Projection of urban land surface temperature: An inter- and intra-annual modeling approach

An easily applied and efficiently exploited approach was proposed for the prediction of inter-and intra-annual land surface temperature (LST) in a city's central zone. Chengdu is chosen as the study area to demonstrate the applicability of the novel approach. Built-up areas were extracted using Landsat images in 2008, 2013, and 2019 and projected for 2030 using the cellular automata-Markov chain model, indicating that built-up areas grew nearly 109% during 2008-2019 in Chengdu and will continue to grow up to 2030. Employing the multiple linear regression, the LST of built-up areas in 2019 was predicted using the normalized difference built-up index (NDBI) and the mean LST of its surrounding pixels (LST-R3) in 2013 (R2 = 0.981). The model's intra-annual application for 2019 showed a mean LST deviation of 0.93 degrees C. According to the model's inter-annual application, the projected LST of the built-up areas in 2030 will increase to 36.5 & PLUSMN; 2.0 degrees C. The results can contribute to achieving a thermally comfortable urban envi-ronment by providing insights into the impact of urban expansion on the urban heat island (UHI) phenomenon under the limited availability of continuous LST data.

Automated summary

An approach for predicting inter-and intra-annual land surface temperature (LST) in a city's central zone was proposed and demonstrated using the case of Chengdu city. The study extracted built-up areas using Landsat images and projected future growth using the cellular automata-Markov chain model. Multiple linear regression was employed to predict LST, showing high accuracy for both intra-annual and inter-annual applications. The results provide insights into the impact of urban expansion on the urban heat island phenomenon and contribute to achieving a thermally comfortable urban environment.