Near-surface air temperature dataset for the Qinghai-Tibet Plateau (2019) derived from thermal infrared remote sensing and elevation-constrained modeling

Project: Heat Island Intensity Prediction in an Intelligent Sponge Urban System in the Qinghai-Tibet Plateau - This project investigates how intelligent sponge city technologies can mitigate urban heat islands (UHI) in the Qinghai-Tibet Plateau. It aims to develop scalable models for climate-resilient urban planning in high-altitude regions through a series of targeted experiments.
Supported by grant LJ200080773 ('Intelligent Urban Rainwater Collection Module System Application'), this project develops a machine learning framework for urban heat island (UHI) prediction in sponge cities. The framework integrates thermal infrared remote sensing and meteorological data to jointly analyze UHI intensity and rainwater storage capacity, supporting ecological runoff management and heat mitigation strategies.
Summary: 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.