Modelling spatio-temporally resolved air temperature across the complex geo-climate area of France using satellite-derived land surface temperature data

Abstract

Climate change has focused attention on the effects of changing temperature, particularly the effect on human health. Thus, robust and accurate spatially and temporally resolved air temperature (Ta) data are of particular importance in the field of epidemiology and public health. However, most health studies to date have matched people to the nearest monitor. In this study, we aimed to develop a robust satellite-based spatio-temporally resolved Ta estimation model across the complex geo-climatic regions of France resulting in daily high-resolution 1 km predicted air temperature (Tap) estimations. We use a daily calibration approach using a series of processes to generate daily Tap for every day across the entire study area and period. First, we start by calibrating MODIS (Moderate Resolution Imaging Spectroradiometer) satellite-gridded surface temperature (Ts) data against Ta collected within 1 km of the Ts centroid. The calibration stage adjusted for spatio-temporal predictors, as done in environmental exposure assessment methods such as land use regressions. Second, to estimate Tap when no Ts data are available we fit a second model which uses the association of predicted grid cells Tap values (based on satellite Ts) with surrounding Ta monitors and the association with values in neighbouring grid cells. Out-of-sample tenfold cross-validation was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with available Ts and days without Ts observations (overall mean out-of-sample R2 = 0.95 for both stages). In conclusion, we demonstrate how Ts can be used reliably to predict daily Tap at high-resolution across France for use in studies looking at the effects of fine resolution Ta exposure on various health outcomes.