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Roberto Garcia

Roberto Garcia

Alumni Students, Cohort III, NERTO, Masters

M.S, Electrical Engineering, Graduate

Cohort Level: Cohort - III

Career Goal: Once my Master's degree is complete I plan to start my professional career in government or with a government contractor. My goal is to develop a career in data engineering serving the needs of science focused agencies like NOAA and NASA or possibly the US intelligence community.

Expected Graduation Date: August 30, 2021

Degree: M.S Electrical Engineering

Research Title: Downscaling of GOES-16’s Land Surface Temperature Product

Research Synopsis: Land surface temperature (LST) is an environmental variable derived from thermal infrared (TIR) imagery. Satellite platforms are a good source of TIR imagery because of their ability to provide widespread and frequent coverage of the Earth’s surface. It is common that a single satellite remote sensing platform is able to provide images with good spatial resolution or temporal resolution but not both. LST is an important parameter for studies on the urban heat island (UHI) effect. These studies are limited by the spatial or temporal resolution of available LST products. The goal is to estimate land surface temperature with high spatial and temporal resolution by downscaling GOES-16’s LST product to benefit UHI studies as well as any users of LST data.

Land Surface Temperature (LST) is an environmental variable derived from thermal infrared (TIR) imagery. Satellite platforms are a good source of TIR imagery because of their ability to provide widespread and frequent coverage of the Earth’s surface. It is common that a single satellite platform is able to provide images with good spatial resolution or temporal resolution but not both. LST is an important parameter for studies on the Urban Heat Island (UHI) effect. These studies are limited by the spatial or temporal resolution of available LST products. The goal is to estimate Land Surface Temperature with higher spatial and temporal resolution by downscaling GOES-16’s LST product to benefit UHI studies as well as any users of LST data.

CESSRST Consortium

CESSRST is led by The City University of New York and brings together Hampton University, VA; University of Puerto Rico at Mayaguez, PR; San Diego State University, CA; University of Maryland Baltimore County, MD; University of Texas at El Paso, TX.