Near-surface air temperature dataset for the Qinghai-Tibet Plateau (2019) derived from thermal infrared remote sensing and elevation-constrained modeling - corrected data
This dataset provides 30m-resolution spatialized near-surface air temperature products for the Qinghai-Tibet Plateau, derived from meteorological observations (2019) and thermally corrected Landsat 8/9 imagery (Collection 2 Level 2). Key processing steps include: Elevation-Aware Regression: Temperature records from six high-altitude glaciers were corrected using elevation-dependent modeling. The data covers a high-altitude area of the Qinghai-Tibet Plateau, spanning elevations of 2,156-7,326 m and temperatures of -40°C to +12°C. Temporal Interpolation: A 3rd-order uniform B-spline curve was applied to interpolate temperature at two time points. Noise Reduction: Filtering and smoothing operations were implemented to enhance data quality, integrating Topographic Data (2021) with glacier temperature records. Normalization: The elevation variable was linearly normalized to [0, 1] via Min-Max scaling to ensure compatibility with machine learning workflows. This dataset is optimized for supervised regression tasks (e.g., Random Forest, Multilayer Perceptron (MLP), or Decision Tree regression), supporting bias correction in spatially distributed air temperature updates.
[ Derived from parent entry - See data hierarchy tab ]
Wang, Tianyun; Yang, Lu; Zhang, Deyuan; Zhou, Juncheng; Zhou, Tao; Song, Haolin (2025). Near-surface air temperature dataset for the Qinghai-Tibet Plateau (2019) derived from thermal infrared remote sensing and elevation-constrained modeling. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/QTPTIR
Near-surface air temperature dataset for the Qinghai-Tibet Plateau (2019) derived from thermal infrared remote sensing and elevation-constrained modeling