The Qinghai-Tibet Plateau, known for its high altitude, cold climate, and fragile ecosystem, presents unique challenges and opportunities for the implementation of an intelligent sponge urban system. The heat island effect, a phenomenon where urban areas experience higher temperatures compared to surrounding rural areas, can be particularly problematic in such a sensitive environment. Predicting and mitigating heat island intensity is crucial for improving urban livability and environmental sustainability. To develop a procedure for predicting heat island intensity in an intelligent sponge urban system, ensuring accurate and real-time predictions through a series of steps. Collect parameter information of the underlying surface using meteorological observation data from the sponge city, field observation data, and investigation data of the sponge city. Gather comprehensive data on the physical and environmental characteristics of the urban surface. Establish a set of digital labels with feature data derived from the collected information. Add the labeled data to the training sample set for the prediction model of sponge city surface heat island intensity. A crucial input for establishing a real-time prediction model. Train the prediction model function for sponge city surface heat island intensity using the data. This Experiment contains 2 datasets, corrected surface air temperature data and training data. The corrected surface air temperature data has been processed using meteorological observation data and thermal infrared remote sensing data. The data covers a high-altitude area of the Qinghai-Tibet Plateau, with a spatial resolution of 30 meters. The original temperature data were obtained from multiple sources, including thermal infrared remote sensing data from Landsat 8(L8) and Landsat 9 (L9) Collection 2 (C2) Level 2 (L2) products, as well as ground station measurements from National Tibetan Plateau/Third Pole Environment Data Center. The regression algorithms in supervised learning was trained to correct for biases and inaccuracies in updating the spatialized data of near-surface air temperature. This dataset is suitable for climate research, environmental monitoring, and other applications requiring relatively accurate surface air temperature data.

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.