| Autor: |
Mohamadi B; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China., Chen S; Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangzhou 510070, China., Balz T; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China., Gulshad K; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China., McClure SC; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China. |
| Abstrakt: |
The temporal analysis of land surface temperature (LST) has generally been studied using data from the same season, as temperature varies greatly over time. However, the cloud cover in thermal remotely sensed images and the coarse resolution of passive sensor system significantly limits data availability of same season for comparative temporal analysis in many parts of the world. To address this problem, we propose a new method for temporal monitoring of surface temperature based on LST normalization (LSTn); deploying the average open water temperature to normalize LST when monitoring temporal change in the surface temperature of newly coastal reclaimed areas. This method was applied in the Lingding Bay area, Guangdong Province, Southern China. Original LST and LSTn values were calculated for years 1987, 1997, 2007, and 2017. In contrast to the original LST, results show that LSTn can reduce seasonal variability when monitoring temporal change in surface temperatures. Additionally, LSTn revealed pronounced differences between the temperature of impervious surfaces and other land cover types. This method offers more robust detection of surface urban heat islands than original LST in newly developed coastal areas. |
