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People

Emmanuel Gil

Emmanuel Gil

Cohort III, Undergraduate

B.S, Computer Systems Technology , Undergraduate, 06/01/2020

Cohort Level: Cohort - III

Career Goal: My career goal once I graduate from my current degree is to work in the data science field. I would like to have environmental applications to my future work and start to engineer machine learning algorithms to solve issues while creating opportunities.

Expected Graduation Date: June 1, 2020

Degree: B.S Computer Systems Technology

Research Title: Development of High Temporal Diurnal Variation of Microwave Brightness Temperature

Research Synopsis: Brightness temperature (Tb) measurements by passive microwave radiometers are widely used to retrieve several atmospheric and surface parameters such as precipitation, soil moisture, freeze and thaw, water vapor, air temperature profile, and land surface emissivity. Land surface emissivity, retrieved after removing the atmospheric effects, is largely sensitive to surface properties and has been used to characterize surface freeze and thaw (FT) conditions and drought predictions. Since Tbs are measured at different microwave frequencies with various instruments, incident angles, spatial resolutions, and radiometric characteristics, an integration of Tbs from different microwave sensors would not necessarily be consistent. The goal of this study is to construct a diurnal cycle of TB using passive microwave sensors and harmonize them to develop accurate emissivity estimates.

Brightness temperature (Tb) measurements by passive microwave radiometers are widely used to retrieve several atmospheric and surface parameters such as precipitation, soil moisture, freeze and thaw, water vapor, air temperature profile, and land surface emissivity. Land surface emissivity, retrieved after removing the atmospheric effects, is largely sensitive to surface properties and has been used to characterize surface freeze and thaw (FT) conditions and drought predictions. Since Tbs are measured at different microwave frequencies with various instruments, incident angles, spatial resolutions, and radiometric characteristics, an integration of Tbs from different microwave sensors would not necessarily be consistent. The goal of this study is to construct a diurnal cycle of TB using passive microwave sensors and harmonize them to develop accurate emissivity estimates.

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.