Near-surface air temperature dataset for the Qinghai-Tibet Plateau (2019) derived from thermal infrared remote sensing and elevation-constrained modeling - metadata information
Wang, Tianyun
Additional Info
Summary
This study presents a high-resolution (30 m) spatialized near-surface air temperature dataset for the Qinghai-Tibet Plateau (QTP) by integrating Landsat 8/9 thermal imagery (Collection 2 Level 2) with elevation-corrected machine learning regression (Random Forest, Decision Tree, and Multilayer Perceptron). The proposed method overcomes empirical lapse-rate limitations through an optimized spatialization approach combining Local Representatives (LRs) and Inverse Distance Weighting (IDW), enhancing spatial representativeness for elevation-dependent microclimates. Processing includes elevation-aware bias correction, spatiotemporal interpolation (3rd-order B-spline), Gaussian-filtered topographic integration (2021 QTP data), and rigorous validation using 31 Temperature_Error Graphs to quantify accuracy (RMSE, SD, and permutation importance). The dataset covers elevations of 2,156–7,326 m and temperatures of ?40°C to +12°C, demonstrating robust performance with independent validation from six high-altitude glaciers (Yang et al, 2022). Publicly available training data (DOI:10.26050/WDCC/QTP) support applications in glaciology, hydrology, and climate research requiring precise temperature estimates.
Wang, Tianyun (2025). Near-surface air temperature dataset for the Qinghai-Tibet Plateau (2019) derived from thermal infrared remote sensing and elevation-constrained modeling - metadata information. World Data Center for Climate (WDCC) at DKRZ. https://www.wdc-climate.de/ui/entry?acronym=QTPTIRmdinfo
Near-surface air temperature dataset for the Qinghai-Tibet Plateau (2019) derived from thermal infrared remote sensing and elevation-constrained modeling