GOES-16 Urban Land Surface Temperature Calibration Using a Handheld
Infrared Sensor Framework
Abstract
Satellite based remote sensing data are increasingly used for urban
meteorological applications, particularly to study urban heat island
impacts. However, the land surface temperature, a critical variable used
to characterize the urban thermal state has never been calibrated for
urbanized landcover. This will in turn escalate the uncertainties in
various applications (like weather forecasting in urban areas) which use
remote sensing data. This research focuses on the development and
testing of an Arduino-based, GPS-enabled, non-contact passive infrared
temperature sensor that provides ground-truth temperature validation of
the Geostationary Operational Environmental Satellite, GOES-16, and its
LST operational product. It is posited that high-resolution,
multi-point, near-surface temperature information will improve LST
algorithms and ultimately advance the application of satellite data to
study urban climate. New York City (NYC) is used as a test site for the
temperature sensor along with its geographically-respective satellite
calibration points. The analysis anticipates expansion into several U.S.
cities, pending preliminary evaluation and testing in NYC. GIS tools
will be used to visualize data points atop geographic maps, with the
intention of correlating more built-up landcover regions with
temperature differences quantified by the ground-based sensor and the
GOES-16 LST data. The Arduino sensor is equipped with a thermocouple to
provide real-time calibration measurements on the encountered surfaces
to ensure that parameters such as emissivity are captured, as well as
accurate and repeatable infrared temperature readings. The enhancement
of satellite information improves the well-being of the general public,
which can save lives during extreme weather events such as heat waves.
The research presented here intends to broaden the LST calibration
network available to satellites by providing a ground-based, portable
sensor framework that is implementable across cities and urban areas.