Downscaling GOES Land Surface Temperature for Assessing Heat Wave Health Risks

Abstract

Recent years have witnessed an emerging concern of the health impact of heat waves. A common approach to investigate heat waves is to resort to the geostationary thermal infrared imagery, such as those from the Geostationary Operational Environmental Satellite (GOES) and Meteosat Second Generation. However, coarse spatial resolutions of geostationary images cannot meet the need of assessing and monitoring heat waves in complex urban settings. To address the spatial and temporal variability of heat waves in urban areas, this letter presented a study of analyzing heat wave risk in Los Angeles, USA, by the synergistic use of GOES land surface temperature (LST), auxiliary geospatial, and census data within the framework of Crichton's Risk Triangle (i.e., hazard, exposure, and vulnerability). Principal component analysis and regression analysis were employed to downscale the original GOES LST imagery from 4 to 1 km. The resultant subhourly 1-km LST data was used to characterize and quantify heat hazard. The census population represented the exposure, while existing health, socioeconomic, and physical environmental conditions were used to describe the vulnerabilities. The risk map of heat wave was computed using the weighted indices of hazard, exposure, and vulnerability. The map was further overlaid with a zip-code data layer to generate statistics. The derived risk map showed that areas with high risk were identified in the central city, part of western LA County, and the desert area, based on a 10-point scale rank. Index Terms-GeostationaryOperational Environmental Satellite (GOES), heat wave, land surface temperature (LST), risk assessment, thermal downscaling, urban areas. Manuscript

Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives

“…Likewise, various regression tools have been employed to establish the relationships between regression kernels and LST. Several statistical models have been widely applied, including multivariate linear regression [34,58], piecewise linear regression [62], principal component regression [52,53], and geographically weighted regression [11]. These tools can achieve satisfactory enhancement results for rather homogeneous areas [19] and provide rapid enhancement due to their relatively low computational complexity [63].…”

Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data

“…Studies have suggested that DTCs are directly related to plant water stress and soil drought (Fensholt et al, 2011;Stisen et al, 2008;Hernandez-Barrera et al, 2017); thus, such a relationship has been utilized for mapping evapotranspiration (Anderson et al, 2011) and soil moisture (Piles et al, 2016). In addition, it helps improve meteorological forecasting through data assimilation (Orth et al, 2017), extreme heat wave assessments (Hrisko et al, 2020;Jiang et al, 2015), crop yield estimations (Anderson et al, 2016), LST spatiotemporal-scale conversions (Hu et al, 2020), orbit drift corrections of Advanced Very High Resolution Radiometer (AVHRR) LST data (Jin and Treadon, 2003), and vegetation phenology analyses (Piao et al, 2015). Considering the great potential of DTCs in scientific applications and the high temporal variability in LSTs, accurate diurnal LST datasets are crucial for the research community and public (Chang et al, 2021;Hrisko et al, 2020;Pinker et al, 2019).…”

Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data

“…Land surface temperature (LST, T s ) and air temperature (T a ) are key parameters for analyzing the thermal environment. Both the urban heat island (UHI) and heat waves have relationships with thermal environmental characteristics, yet the study of the UHI focuses more on the long-term climate, and heat waves are more concerned with short-term meteorological conditions (Jiang et al, 2015). To date, most studies have focused on deriving the LST from thermal infrared and microwave channels as well as its application to the urban heat island (Weng et al, 2005;Sobrino et al, 2012;Tomlinson et al, 2012;Ngie et al, 2014;Liu et al, 2015).…”

“…Although the Advanced Very High Resolution Radiometer (AVHRR) produces 1-2 images per day for the same area and the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites can acquire up to four images in middle and high-latitude areas, these techniques cannot capture the continuous temporal variance of heat waves in a day. The geostationary satellite imager has a much higher frequency of observation, but it cannot meet the need for assessing and monitoring detailed heat waves in complex settings (Jiang et al, 2015). Therefore, a common solution for characterizing heat wave characteristics is to downscale images to obtain a higher spatial resolution while maintaining the temporal resolution.…”

“…Weng and Fu (2014) proposed a least square support vector machine method to obtain half-hourly 1-km LSTs by downscaling GOES image data and further monitored the surface urban heat island in the Los Angeles region by extracting the diurnal temperature variations. To address the spatial and temporal variability of heat waves in urban areas, a study was presented that analyze the heat wave risk in Los Angeles, USA, by the synergistic use of GOES land surface temperature, auxiliary geospatial data, and census data within the framework of Crichton's Risk Triangle (Jiang et al, 2015).…”