Scholarlink

Land surface temperature (LST) has been playing a vital role in meteorology studies. The integration of satellite thermal infrared (TIR) and passive microwave (PMW) remote sensing observations provides a possibility to obtain moderate/high-resolution all-weather LST which has been an urgent necessity. However, due to difficulties in the integration, i.e., the PMW-TIR LST discrepancies in physical meaning and spatial scales and PMW data gap from the polar-orbited satellite, there is no official TIR-PMW integrated all-weather LST products at moderate/high spatial resolutions for merging Chinese satellite sensor ovservations. Under this context, with the help of machine-learning model, this study revises a former-developed physical method for merging the PMW-TIR observations and applies it to the Chinese FY-3 Microwave radiation imager (MWRI) data (10 km) and Visible and InfraRed Radiometer (VIRR) data (1 km) remote sensing data to generate 1-km all-weather gap-free LST over the Tibetan Plateau observations. Results show that the merged all-weather LST has accuracy of 1.50–2.92 K when validated against in-situ LSTs from ground stations and highly consistent to the original VIRR LST. This study would benefit the integration of Chinese satellite PMW-TIR integrations and meteorological research and applications requiring all-weather LST at high spatial resolution over large scales.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Estimation of 1-km All-Weather Land Surface Temperature by Integration of FY-3 MWRI and VIRR Remote Sensing Observations

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Xiaodong Zhang,
  • Lingge Qu,
  • Lifei Jiang,
  • Ruanyu Zhang,
  • Pingkai Wang

摘要

Land surface temperature (LST) has been playing a vital role in meteorology studies. The integration of satellite thermal infrared (TIR) and passive microwave (PMW) remote sensing observations provides a possibility to obtain moderate/high-resolution all-weather LST which has been an urgent necessity. However, due to difficulties in the integration, i.e., the PMW-TIR LST discrepancies in physical meaning and spatial scales and PMW data gap from the polar-orbited satellite, there is no official TIR-PMW integrated all-weather LST products at moderate/high spatial resolutions for merging Chinese satellite sensor ovservations. Under this context, with the help of machine-learning model, this study revises a former-developed physical method for merging the PMW-TIR observations and applies it to the Chinese FY-3 Microwave radiation imager (MWRI) data (10 km) and Visible and InfraRed Radiometer (VIRR) data (1 km) remote sensing data to generate 1-km all-weather gap-free LST over the Tibetan Plateau observations. Results show that the merged all-weather LST has accuracy of 1.50–2.92 K when validated against in-situ LSTs from ground stations and highly consistent to the original VIRR LST. This study would benefit the integration of Chinese satellite PMW-TIR integrations and meteorological research and applications requiring all-weather LST at high spatial resolution over large scales.