High Spatial Resolution Retrieval of LST and LSE for the Urban Environment

Understanding the changing and complex urban thermal environment is key to addressing the health and sustainability of the cities in which more than half of the world’s population live. The monitoring and assessment of the thermal environment requires spatial resolution that so far has precluded air temperatures from being a viable parameter in most cities. Land surface temperatures (LSTs) offer the ability through satellite remote sensing to investigate the urban environment in a robust and consistent manner. Additionally land surface emissivity (LSE) is required to enable accurate LST estimation and characterise broad-scale thermal infra-red properties of materials. In this thesis, the first optimal estimation of simultaneous LST and LSE data optimised to be robust for urban areas with highly complex surfaces is presented. It uses the thermal channels of the ASTER instrument with a spatial resolution of 90 m. In simulations the algorithm retrieved LST to 1 K or better, and LSEs to within 0.01. The simulation uncertainties retrieved are better than 1 K in LST and 0.015-0.017 for LSE. This marks the first usage of an inverse method with ASTER data. Verification of the LSE for a non-urban scene (Algodones) was undertaken, through inter-comparison with the TES method. Results agreed well with both TES and the validation site in channel 12 and with very low retrieval radiance residuals. The algorithm was also used in three urban case studies. In each, this scheme was able to address key scientific issues, including urban green space and rapid urban expansion, using a combination of the LST and the LSE. The high accuracy of the retrieved LSE was able to distinguish characteristic LSE spectra and identify surface changes. These results show the retrieval of robust and scientifically meaningful LST and LSE data for the heterogeneous urban environment from ASTER, vital to urban studies