Abstract
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.
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Data availability
The datasets associated with this article, including temperature drift-corrected BT orthomosaics, multispectral orthomosaics, NDVI maps, as well as automatic weather station and ground-based TIR radiometer observations, are freely available from the National Tibetan Plateau Center. The dataset’s accession number and direct download link are as follows: https://doi.org/10.11888/Terre.tpdc.302412.
Code availability
The primary data processing, including UAV image stitching and orthomosaic creation, was performed using the software Pix4D Mapper (v4.7). For data plotting and visualization, OriginPro (v2024a) was used, and for map production, ArcMap (v10.2) was employed. To enhance the reproducibility of our dataset, the core custom Python (v3.10) scripts utilized for the correction algorithms, specifically the DRAT method and the relative radiometric normalization of multispectral images, have been organized and made publicly available. These scripts, along with a detailed README file for usage instructions, are deposited in the Zenodo repository and can be accessed at https://doi.org/10.5281/zenodo.18869078. Additionally, minor data handling and quality control tasks were performed leveraging standard Python libraries such as gdal, rasterio, and pandas. All key parameters and methods for data processing are detailed in the “Methods” section of the manuscript.
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Acknowledgements
This work was supported by the National Key Research and Development Program of China under Grant 2023YFF1303502. The authors would like to express their deepest gratitude to all participants involved in the MUSOES-UAV experiment. Their diligent efforts in various stages, including experimental design, data collection, and data preprocessing, have provided favorable and well-organized conditions for the successful execution of this study.
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J.Z. secured funding for the project, conceived the experimental framework, curated all research data, provided critical supervisory guidance, and directed the manuscript revision and peer-review strategy. Z.W. drafted the initial manuscript, led the comprehensive manuscript revision, spearheaded the development and validation of data correction algorithms, performed experimental data visualization, and actively participated in field campaigns. S.L. contributed meteorological datasets from automated weather stations and provided logistical support for personnel coordination and instrumentation deployment during fieldwork. M.L. and J.M. participated in the conceptualization of experimental protocols and executed field data collection. L.M. was responsible for acquiring UAV-borne remote sensing raw data. N.F. performed data curation and quality assurance through systematic archiving and inspection procedures. All authors contributed intellectually to manuscript composition, critically reviewed and revised the manuscript for scientific accuracy, and approved the final version.
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Zhou, J., Wang, Z., Liu, S. et al. Comprehensive UAV and ground data for typical semiarid sites in the midstream of the Heihe River Basin. Sci Data (2026). https://doi.org/10.1038/s41597-026-07151-0
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DOI: https://doi.org/10.1038/s41597-026-07151-0
