Estimating urban LST using multiple remotely sensed spectral indices and elevation retrievals

Abstract

• multivariate regression analysis was used to estimate LST at 30 m resolution. • Local climate zones were also mapped from 30 m resolution optical data. • model based on variables produced highest LST estimation accuracy. • r-squared value was (>0.9) compared to using the variables individually. • Maps of predicted and actual LST showed similar spatial distribution. Detailed spatial characterization of prediction of Land Surface Temperature (LST) offers opportunities for assessment of sustainability of urban growth patterns. Hence, the development of algorithms for predicting LST from remotely sensed spectral indices has potential to accurately yield temperature maps at moderate to high spatial resolution useful for development planning and assessment of impact of growth patterns on the thermal environment of urban areas. Due to varied strengths in indices, an algorithm based on many spectral indices has potential to increase accuracy of LST prediction as each input index will contribute unique information to the relationship. In this study, 14 spectral indices and two topographic variables (elevation and aspect) were tested for their potential to accurately predict LST. Variable selection and determination of coefficients for best fitting algorithm was done using an iterative and automated procedure which converged at a linear function of best performing combination of indices in an Ordinary Least Squares multi-variate regression approach. Local Climate Zones (LCZ) were also mapped from optical data to test the performance of the developed algorithm in explaining the relationship between LST and LCZ. Results showed that all indices and topographic retrievals were selected for use in the model which produced highest accuracy. The developed algorithm showed high performance as indicated by high r-squared value (>0.9) to the actual LST. The multiple indices based algorithm had a significantly stronger relationship with LST than individual indices. Maps of estimated and actual LST showed similar spatial distribution, while prediction errors were high in LCZs with tall buildings due to complexity. The developed algorithm sustained relationship between LST and LCZ, thus consistent and reliable. The findings of the study provide insight into the use of indices to predict LST at high accuracy, important for detailed understanding of implications of city growth on the thermal environment.