Date of Award
Remote Sensing, Satellite, Energy Balance, Heat Transfer, Urban, Machine Learning
The energy exchanges at the Earth’s surface are responsible for many of the processes that govern weather, climate, human health, and energy use. This exchange, commonly known as the surface energy balance (SEB), determines the near-surface thermodynamic state by partitioning the available energy into surface fluxes. The net all-wave radiation is often the primary energy source, while the heat storage and sensible and latent heat fluxes account for the majority of energy distributed elsewhere. While the SEB of various natural environments(trees, crops, soils) has been well-observed and modeled, the urban surface energy balance remains elusive. This is due to the heterogeneity of urban land cover, where the surface cover is dominated by impervious materials (buildings, roads, and pavements) interspersed with vegetation and bare soil. The impervious materials differ in their hygro-thermal properties based on their inherent capacity to conduct and retain heat and moisture. Traditional observation techniques are unable to capture all the relevant processes in cities, and as a result, the urban surface energy budget remains mostly unknown. In this seminar, novel techniques that combine traditional boundary layer turbulence measurements and advanced remote sensing methods are presented as solutions to advance our understanding of urban surface energy balance. Here, new methodologies are developed that apply remote sensing-based algorithms to urban environments. The first topic uses satellite measurements to derive near-surface air temperature for urban areas- this has yielded a publication (DOI: 10.1016/j.rse.2019.111495). Next, a satellite-based algorithm that approximates the net all-wave radiation is presented, using machine learning and land cover information. Lastly, two novel methods for predicting the heat stored in cities are introduced (one of which resulted in a publication with DOI: 10.1016/j.rse.2020.112125). Overall, this dissertation presents new knowledge and develops novel algorithms that expand and advance our understanding of urban thermodynamics, which impacts how we observe and model agricultural processes, human vulnerability to weather and climate, better predict energy use, and improve the sustainability of our cities.
Hrisko, Joshua, "Toward Closing the Urban Surface Energy Balance Using Satellite Remote Sensing" (2020). CUNY Academic Works.