Prediction of Land-Surface Temperatures of Jaipur City Using Linear Time Series Model

Abstract

All cities of the world have undergone rapid urbanization. Consequently, urban areas encounter higher surface and air temperatures than the surrounding nonurbanized areas and exhibit urban heat island (UHI) effect. Surface temperature derived from remote sensing data has been used for analyzing the UHI effect over a number of cities. This study has been carried out to predict the land-surface temperature (LST) of Jaipur city, India. Remote sensing data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors have been used for the prediction. 10-year linear time series (LTS) model has been developed using enhanced vegetation index (EVI), elevation, and LST for the prediction of future LST. Model output has been validated using LST data of the year 2014. A comparison of model-estimated LST and measured LST shows that mean absolute error (MAE) varies from 0.292 to 0.353 and mean absolute percentage error (MAPE) varies from 0.098 to 0.123. High correlation exists between the model-estimated LST and measured LST with an average ${R}^{2}$ value of 0.95. LTS model developed in this study can be used for many studies involving LST, and it can be a significant tool for the prediction of UHI effect at any location.