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wdc-climate.de:5311409 eng; USA collection QTPTIR Dr. Tianyun Wang Shenyang University of Technology https://www.sut.edu.cn pointOfContact 2025-09-11T08:39:47 ISO 19115 ISO 19115-2:2009 Near-surface air temperature dataset for the Qinghai-Tibet Plateau (2019) derived from thermal infrared remote sensing and elevation-constrained modeling 2025-09-25 publication 1 QTPTIR doi:10.26050/WDCC/QTPTIR Dr. Tianyun Wang Shenyang University of Technology https://www.sut.edu.cn Email: tywang_iem@163.com principalInvestigator Dr. Tianyun Wang Shenyang University of Technology https://www.sut.edu.cn Email: tywang_iem@163.com author Prof. Dr. Lu Yang Shenyang University of Technology https://www.sut.edu.cn Email: yanglu515@163.com author Dr. Deyuan Zhang Shenyang Aerospace University not filled Email: dyzhang@sau.edu.cn author Juncheng Zhou Shenyang University of Technology https://www.sut.edu.cn Email: zhoujuncheng040909@163.com author Tao Zhou Shenyang University of Technology https://www.sut.edu.cn Email: zhoutao_sut@163.com author Haolin Song Shenyang University of Technology https://www.sut.edu.cn Email: songhaolin0224@163.com author WDCC publisher This dataset provides high-resolution (30 m) spatialized near-surface air temperature products for the Qinghai-Tibet Plateau, updated using thermal infrared remote sensing data from Landsat 8 (L8) and Landsat 9 (L9) Collection 2 (C2) Level 2 (L2) products, combined with elevation-corrected regression modeling. The dataset includes corrected temperature files (adjusted via machine learning-based elevation corrections) for model development. The elevation corrections were performed using Topographic Data of Qinghai-Tibet Plateau (2021), integrated via Gaussian filtering to enhance spatial consistency in high-elevation regions. Supervised learning regression models (Random Forest Regression, Multilayer Perceptron regression, or Decision Tree regression) were applied to minimize Thermal Infrared Radiation-derived temperature biases and optimize high-altitude temperature estimation. The near-surface temperature lapse rate (LR) is a critical parameter in glaciological and hydrological models, but existing approaches often rely on empirical estimations with limited spatial representativeness. To mitigate these limitations, an optimized temperature spatialization method is proposed, fusing Local Representatives (LRs) across glacierized regions through Inverse Distance Weighting (IDW). This approach accounts for elevation-dependent microclimates while maintaining regional consistency. This dataset is suitable for climate research, and environmental modeling requiring high-resolution near-surface air temperature data. Dr. Tianyun Wang Shenyang University of Technology https://www.sut.edu.cn pointOfContact NetCDF Qinghai-Tibet Plateau inverse distance weighting machine learning machine learning regression near-surface air temperature spatialization thermal infrared remote sensing CC-BY-4.0: Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ eng; USA 80.21 98.15 29.18 39.52 gregorian 2019-01-02 2019-10-17 info:eu-repo/grantAgreement/SUT//LJ200080773/CN//Intelligent Urban Rainwater Collection Module System Application surface_bidirectional_reflectance The surface called "surface" means the lower boundary of the atmosphere. "Bidirectional_reflectance" depends on the angles of incident and measured radiation. Reflectance is the ratio of the energy of the reflected to the incident radiation. A coordinate variable of radiation_wavelength or radiation_frequency can be used to specify the wavelength or frequency, respectively, of the radiation. [CF-Standard Name]; unit: 0.01 air_temperature Air temperature is the bulk temperature of the air, not the surface (skin) temperature. [CF-Standard Name]; unit: degC NetCDF 10759 https://www.wdc-climate.de/ui/entry?acronym=QTPTIR information collection