Validating satellite-derived land surface temperature with in situ measurements: a public health perspective

Racial and ethnic minorities disproportionately exposed to extreme daily temperature variation in the United States

In the history of Homo sapiens, well-populated habitats have featured relatively stable temperatures with generally small daily variations. As the global population is increasingly residing in highly disparate climates, a burgeoning literature has documented the adverse health effects of single-day and day-to-day variation in temperature, raising questions of inequality in exposure to this environmental health risk. Yet, we continue to lack understanding of inequality in exposure to daily temperature variation (DTV) in the highly unequal United States. Using nighttime and daytime land surface temperature data between 2000 and 2017, this study analyzes population exposure to long-term DTV by race and ethnicity, income, and age for the 50 states and the District of Columbia. The analysis is based on population-weighted exposure at the census-tract level. We find that, on average, non-White (especially Black and Hispanic) and low-income Americans are exposed disproportionately to larger DTV. Race-based inequalities in exposure to DTV are larger than income-based disparities, with inequalities heightened in the summer months. In May, for example, the DTV difference by race and ethnicity of 51 states is between 0.20 and 3.01 °C (up to 21.0%). We find that younger populations are, on average, exposed to larger DTV, though the difference is marginal.

Linking Prenatal Environmental Exposures to Lifetime Health with Epigenome-Wide Association Studies: State-of-the-Science Review and Future Recommendations

BACKGROUND

The prenatal environment influences lifetime health; epigenetic mechanisms likely predominate. In 2016, the first international consortium paper on cigarette smoking during pregnancy and offspring DNA methylation identified extensive, reproducible exposure signals. This finding raised expectations for epigenome-wide association studies (EWAS) of other exposures.

OBJECTIVE

We review the current state-of-the-science for DNA methylation associations across prenatal exposures in humans and provide future recommendations.

METHODS

We reviewed 134 prenatal environmental EWAS of DNA methylation in newborns, focusing on 51 epidemiological studies with meta-analysis or replication testing. Exposures spanned cigarette smoking, alcohol consumption, air pollution, dietary factors, psychosocial stress, metals, other chemicals, and other exogenous factors. Of the reproducible DNA methylation signatures, we examined implementation as exposure biomarkers.

RESULTS

Only 19 (14%) of these prenatal EWAS were conducted in cohorts of 1,000 or more individuals, reflecting the still early stage of the field. To date, the largest perinatal EWAS sample size was 6,685 participants. For comparison, the most recent genome-wide association study for birth weight included more than 300,000 individuals. Replication, at some level, was successful with exposures to cigarette smoking, folate, dietary glycemic index, particulate matter with aerodynamic diameter < 10 μ m and < 2.5 μ m , nitrogen dioxide, mercury, cadmium, arsenic, electronic waste, PFAS, and DDT. Reproducible effects of a more limited set of prenatal exposures (smoking, folate) enabled robust methylation biomarker creation.

DISCUSSION

Current evidence demonstrates the scientific premise for reproducible DNA methylation exposure signatures. Better powered EWAS could identify signatures across many exposures and enable comprehensive biomarker development. Whether methylation biomarkers of exposures themselves cause health effects remains unclear. We expect that larger EWAS with enhanced coverage of epigenome and exposome, along with improved single-cell technologies and evolving methods for integrative multi-omics analyses and causal inference, will expand mechanistic understanding of causal links between environmental exposures, the epigenome, and health outcomes throughout the life course. https://doi.org/10.1289/EHP12956.

Adverse Health Outcomes Following Hurricane Harvey: A Comparison of Remotely-Sensed and Self-Reported Flood Exposure Estimates

Remotely sensed inundation may help to rapidly identify areas in need of aid during and following floods. Here we evaluate the utility of daily remotely sensed flood inundation measures and estimate their congruence with self-reported home flooding and health outcomes collected via the Texas Flood Registry (TFR) following Hurricane Harvey. Daily flood inundation for 14 days following the landfall of Hurricane Harvey was acquired from FloodScan. Flood exposure, including number of days flooded and flood depth was assigned to geocoded home addresses of TFR respondents (N = 18,920 from 47 counties). Discordance between remotely-sensed flooding and self-reported home flooding was measured. Modified Poisson regression models were implemented to estimate risk ratios (RRs) for adverse health outcomes following flood exposure, controlling for potential individual level confounders. Respondents whose home was in a flooded area based on remotely-sensed data were more likely to report injury (RR = 1.5, 95% CI: 1.27-1.77), concentration problems (1.36, 95% CI: 1.25-1.49), skin rash (1.31, 95% CI: 1.15-1.48), illness (1.29, 95% CI: 1.17-1.43), headaches (1.09, 95% CI: 1.03-1.16), and runny nose (1.07, 95% CI: 1.03-1.11) compared to respondents whose home was not flooded. Effect sizes were larger when exposure was estimated using respondent-reported home flooding. Near-real time remote sensing-based flood products may help to prioritize areas in need of assistance when on the ground measures are not accessible.

Building Public Health Surveillance 3.0: Emerging Timely Measures of Physical, Economic, and Social Environmental Conditions Affecting Health

In response to rapidly changing societal conditions stemming from the COVID-19 pandemic, we summarize data sources with potential to produce timely and spatially granular measures of physical, economic, and social conditions relevant to public health surveillance, and we briefly describe emerging analytic methods to improve small-area estimation. To inform this article, we reviewed published systematic review articles set in the United States from 2015 to 2020 and conducted unstructured interviews with senior content experts in public heath practice, academia, and industry. We identified a modest number of data sources with high potential for generating timely and spatially granular measures of physical, economic, and social determinants of health. We also summarized modeling and machine-learning techniques useful to support development of time-sensitive surveillance measures that may be critical for responding to future major events such as the COVID-19 pandemic. (Am J Public Health. 2022;112(10):1436-1445. https://doi.org/10.2105/AJPH.2022.306917).

High-Resolution Estimation of Monthly Air Temperature from Joint Modeling of In Situ Measurements and Gridded Temperature Data

Surface air temperature is an important variable in quantifying extreme heat, but high-resolution temporal and spatial measurement is limited by sparse climate-data stations. As a result, hyperlocal models of extreme heat involve intensive physical data collection efforts or analyze satellite-derived land-surface temperature instead. We developed a geostatistical model that integrates in situ climate-quality temperature records, gridded temperature data, land-surface temperature estimates, and spatially consistent covariates to predict monthly averaged daily maximum surface-air temperatures at spatial resolutions up to 30 m. We trained and validated the model using data from North Carolina. The fitted model showed strong predictive performance with a mean absolute error of 1.61 ∘F across all summer months and a correlation coefficient of 0.75 against an independent hyperlocal temperature model for the city of Durham. We show that the proposed model framework is highly scalable and capable of producing realistic temperature fields across a variety of physiographic settings, even in areas where no climate-quality data stations are available.

The Dynamic Relationship between Air and Land Surface Temperature within the Madison, Wisconsin Urban Heat Island

The urban heat island (UHI) effect, the phenomenon by which cities are warmer than rural surroundings, is increasingly important in a rapidly urbanizing and warming world, but fine-scale differences in temperature within cities are difficult to observe accurately. Networks of air temperature (Tair) sensors rarely offer the spatial density needed to capture neighborhood-level disparities in warming, while satellite measures of land surface temperature (LST) do not reflect the air temperatures that people physically experience. This analysis combines both Tair measurements recorded by a spatially-dense stationary sensor network in Dane County, Wisconsin, and remotely-sensed measurements of LST over the same area—to improve the use and interpretation of LST in UHI studies. The data analyzed span three summer months (June, July, and August) and eight years (2012–2019). Overall, Tair and LST displayed greater agreement in spatial distribution than in magnitude. The relationship between day of the year and correlation was fit to a parabolic curve (R2 = 0.76, p = 0.0002) that peaked in late July. The seasonal evolution in the relationship between Tair and LST, along with particularly high variability in LST across agricultural land cover suggest that plant phenology contributes to a seasonally varying relationship between Tair and LST measurements of the UHI.

Reducing Scaling Effect on Downscaled Land Surface Temperature Maps in Heterogenous Urban Environments

The literature review indicates that a scaling effect does exist in downscaling land surface temperature (DLST) processes, and no substantial methods were specially developed for addressing it. In this research, the main aim is to develop a new method to reduce the scaling effect on DLST maps at high resolutions. A thermal component-based thermal spectral unmixing (TSU) model was modified and a multiple regression (REG) model was adopted to create DLST maps at high resolutions. A combined variance of red and NIR bands at a very high resolution with a difference image between upscaled LST and DLST was used to develop a new method. With two case data sets, LSTs at coarse resolutions were downscaled by using the modified TSU model and the REG model to create DLST results. The new method with a correction term expression (a linear model created by using a semi-empirical approach) was used to improve the DLST maps in the two case study areas. The experimental results indicate that the new method could reduce the root mean square error and the mean absolute error >30% and >33%, respectively, and thus demonstrate that the proposed method was effective and significant, especially reducing the scaling effect on DLST results at very high resolutions. The novel significance for the new method is directly reducing the scaling effect on DLST maps at high resolutions.

Effects of trees, gardens, and nature trails on heat index and child health: design and methods of the Green Schoolyards Project

BACKGROUND

Latinx children in the United States are at high risk for nature-deficit disorder, heat-related illness, and physical inactivity. We developed the Green Schoolyards Project to investigate how green features-trees, gardens, and nature trails-in school parks impact heat index (i.e., air temperature and relative humidity) within parks, and physical activity levels and socioemotional well-being of these children. Herein, we present novel methods for a) observing children's interaction with green features and b) measuring heat index and children's behaviors in a natural setting, and a selection of baseline results.

METHODS

During two September weeks (high temperature) and one November week (moderate temperature) in 2019, we examined three joint-use elementary school parks in Central Texas, United States, serving predominantly low-income Latinx families. To develop thermal profiles for each park, we installed 10 air temperature/relative humidity sensors per park, selecting sites based on land cover, land use, and even spatial coverage. We measured green features within a geographic information system. In a cross-sectional study, we used an adapted version of System for Observing Play and Recreation in Communities (SOPARC) to assess children's physical activity levels and interactions with green features. In a cohort study, we equipped 30 3rd and 30 4th grade students per school during recess with accelerometers and Global Positioning System devices, and surveyed these students regarding their connection to nature. Baseline analyses included inverse distance weighting for thermal profiles and summing observed counts of children interacting with trees.

RESULTS

In September 2019, average daily heat index ranged 2.0 °F among park sites, and maximum daily heat index ranged from 103.4 °F (air temperature = 33.8 °C; relative humidity = 55.2%) under tree canopy to 114.1 °F (air temperature = 37.9 °C; relative humidity = 45.2%) on an unshaded playground. 10.8% more girls and 25.4% more boys interacted with trees in September than in November.

CONCLUSIONS

We found extreme heat conditions at select sites within parks, and children positioning themselves under trees during periods of high heat index. These methods can be used by public health researchers and practitioners to inform the redesign of greenspaces in the face of climate change and health inequities.

Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey

Household-level information on central air conditioning (cenAC) and room air conditioning (rmAC) air conditioning and cold-weather thermal comfort are often missing from publicly available housing databases hindering research and action on climate adaptation and air pollution exposure reduction. We modeled these using information from the American Housing Survey for 2003-2013 and 140 US core-based statistical areas employing variables that would be present in publicly available parcel records. We present random-intercept logistic regression models with either cenAC, rmAC or "home was uncomfortably cold for 24 h or more" (tooCold) as outcome variables and housing value, rented vs. owned, age, and multi- vs. single-family, each interacted with cooling- or heating-degree days as predictors. The out-of-sample predicted probabilities for years 2015-2017 were compared with corresponding American Housing Survey values (0 or 1). Using a 0.5 probability threshold, the model had 63% specificity (true negative rate), and 91% sensitivity (true positive rate) for cenAC, while specificity and sensitivity for rmAC were 94% and 34%, respectively. Area-specific sensitivities and specificities varied widely. For tooCold, the overall sensitivity was effectively 0%. Future epidemiologic studies, heat vulnerability maps, and intervention screenings may reliably use these or similar AC models with parcel-level data to improve understanding of health risk and the spatial patterning of homes without AC.

Developing a geospatial measure of change in core temperature for migrating persons in the Mexico-U.S. border region

Although heat exposure is the leading cause of mortality for undocumented immigrants attempting to traverse the Mexico-U.S. border, there has been little work in quantifying risk. Therefore, our study aims to develop a methodology projecting increase in core temperature over time and space for migrants in Southern Arizona using spatial analysis and remote sensing in combination with the heat balance equation-adapting physiological formulae to a multi-step geospatial model using local climate conditions, terrain, and body specifics. We sought to quantitatively compare the results by demographic categories of age and sex and qualitatively compare them to known terrestrial conditions and prior studies of those conditions. We demonstrated a more detailed measure of risk for migrants than those used most recently: energy expenditure and terrain ruggedness. Our study not only gives a better understanding of the 'funnel effect' mechanisms, but also provides an opportunity for relief and rescue operations.

The Effects of Historical Housing Policies on Resident Exposure to Intra-Urban Heat: A Study of 108 US Urban Areas

The increasing intensity, duration, and frequency of heat waves due to human-caused climate change puts historically underserved populations in a heightened state of precarity, as studies observe that vulnerable communities—especially those within urban areas in the United States—are disproportionately exposed to extreme heat. Lacking, however, are insights into fundamental questions about the role of historical housing policies in cauterizing current exposure to climate inequities like intra-urban heat. Here, we explore the relationship between “redlining”, or the historical practice of refusing home loans or insurance to whole neighborhoods based on a racially motivated perception of safety for investment, with present-day summertime intra-urban land surface temperature anomalies. Through a spatial analysis of 108 urban areas in the United States, we ask two questions: (1) how do historically redlined neighborhoods relate to current patterns of intra-urban heat? and (2) do these patterns vary by US Census Bureau region? Our results reveal that 94% of studied areas display consistent city-scale patterns of elevated land surface temperatures in formerly redlined areas relative to their non-redlined neighbors by as much as 7 °C. Regionally, Southeast and Western cities display the greatest differences while Midwest cities display the least. Nationally, land surface temperatures in redlined areas are approximately 2.6 °C warmer than in non-redlined areas. While these trends are partly attributable to the relative preponderance of impervious land cover to tree canopy in these areas, which we also examine, other factors may also be driving these differences. This study reveals that historical housing policies may, in fact, be directly responsible for disproportionate exposure to current heat events.

Shifting from "Community-Placed" to "Community-Based" Research to Advance Health Equity: A Case Study of the Heatwaves, Housing, and Health: Increasing Climate Resiliency in Detroit (HHH) Partnership

Extreme summertime heat is a significant public health threat that disproportionately impacts vulnerable urban populations. Research on health impacts of climate change (including increasing intensity, duration, and frequency of hot weather) is sometimes designed and implemented without the involvement of the communities being studied, i.e., "community-placed" not "community-based." We describe how the Heatwaves, Housing, and Health: Increasing Climate Resiliency in Detroit (HHH) partnership engaged relevant communities by integrating a community-based participatory research (CBPR) approach into an existing, academic-designed research project through a steering committee of community and academic partners. Using a case study approach, we analyze program documentation, partnership evaluation questionnaires, and HHH steering committee meeting notes. We describe the CBPR process by which we successfully collected research data in Detroit during summer 2016, engaged in collaborative analysis of data, and shared results with Detroit residents. Evaluations of the partnership over 2 years show community involvement in research; enhanced capacities; success in securing new grant funding; and ways that CBPR strengthened the validity, relevance, and translation of research. Engaging communities as equal partners using CBPR, even after a study is underway, can strengthen research to understand and address the impacts of extreme heat on health and equity in urban communities.

Multi-Temporal Effects of Urban Forms and Functions on Urban Heat Islands Based on Local Climate Zone Classification

Urban forms and functions have critical impacts on urban heat islands (UHIs). The concept of a “local climate zone” (LCZ) provides a standard and objective protocol for characterizing urban forms and functions, which has been used to link urban settings with UHIs. However, only a few structure types and surface cover properties are included under the same climate background or only one or two time scales are considered with a high spatial resolution. This study assesses multi-temporal land surface temperature (LST) characteristics across 18 different LCZ types in Beijing, China, from July 2017 to June 2018. A geographic information system-based method is employed to classify LCZs based on five morphological and coverage indicators derived from a city street map and Landsat images, and a spatiotemporal fusion model is adopted to generate hourly 100-m LSTs by blending Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), and FengYun-2F LSTs. Then, annual and diurnal cycle parameters and heat island and cool island (HI or CI) frequency are linked to LCZs at annual, seasonal, monthly, and diurnal scales. Results indicate that: (1) the warmest zones are compact and mid and low-rise built-up areas, while the coolest zones are water and vegetated types; (2) compact and open high-rise built-up areas and vegetated types have seasonal thermal patterns but with different causes; (3) diurnal temperature ranges are the highest for compact and large low-rise settings but the lowest for water and dense or scattered trees; and (4) HIs are the most frequent summertime and daytime events, while CIs occur primarily during winter days, making them more or less frequent for open or compact and high- or low-rise built-up areas. Overall, the distinguishable LSTs or UHIs between LCZs are closely associated with the structure and coverage properties. Factors such as geolocation, climate, and layout also interfere with the thermal behavior. This study provides comprehensive information on how different urban forms and functions are related to LST variations at different time scales, which supports urban thermal regulation through urban design.

Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer

As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10-, 30-, 60-, and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (∼7 km length, sampled 3-12 times each) spanning a range of impervious and tree canopy cover (0-100%, each) in a midsized city in the Upper Midwest United States. Variability in daytime air temperature within the urban landscape averaged 3.5 °C (range, 1.1-5.7 °C). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale and was greatest at the size of a typical city block (60-90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1 °C (range, 1.2-3.0 °C), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.

Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer

As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10-, 30-, 60-, and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (∼7 km length, sampled 3-12 times each) spanning a range of impervious and tree canopy cover (0-100%, each) in a midsized city in the Upper Midwest United States. Variability in daytime air temperature within the urban landscape averaged 3.5 °C (range, 1.1-5.7 °C). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale and was greatest at the size of a typical city block (60-90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1 °C (range, 1.2-3.0 °C), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.

Turning down the heat: An enhanced understanding of the relationship between urban vegetation and surface temperature at the city scale

Guiding urban planners on the cooling returns of different configurations of urban vegetation is important to protect urban dwellers from adverse heat impacts. To this end, we estimated statistical models that fused multi-temporal very fine spatial (20 cm) and vertical (1 mm) resolution imagery, that captures the complexity of urban vegetation, with remotely sensed temperature data to assess how urban vegetation configuration influences urban temperatures. Perth, Western Australia, was used as a case-study for this analysis. Panel regression models showed that within a location an increase in tree and shrub cover has a larger cooling effect than grass coverage. On average, holding all else equal, an approximate 1 km² increase in shrub (tree) cover within a location reduces surface temperatures by 12 °C (5 °C). We included a range of robustness checks for the observed relationships between urban vegetation type and temperature. Geographically weighted regression models showed spatial variation in the cooling effect of different vegetation types; this indicates that i) unobserved factors moderate temperature-vegetation relationships across urban landscapes, and ii) that urban vegetation type and temperature relationships are complex. Machine learning models (Random Forests) were used to further explore complex and non-linear relationships between different urban vegetation configurations and temperature. The Random Forests showed that vegetation type explained 31.84% of the out-of-bag variance in summer surface temperatures, that increased cover of large vegetation within a location increases cooling, and that different configurations of urban vegetation structure can lead to cooling gains. The models in this study were trained with vegetation data capturing local detail, multiple time-periods, and entire city coverage. Thus, these models illustrate the potential to develop locally-detailed and spatially explicit tools to guide planning of vegetation configuration to optimise cooling at local- and city-scales.

Assessment of heat exposure in cities: Combining the dynamics of temperature and population

Urban populations are typically subject to higher outdoor heat exposure than nearby rural areas due to the urban heat island (UHI) effect. Excessive Heat Events (EHEs) further amplify heat stress imposed on city dwellers. Heat exposure largely depends on the spatial and temporal distribution of temperature and population, however, few studies considered their concurrent variations. To better characterize exposure to heat in the context of long-term urban climatology and during excessive heat events, this study focuses on the dynamics of ambient temperature and population and proposes an open-data-based approach for spatiotemporal analysis of urban exposure to heat by using air temperature estimated from satellite observations and commute-adjusted diurnal population calculated primarily on the Census Transportation Planning Products. We use the metropolitan area of Chicago, U.S.A. as a case study to analyze the urban heat pattern changes during EHEs and their influence on population heat exposure diurnally. The intra-urban spatiotemporal analysis reveals that the population's exposure to heat changes fast as the nighttime temperature increases and the EHEs increase the spatial exposure impact due to the ubiquitous higher nocturnal temperature over the Chicago metropolitan area. "Hotspots" associated with a higher temperature and greater number of urban residents are identified in the heat exposure map. Meanwhile, the spatial extent of high ambient exposure areas varies diurnally. Our study contributes to a better understanding of the dynamic heat exposure patterns in urban areas. The approaches presented in this article can be used for informing heat mitigation as well as emergency response strategies at specific times and locations.

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.

Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel

Meteorological stations measure air temperature (Ta) accurately with high temporal resolution, but usually suffer from limited spatial resolution due to their sparse distribution across rural, undeveloped or less populated areas. Remote sensing satellite-based measurements provide daily surface temperature (Ts) data in high spatial and temporal resolution and can improve the estimation of daily Ta. In this study we developed spatiotemporally resolved models which allow us to predict three daily parameters: Ta Max (day time), 24h mean, and Ta Min (night time) on a fine 1km grid across the state of Israel. We used and compared both the Aqua and Terra MODIS satellites. We used linear mixed effect models, IDW (inverse distance weighted) interpolations and thin plate splines (using a smooth nonparametric function of longitude and latitude) to first calibrate between Ts and Ta in those locations where we have available data for both and used that calibration to fill in neighboring cells without surface monitors or missing Ts. Out-of-sample ten-fold cross validation (CV) was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with and without available Ts observations for both Aqua and Terra (CV Aqua R² results for min 0.966, mean 0.986, and max 0.967; CV Terra R² results for min 0.965, mean 0.987, and max 0.968). Our research shows that daily min, mean and max Ta can be reliably predicted using daily MODIS Ts data even across Israel, with high accuracy even for days without Ta or Ts data. These predictions can be used as three separate Ta exposures in epidemiology studies for better diurnal exposure assessment.

Challenges to quantitative applications of Landsat observations for the urban thermal environment

Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program significantly benefits both the academic community and society. Thermal imagery from Landsat sensors, provided with relatively high spatial resolution, is suitable for monitoring urban thermal environment. Growing use of Landsat data in monitoring urban thermal environment is demonstrated by increasing publications on this subject, especially over the last decade. Urban thermal environment is usually delineated by land surface temperature (LST). However, the quantitative and accurate estimation of LST from Landsat data is still a challenge, especially for urban areas. This paper will discuss the main challenges for urban LST retrieval, including urban surface emissivity, atmospheric correction, radiometric calibration, and validation. In addition, we will discuss general challenges confronting the continuity of quantitative applications of Landsat observations. These challenges arise mainly from the scan line corrector failure of the Landsat 7 ETM+ and channel differences among sensors. Based on these investigations, the concerns are to: (1) show general users the limitation and possible uncertainty of the retrieved urban LST from the single thermal channel of Landsat sensors; (2) emphasize efforts which should be done for the quantitative applications of Landsat data; and (3) understand the potential challenges for the continuity of Landsat observation (i.e., thermal infrared) for global change monitoring, while several climate data record programs being in progress.

Satellite Remote Sensing for Coastal Management: A Review of Successful Applications

Management of coastal and marine natural resources presents a number of challenges as a growing global population and a changing climate require us to find better strategies to conserve the resources on which our health, economy, and overall well-being depend. To evaluate the status and trends in changing coastal resources over larger areas, managers in government agencies and private stakeholders around the world have increasingly turned to remote sensing technologies. A surge in collaborative and innovative efforts between resource managers, academic researchers, and industry partners is becoming increasingly vital to keep pace with evolving changes of our natural resources. Synoptic capabilities of remote sensing techniques allow assessments that are impossible to do with traditional methods. Sixty years of remote sensing research have paved the way for resource management applications, but uncertainties regarding the use of this technology have hampered its use in management fields. Here we review examples of remote sensing applications in the sectors of coral reefs, wetlands, water quality, public health, and fisheries and aquaculture that have successfully contributed to management and decision-making goals.

Spatiotemporal estimation of air temperature patterns at the street level using high resolution satellite imagery

Although meteorological monitoring stations provide accurate measurements of Air Temperature (AT), their spatial coverage within a given region is limited and thus is often insufficient for exposure and epidemiological studies. In many applications, satellite imagery measures energy flux, which is spatially continuous, and calculates Brightness Temperature (BT) that used as an input parameter. Although both quantities (AT-BT) are physically related, the correlation between them is not straightforward, and varies daily due to parameters such as meteorological conditions, surface moisture, land use, satellite-surface geometry and others. In this paper we first investigate the relationship between AT and BT as measured by 39 meteorological stations in Israel during 1984-2015. Thereafter, we apply mixed regression models with daily random slopes to calibrate Landsat BT data with monitored AT measurements for the period 1984-2015. Results show that AT can be predicted with high accuracy by using BT with high spatial resolution. The model shows relatively high accuracy estimation of AT (R²=0.92, RMSE=1.58°C, slope=0.90). Incorporating meteorological parameters into the model generates better accuracy (R²=0.935) than the AT-BT model (R²=0.92). Furthermore, based on the relatively high model accuracy, we investigated the spatial patterns of AT within the study domain. In the latter we focused on July-August, as these two months are characterized by relativity stable synoptic conditions in the study area. In addition, a temporal change in AT during the last 30years was estimated and verified using available meteorological stations and two additional remote sensing platforms. Finally, the impact of different land coverage on AT were estimated, as an example of future application of the presented approach.

Satellite remote sensing in epidemiological studies

PURPOSE OF REVIEW

Particulate matter air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite-based remote-sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies.

RECENT FINDINGS

Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level particulate matter can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for particulate matter model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates.

SUMMARY

It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies.

A Bicycle-Based Field Measurement System for the Study of Thermal Exposure in Cuyahoga County, Ohio, USA

Collecting a fine scale of microclimate data can help to determine how physical characteristics (e.g., solar radiation, albedo, sky view factor, vegetation) contribute to human exposure to ground and air temperatures. These data also suggest how urban design strategies can reduce the negative impacts of the urban heat island effect. However, urban microclimate measurement poses substantial challenges. For example, data taken at local airports are not representative of the conditions at the neighborhood or district level because of variation in impervious surfaces, vegetation, and waste heat from vehicles and buildings. In addition, fixed weather stations cannot be deployed quickly to capture data from a heat wave. While remote sensing can provide data on land cover and ground surface temperatures, resolution and cost remain significant limitations. This paper describes the design and validation of a mobile measurement bicycle. This bicycle permits movement from space to space within a city to assess the physical and thermal properties of microclimates. The construction of the vehicle builds on investigations of the indoor thermal environment of buildings using thermal comfort carts.

Spatio-temporal behavior of brightness temperature in Tel-Aviv and its application to air temperature monitoring

This study applies remote sensing technology to assess and examine the spatial and temporal Brightness Temperature (BT) profile in the city of Tel-Aviv, Israel over the last 30 years using Landsat imagery. The location of warmest and coldest zones are constant over the studied period. Distinct diurnal and temporal BT behavior divide the city into four different segments. As an example of future application, we applied mixed regression models with daily random slopes to correlate Landsat BT data with monitored air temperature (Tair) measurements using 14 images for 1989-2014. Our preliminary results show a good model performance with R(2) = 0.81. Furthermore, based on the model's results, we analyzed the spatial profile of Tair within the study domain for representative days.

Urban-hazard risk analysis: mapping of heat-related risks in the elderly in major Italian cities

BACKGROUND

Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks.

OBJECTIVES

Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥ 65).

METHODS

A long time-series (2001-2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using "Crichton's Risk Triangle" hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI).

RESULTS

The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities.

CONCLUSIONS

This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat-related emergencies.

The 2011 heat wave in Greater Houston: Effects of land use on temperature

Effects of land use on temperatures during severe heat waves have been rarely studied. This paper examines land use-temperature associations during the 2011 heat wave in Greater Houston. We obtained high resolution of satellite-derived land use data from the US National Land Cover Database, and temperature observations at 138 weather stations from Weather Underground, Inc (WU) during the August of 2011, which was the hottest month in Houston since 1889. Land use regression and quantile regression methods were applied to the monthly averages of daily maximum/mean/minimum temperatures and 114 land use-related predictors. Although selected variables vary with temperature metric, distance to the coastline consistently appears among all models. Other variables are generally related to high developed intensity, open water or wetlands. In addition, our quantile regression analysis shows that distance to the coastline and high developed intensity areas have larger impacts on daily average temperatures at higher quantiles, and open water area has greater impacts on daily minimum temperatures at lower quantiles. By utilizing both land use regression and quantile regression on a recent heat wave in one of the largest US metropolitan areas, this paper provides a new perspective on the impacts of land use on temperatures. Our models can provide estimates of heat exposures for epidemiological studies, and our findings can be combined with demographic variables, air conditioning and relevant diseases information to identify 'hot spots' of population vulnerability for public health interventions to reduce heat-related health effects during heat waves.