A modified model of surface temperature inversion based on Landsat 8 remote-sensing data and measured data

ABSTRACT

This article verified the error between inversed land surface temperature (LST) and measured LST and developed the modified model based on Landsat 8 remote-sensing data. First, a single-channel algorithm was used to invert the surface temperature using four Landsat 8 remote-sensing images and the LST of the 98 measured points were obtained meantime. Then, the modified model between inversed LST and measured LST was developed based on LST for the 74 measured points. Finally, the developed models were used to modify the inversion temperatures at other 24 measured points, and the mean absolute error (MAE) and mean square error (MSE) between the measured temperature and the inversed temperature before and after the modification were compared to verify the validity of the model. The results showed that the MAE and the MSE of temperature for the 24 measured points used for verification reduced by 0.26 and 0.20 K, respectively, after modification. The development of the modified model can provide an important reference for using Landsat 8 remote-sensing image to invert surface temperature in other regions.

Geolocation information

The study area was partial region of Tianjin City of China, which is located between 38°34′–40°15′ N and 116°43′–118°04′ E.

Acknowledgements

This work was supported by the Natural Science Foundation of Tianjin, China under Grant No. 16JCYBJC21400.

Disclosure statement

No potential conflict of interest was reported by the authors.

Author Contributions

All of the authors contributed extensively to the work. Ying Fu designed and finished the experiments, analysed the data, and wrote the article. Qiaozhen Guo proposed key ideas and gave suggestions for modifications to the manuscript. Xiaoxu Wu contributed to the data analysis and modified the manuscript. Chengwei He, Xiao Sang, and Tongyao Xie participated in the collection of measured data.

Additional information

Funding

This work was supported by the Natural Science Foundation of Tianjin, China: [Grant Number 16JCYBJC21400].