Abstract
The feasibility of integrating urban biophysical descriptors into land surface temperature (LST) prediction in a rapidly urbanizing region, Jinan, China, was explored. We combined several models, including LST and an urban biophysical descriptor generation algorithm, through regression analysis and cellular automata-Markov modeling. First, the relationship between the indices, including the soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI), index-based built-up index (IBI), and the LST was established; then, the future LST was simulated and predicted. The results showed that LST has a negative correlation with SAVI (R2 = 0.905, P < 0.01) and MNDWI (R2 = 0.911, P < 0.01) while having a high positive correlation with IBI (R2 = 0.801, P < 0.01). Additionally, correlations between the simulated values and retrieval of different land use/cover types show good agreement, with a mean R from 0.6 to 0.9, such that this method can be used to simulate future urban heat island effects. If the development trend remains unchanged, the urban heat island effect of Jinan will continue to strengthen by 2030 because of the increase in impervious ground and reduction in green vegetation. These results can provide guidance for Jinan’s urban construction and planning, which promote sustainable urbanization.
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