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.
Wang, Tianyun; Yang, Lu; Zhang, Deyuan (2025). Spatialization of near-surface air temperature and updating based on thermal infrared remote sensing information in the Qinghai-Tibet Plateau. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/QTP
This report evaluates the accuracy of several models on a dataset focused on the spatialization of near-surface air temperature and its updating based...
Description
This report evaluates the accuracy of several models on a dataset focused on the spatialization of near-surface air temperature and its updating based on thermal infrared remote sensing information in the Qinghai-Tibet Plateau. Each model was trained using two variables from the training set and tested using meteorological observation data from six high-altitude glaciers in the Qinghai-Tibet Plateau (2019). The regression models are trained utilizing air temperature data from the coverage area of meteorological observation stations and thermal infrared remote sensing information, which are subscribed the same grids. The supervised learning algorithms used for training the models are Random Forest Regression and Decision Tree Regression. Key performance metrics include the corrected surface air temperature, root mean squared error (RMSE), and standard deviation (STD) of the test stations within the training coverage area. The results indicate that the corrected surface air temperature is more accurate than the surface temperature (ST) from Landsat 8-9 Collection 2 Level 2 (L2SP) products, with an improvement of 0.09 to 12 degrees Celsius at most test station locations.RMSE: The root mean squared error of the corrected surface air temperature was significantly lower at most test station locations. STD: The standard deviation of the test stations within the training coverage area showed a reduced variability in the corrected surface air temperature. The corrected surface air temperature provides a more accurate representation of the near-surface air temperature in the Qinghai-Tibet Plateau.The use of thermal infrared remote sensing data enhances the spatial resolution and coverage of the surface temperature.The accuracy of the models may vary depending on the specific environmental conditions and the quality of the input data.
Completeness report
This report evaluates the completeness of the data analysis project, focusing on data integrity, functional implementation, compliance and resource av...
Description
This report evaluates the completeness of the data analysis project, focusing on data integrity, functional implementation, compliance and resource availability. The datasets covered the required time periods and geographical areas, all necessary data points are included. The necessary global variables have been set and are now available for the application. Data has been validated for accuracy and consistency. Data interpolation and filter steps have been documented. Missing value have been identified and documented as float 32 NaN. All corrected surface air temperature variables have been implemented, each variable has been tested and verified. Test datasets have been conducted to ensure all functionalities work as expected, test sets and results are documented. The data has been saved in compliance with the NetCDF4 standard. The dataset has been saved in the NetCDF4 format, ensuring full compliance with the NetCDF4 standard. This compliance was verified using the atmodat data checker(ATMODAT Standard v3.0). All necessary tools and software are available. Data Source are provided: source_1: Image courtesy of the U.S. Geological Survey, LANDSAT_8 OLI/TIRS, L2SP, FILE_NAME_BAND_ST_B10. source_2: Yang, W. (2021). Temperature data of six glaciers in high altitude area of Qinghai Tibet Plateau (2019). National Tibetan Plateau / Third Pole Environment Data Center. https://doi.org/10.11888/Cryos.tpdc.271916. Suggestions for additional improvements or enhancements, indicating potential areas for further improvement, collect more high-quality meteorological data to enhance the training set, explore advanced data techniques to improve data consistency combined multiple models for the next phase of the project.
FAIR
F-UJI result: total 66 %
Description
Summary:
Findable: 6 of 7 level;
Accessible: 2 of 3 level;
Interoperable: 3 of 4 level;
Reusable: 5 of 10 level
SQA - Scientific Quality Assurance 'approved by author'
Result Date
2025-02-21
Technical Quality Assurance (TQA)
TQA - Technical Quality Assurance 'approved by WDCC'
Description
1. Number of data sets is correct and > 0: passed; 2. Size of every data set is > 0: passed; 3. The data sets and corresponding metadata are accessible: passed; 4. The data sizes are controlled and correct: passed; 5. The temporal coverage description (metadata) is consistent to the data: passed; 6. The format is correct: passed; 7. Variable description and data are consistent: passed
Method
WDCC-TQA checklist
Method Description
Checks performed by WDCC. The list of TQA metrics are documented in the 'WDCC User Guide for Data Publication' Chapter 8.1.1
[1] Wei,Yang(National Tibetan Plateau / Third Pole Environment Data Center). (2021). Temperature data of six glaciers in high altitude area of Qinghai Tibet Plateau (2019). doi.org/10.11888/Cryos.tpdc.271916