Spatial Distribution of Near-surface Air Temperature in the Glacierized Areas of the Tibetan Plateau

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 contains spatial distribution of near-surface air temperature in the glacierized areas of the Tibetan Plateau, which has been processed using meteorological observation data, thermal infrared remote sensing data and glacier boundaries from China's Second Glacier Inventory. The data covers a high-altitude area of the Qinghai-Tibet Plateau for seven glacier regions (Guliya, Aru, Naimona'nyi, Gagze, Dunde, Parlung, and XiaodongKemadi), with a spatial resolution of 30 meters, spanning elevations of 3,924-6,078 m and temperatures of -42°C to +21°C. 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. Missing values were reconstructed through masked nearest-neighbor interpolation, followed by spatial smoothing to minimize residual inaccuracies during the temperature field updating process. This approach (gap-filling, bias-correction, smoothing) ensured robust spatialization of near-surface air temperature data. Technical validations are detailed in Accuracy and Completeness Reports. The dataset provides temperature distributions for glacier-covered areas of the Qinghai-Tibet Plateau. This dataset is suitable for climate research, environmental monitoring, and cryosphere.

The experiment's temporal coverage is discontinuous; the eight datasets have different time ranges. The temporal coverage includes Southeastern data (2023-01 to 2023-03), Xiaodongkemadi data (2012-05 to 2015-08), and Aru, Dagze, Dunde, Guliya, Naimona'nyi, and Parlung data (all from 2019-01, with end dates ranging from 2019-10 to 2019-11).
Southeastern data: 2023-01 to 2023-03
Xiaodongkemadi data: 2012-05 to 2015-08
Aru data: 2019-01 to 2019-10
Dagze data: 2019-01 to 2019-10
Dunde data: 2019-01 to 2019-11
Guliya data: 2019-01 to 2019-10
Naimona'nyi data: 2019-01 to 2019-10
Parlung data: 2019-01 to 2019-10