A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003 to 2019

While weather stations generally capture near‐surface ambient air temperature (Ta) at a high temporal resolution to calculate daily values (i.e., daily minimum, mean, and maximum Ta), their fixed locations can limit their spatial coverage and resolution even in densely populated urban areas. As a result, data from weather stations alone may be inadequate for Ta‐related epidemiology particularly when the stations are not located in the areas of interest for human exposure assessment. To address this limitation in the Megalopolis of Central Mexico (MCM), we developed the first spatiotemporally resolved hybrid satellite‐based land use regression Ta model for the region, home to nearly 30 million people and includes Mexico City and seven more metropolitan areas. Our model predicted daily minimum, mean, and maximum Ta for the years 2003–2019. We used data from 120 weather stations and Land Surface Temperature (LST) data from NASA's MODIS instruments on the Aqua and Terra satellites on a 1 × 1 km grid. We generated a satellite‐hybrid mixed‐effects model for each year, regressing Ta measurements against land use terms, day‐specific random intercepts, and fixed and random LST slopes. We assessed model performance using 10‐fold cross‐validation at withheld stations. Across all years, the root‐mean‐square error ranged from 0.92 to 1.92 K and the R2 ranged from .78 to .95. To demonstrate the utility of our model for health research, we evaluated the total number of days in the year 2010 when residents ≥65 years old were exposed to Ta extremes (above 30°C or below 5°C). Our model provides much needed high‐quality Ta estimates for epidemiology studies in the MCM region. Spatial pattern of the 95th percentiles of minimum (a) and maximum (b) temperature across days for each 1 km2 grid cell in the Megalopolis of Central Mexico for 2018. Temporal imputation of LST, consideration of missing data as a predictor and careful cross‐validation with detailed characterization of predictive accuracy. Application estimates population exposures to extreme temperatures for use in epidemiologic studies.