GOES-R Land Surface Temperature Product and Its Readiness to Users

Thursday, 9 August, 10:00am-12:30pm

Enrollment limited to 30 people.

Dr. Yunyue Yu,  U.S.  NOAA/NESDIS Center for Satellite Applications and Research (STAR), [email protected]
Dr. Peng Yu, Earth System Science Interdisciplinary Center of University of Maryland, NOAA.NESDIS/STAR affiliated,  [email protected]



Information on land surface temperature (LST) is important for understanding climate change, modeling the hydrological and biogeochemical cycles, and is a prime candidate parameter for Numerical Weather Prediction assimilation models. LST is required as one of essential climate variables (ECVs) by the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO). At NOAA National Environmental Satellite and Information Service (NESDIS), it is operationally produced as one of baseline products from the Geostationary Operational Environmental Satellite (GOES) R series (GOES-R) mission as well as from the Joint Polar-orbing Satellite System (JPSS) mission. Different from the JPSS LST product, the GOES-R mission provides hourly LST products with three spatial domains, Full Disk (FD), Continental United States (CONUS), and two adjustable 1000 km by 1000 km meso-scale domains (MESO). Given the rapid scanning speed of the Advanced Baseline Imager (ABI) sensor onboard GOES-R, higher temporal resolution LST products for the three spatial domains are possible, i.e., 15-minute FD LST, 5-minute CONUS LST, and one-minute MESO LST. The high temporal resolution addresses the rapid change of the LST variable and offers users unique opportunities to study diurnal LST variation details from satellite over regions of interest.


While the first GOES-R (later named as GOES-16) was launched in November 2016 and the GOES-16 LST product has passed provisional maturity review in March 2018, many users are interested in using the GOES-16 for their applications. For instances, Environmental Modeling Center (EMC) data assimilation and modeling group at NOAA

National Centers for Environmental Prediction (NCEP) is trying to use the GOES LST data for evaluating the model output and for possible model adjustment; researchers from City College of New York are utilizing the GOES LST product for their urban climate study around New York City Region. Some scientists are also interested in further calibrating the LST data with ground-based stations and to downscaling the GOES-R LST data using Landsat measurements.

In this session, scientists from NOAA/NESDIS will provide detailed information about the GOES-R LST product: its measurement basics, product quality, data access, quality control measures, sample applications, and resources of technical supports.