Date of Award
Master of Public Health (MPH)
School of Public Health
Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reduce exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily Ta from satellite-based land surface temperature (Ts) over Sweden during 2001-2019 at a high spatial resolution of 1×1 km2. The ensemble model incorporated four base models, including a generalized additive model, a generalized additive mixed model, and two machine learning models (random forest and extreme gradient boosting), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight. The ensemble model showed high performance with an overall R2 of 0.98 and a root mean square error of 1.38 °C in the ten-fold cross-validation, and outperformed each of the four base models. Among base models, the two machine learning models outperformed the two regression models. In the machine learning models, Ts was the dominant predictor of Ta, followed by day of year, Normalized Difference Vegetation Index, latitude, elevation, and longitude. The highly spatiotemporally-resolved Ta can improve temperature exposure assessment and reduce exposure misclassification in future epidemiological studies.
Jin, Zhihao, "Predicting Spatiotemporally-Resolved Air Temperature Over Sweden From Satellite Data Using An Ensemble Model" (2021). Public Health Theses. 2059.