Taylor & Francis Group
Browse
tjde_a_1593527_sm2391.docx (2.7 MB)

Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data

Download (2.7 MB)
journal contribution
posted on 2019-03-25, 17:16 authored by Joanna Zawadzka, Ron Corstanje, Jim Harris, Ian Truckell

We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30 m) resolution down to 2–4 m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83 K prior to and 0.76–1.21 K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.

Funding

This research (Grant Number NE/ J015067/1) was conducted as part of the Fragments, Functions and Flows in Urban Ecosystem Services (F3UES) project as part of the larger Biodiversity and Ecosystem Service Sustainability (BESS) framework. BESS is a six-year programme (2011–2017) funded by the UK Natural Environment Research Council (NERC) and the Biotechnology and Biological Sciences Research Council (BBSRC) as part of the UK’s Living with Environmental Change (LWEC) programme. This work presents the outcomes of independent research funded by NERC and the BESS programme, and the views expressed are those of the authors and not necessarily those of the BESS Directorate or NERC Environmental Bioinformatics Centre.

History