Summer land surface temperature from MODIS Aqua and Terra satellites for Houston in 2014 and Phoenix in 2003 at 1km resolution

Satellite remote-sensing is used to collect important atmospheric and
geophysical data at various spatial resolutions, providing insight into
spatiotemporal surface and climate variability globally. These
observations are often plagued with missing spatial and temporal
information of Earth’s surface due to (1) cloud cover at the time of a
satellite passing and (2) infrequent passing of polar-orbiting satellites.
While many methods are available to model missing data in space and time,
in the case of land surface temperature (LST) from thermal infrared remote
sensing, these approaches generally ignore the temporal pattern called the
‘diurnal cycle’ which physically constrains temperatures to peak in the
early afternoon and reach a minimum at sunrise. In order to infill an LST
dataset, we parameterize the diurnal cycle into a functional form with
unknown spatiotemporal parameters. Using multiresolution spatial basis
functions, we estimate these parameters from sparse satellite observations
to reconstruct an LST field with continuous spatial and temporal
distributions. These estimations may then be used to better inform
scientists of spatiotemporal thermal patterns over relatively complex
domains. The methodology is demonstrated using data collected by MODIS on
NASA’s Aqua and Terra satellites over both Houston, TX and Phoenix, AZ
USA.