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Toward the method for generating 250-m all-weather land surface temperature for glacier regions in Southeast Tibet

  • role： First author第一作者
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

  • Email：huangzhiming233@163.com
  • Introduction：黄志明，1996年生，男，硕士研究生，研究方向为热红外定量遥感。E-mail： huangzhiming233@163.com
HUANG Zhiming1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

    The Yangtze Delta Region Institute (Huzhou),University of Electronic Science and Technology of China, Huzhou 313001, China

  • Email：jzhou233@uestc.edu.cn
  • Introduction：周纪，1983年生，男，教授，研究方向为定量遥感。E-mail： jzhou233@uestc.edu.cn
ZHOU Ji12*,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

DING Lirong1,  
  • Affiliation：

    78127 troops, People's Liberation Army, Chengdu 610000, China

ZHANG Rucai3,  
  • Affiliation：

    Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China

ZHANG Xiaodong4,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

MA Jin1

résumé

All-weather LST with higher spatial resolution are important for the detailed monitoring and early-stage warning of glacial debris flow disasters in Southeast Tibet. Based on the latest 1-km all-weather LST (TRIMS LST) product, a variety of LST downscaling methods were compared in this paper, and the optimal downscaling method was determined to generate all-weather LST at 250 m in the glacier regions in Southeast Tibet.The application of remotely sensed all-weather LST with insufficient spatial resolution in fine disaster monitoring has many limitations. At present, there are few studies on the spatial downscaling of all-weather LST. In this paper, the 1-km/250-m elevation, slope, aspect, land cover type, vegetation index, surface reflectivity, and snow index were used as the descriptors of LST. Combining the moving window strategy, a variety of LST downscaling methods were compared in this paper, and the optimal downscaling method was used to improve the spatial resolution of all-weather LST (TRIMS LST) from 1 km to 250 m.Based on the RMSE between the 250-m all-weather LST generated in different moving windows and the original 1-km TRIMS LST, and the 20×20 km is determined as the best downscaling window. The evaluation results based on the measurement data from ground sites and image quality indices show that LightGBM has optimal downscaling performance. The RMSE and MBE between the 250-m all-weather LST generated by the LightGBM and the measurement soil temperature during the day are 2.25 K and -0.01 K and during the night are 2.15 K and 0.74 K. Compared with the original 1-km TRIMS LST, RMSE and MBE were reduced by approximately 0.25 K. Evaluation results based on the air temperature from ground sites show that the 250-m all-weather LST also has good reliability at the glacier. The Q index shows that the 250-m all-weather LST generated by the LightGBM not only maintains high consistency with the original 1-km all-weather LST in terms of spatial pattern and amplitude, but also provides a large amount of spatial detailed information of surface temperature. The SIFI index further indicates that the 250-m LST has high image quality without over-sharpening.The all-weather LST with a 250-m spatial resolution can be used as reliable and high-resolution LST data for the monitoring and early warning of disasters such as glacial debris flow in the glacier area of Southeast Tibet, which have positive significance for disaster monitoring in this area. Since the spatial resolution of some descriptors (e.g., vegetation index) is 250 m, the all-weather LST can only be downscaled to a spatial resolution of 250 m in the current stage. How to improve the spatial resolution of descriptors for further determining a higher spatial-resolution all-weather LST still needs more in-depth research.

mots-clés

LST;downscaling;all-weather;glaciers;Southeast Tibet

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