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<p>Understanding land surface processes in arid and semiarid environments is crucial for ecosystem dynamics and water management. This data descriptor presents a comprehensive dataset collected during the MUlti-Scale Observation Experiment on land Surface temperature using UAV remote sensing (MUSOES-UAV). Acquired from June to October 2020 at typical semiarid sites in the midstream of the Heihe River Basin, China, the dataset includes high-resolution thermal infrared (TIR) and multispectral images from a UAV. The TIR data were corrected for temperature drift, while the multispectral images underwent radiometric relative normalization to ensure data consistency. Concurrently, ground-based observations were collected from TIR radiometers and automatic weather stations. The final dataset consists of TIR brightness temperature mosaics, multispectral mosaics, and normalized difference vegetation index (NDVI) maps, complemented by the ground-based measurements. This multi-scale dataset is a valuable resource for monitoring environmental changes and provides a foundational basis for developing and validating algorithms for UAV remote sensing applications.</p>

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Comprehensive UAV and ground data for typical semiarid sites in the midstream of the Heihe River Basin

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月1日

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  • Ji Zhou,
  • Ziwei Wang,
  • Shaomin Liu,
  • Mingsong Li,
  • Jin Ma,
  • Lingxuan Meng,
  • Nanjie Feng

摘要

Understanding land surface processes in arid and semiarid environments is crucial for ecosystem dynamics and water management. This data descriptor presents a comprehensive dataset collected during the MUlti-Scale Observation Experiment on land Surface temperature using UAV remote sensing (MUSOES-UAV). Acquired from June to October 2020 at typical semiarid sites in the midstream of the Heihe River Basin, China, the dataset includes high-resolution thermal infrared (TIR) and multispectral images from a UAV. The TIR data were corrected for temperature drift, while the multispectral images underwent radiometric relative normalization to ensure data consistency. Concurrently, ground-based observations were collected from TIR radiometers and automatic weather stations. The final dataset consists of TIR brightness temperature mosaics, multispectral mosaics, and normalized difference vegetation index (NDVI) maps, complemented by the ground-based measurements. This multi-scale dataset is a valuable resource for monitoring environmental changes and provides a foundational basis for developing and validating algorithms for UAV remote sensing applications.