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

A linear time series (LTS) model developed for predicting land-surface temperature (LST) in Jaipur, India, demonstrates high accuracy with mean absolute error (MAE) ranging from 0.292 to 0.353 and mean absolute percentage error (MAPE) from 0.098 to 0.123. The model, validated using LST data from 2014, shows a strong correlation (average R² of 0.95) between predicted and measured temperatures. Utilizing remote sensing data from MODIS and ASTER sensors, the study incorporates enhanced vegetation index (EVI), elevation, and historical LST to forecast future temperatures effectively. This approach provides a robust tool for analyzing urban heat island (UHI) effects, applicable to various urban studies.

Mathew, A., Sreekumar, S., Khandelwal, S., Kaul, N., & Kumar, R. (2016). Prediction of Land-Surface Temperatures of Jaipur City Using Linear Time Series Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8), 3546–3552. https://doi.org/10.1109/jstars.2016.2523552