.

Earth Science: Remote Sensing

Develop explainable ML models to facilitate aerosol detection and property retrievals by combining active and passive sensors as well as polarimetry informations. Creating bias corrected aerosol integrated products by combining multi-aerosol products from geostationary and low earth orbit sensors.

Positions/Employment


Assistant Researcher

GEASTAR II / UMBC - B33 GSFC

June 2019 - March 2023


Associate Researcher

GESTAR II UMBC / GSFC - B33

March 2023 - Present

Professional Service


Aerocenter Committee 2016-2022

Proposal pannelist

Reviewers for multiple journals


Awards



2020-2023 NASA New (Early Career) Investigator Program

2020 Scientific Leadership Award, GSFC Climate and Radiation Laboratory

2017 Scientific Leadership Award, GSFC Climate and Radiation Laboratory

2016 Best Dissertation Award, Chinese-American Oceanic and Atmospheric Association (COAA)

2010-2013 NASA Earth and Space Science Fellowship (NESSF)

2010 Outstanding Student Paper Award, AGU 2010 Fall Meeting

2009 Outstanding Graduate Researcher Award, University of North Dakota

2000-2001 Academic Scholarship Recipient, Sun Yat-Sen University

 

Grants


Atmospheric Nourishment of Global Ocean Ecosystems on a Changing Planet 

NNH22ZDA001N-IDS - NASA - Awarded: 2023-09-21


Dates:  - 


Developing a Comprehensive and Augmented Multi-decadal Remote-sensing Observations of Dust (CAMRO-Dust) Data Record for Earth 

NNH22ZDA001N-MEASURES - NASA - Awarded: 2023-11-01


Dates:  - 


Adding high temporal resolution to the global long-term aerosol data record: A synergy of LEO and GEO

NNH17ZDA001N-MEASURES - NASA - Awarded: 2018-03-01


Dates:  - 


NNH19ZDA001N-ESROGSS - NASA - Awarded: 2020-05-26


Dates:  - 


Long-Term, High-Resolution Urban Aerosol Database for Research, Education and Outreach

NNH22ZAO001N-MUREPDEAP - NASA - Awarded: 2023-10-01


Dates:  - 


Resolving spectral aerosol absorption and optical depth of rapidly-evolving smoke with geostationary observations

NNH20ZDA001N-NIP - NASA - Awarded: 2021-05-01


Dates:  - 


Understanding airborne fertilization of oceanic ecosystems from analysis of MODIS, VIIRS and CALIOP observations.

NNH17ZDA001N-TASNPP - NASA - Awarded: 2018-05-01


Dates:  - 

Selected Publications


Refereed

2024. "Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX‐AQ." Journal of Geophysical Research: Atmospheres 129 (2): [10.1029/2023jd039085] [Journal Article/Letter]

2023. "Atmospheric nourishment of global ocean ecosystems." Science 380 (6644): 515-519 [10.1126/science.abq5252] [Journal Article/Letter]

2021. "Observation and modeling of the historic “Godzilla” African dust intrusion into the Caribbean Basin and the southern US in June 2020." Atmospheric Chemistry and Physics 21 (16): 12359-12383 [10.5194/acp-21-12359-2021] [Journal Article/Letter]

2021. "A Dark Target research aerosol algorithm for MODIS observations over eastern China: increasing coverage while maintaining accuracy at high aerosol loading." Atmospheric Measurement Techniques 14 (5): 3449-3468 [10.5194/amt-14-3449-2021] [Journal Article/Letter]

2020. "Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark‐Target Algorithm: 1. Dust Detection." Earth and Space Science 7 (10): [10.1029/2020ea001221] [Journal Article/Letter]

2020. "The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future." Remote Sensing 12 (18): 2900 [10.3390/rs12182900] [Journal Article/Letter]

2020. "Continuing the MODIS Dark Target Aerosol Time Series with VIIRS." Remote Sensing 12 (2): 308 [10.3390/rs12020308] [Journal Article/Letter]

2020. "Interannual variability and trends of combustion aerosol and dust in major continental outflows revealed by MODIS retrievals and CAM5 simulations during 2003–2017." Atmospheric Chemistry and Physics 20 139-161 [10.5194/acp-20-139-2020] [Journal Article/Letter]

2019. "Satellite‐Detected Ocean Ecosystem Response to Volcanic Eruptions in the Subarctic Northeast Pacific Ocean." Geophysical Research Letters 46 2019GL083977 [10.1029/2019gl083977] [Journal Article/Letter]

2019. "AERONET Remotely Sensed Measurements and Retrievals of Biomass Burning Aerosol Optical Properties During the 2015 Indonesian Burning Season." Journal of Geophysical Research: Atmospheres 124 (8): 4722-4740 [10.1029/2018jd030182] [Journal Article/Letter]

2019. "Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals." Atmospheric Chemistry and Physics 19 (1): 259-274 [10.5194/acp-19-259-2019] [Journal Article/Letter]

2018. "Exploring systematic offsets between aerosol products from the two MODIS sensors." Atmospheric Measurement Techniques 11 4073-4092 [https://doi.org/10.5194/amt-11-4073-2018] [Journal Article/Letter]

2016. "An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences." Geoscientific Model Development 9 (4): 1489-1522 [10.5194/gmd-9-1489-2016] [Journal Article/Letter]

2014. "Critical evaluation of cloud contamination in the MISR aerosol products using MODIS cloud mask products." Atmospheric Measurement Techniques 7 (6): 1791-1801 [10.5194/amt-7-1791-2014] [Journal Article/Letter]

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 [10.5194/amt-6-949-2013] [Journal Article/Letter]

2013. "Investigating enhanced Aqua MODIS aerosol optical depth retrievals over the mid-to-high latitude Southern Oceans through intercomparison with co-located CALIOP, MAN, and AERONET data sets." J. Geophys. Res. Atmos. 118 (10): 4700-4714 [10.1002/jgrd.50311] [Journal Article/Letter]

2011. "An analysis of the collection 5 MODIS over-ocean aerosol optical depth product for its implication in aerosol assimilation." Atmospheric Chemistry and Physics 11 (2): 557-565 [10.5194/acp-11-557-2011] [Journal Article/Letter]

2011. "A critical examination of spatial biases between MODIS and MISR aerosol products – application for potential AERONET deployment." Atmos. Meas. Tech. 4 (12): 2823-2836 [10.5194/amt-4-2823-2011] [Journal Article/Letter]