Towards daily maximum heat index estimation across the conterminous United States using satellite-derived products.

Satellite-derived land surface temperature (LST) is widely utilized to study urban heat islands in the context of human health and thermal exposure. However, there is growing evidence to suggest that LST may be a poor indicator of apparent temperature, or the human-perceived equivalent temperature that reflects both heat and humidity. Moreover, heat index (HI), the apparent temperature metric used by the US National Weather Service, has yet to be computed at an increased spatial resolution and coverage beyond weather station observations. The goals of this study were to: 1) assess the extent to which HI can be estimated by a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and other satellite-derived products available at continental scales, and 2) determine which factors are most important in this estimation. Specifically, daily maximum 1-km HI from May through September of 2012 was modelled across the conterminous United States as a function of MODIS LST, precipitable water vapor (PWV), and near-infrared indices, in addition to static variables capturing land cover, topographic, and locational factors.The derived model was capable of estimating HI within a reasonable level of error (R2 = 0.83, RMSE = 4.4°F). This is the first time that HI has been directly estimated using exclusively remotely sensed products and validated over a large spatial extent. Analysis of individual variables indicated that LST and PWV were, by far, the most important factors for estimation. However, the incorporation of additional parameters further improved model performance (R2: +0.14, RMSE: -1.6°F). We hope that our work will eventually result in a national HI product assisting researchers in a variety of fields, including epidemiology, building energy demand, and environmental justice. Further work to interpolate cloud-contaminated satellite observations and downscale estimates to a 60-m resolution would considerably increase the utility of this HI estimation methodology. [ABSTRACT FROM AUTHOR]