Deep Learning-Based 500 m Spatio-Temporally Continuous Air Temperature Generation by Fusing Multi-Source Data

Abstract

The all-weather high-resolution air temperature data is crucial for understanding the urban thermal conditions with their spatio-temporal characteristics, driving factors, socio-economic and environmental consequences. In this study, we developed a novel 5-layer Deep Belief Network (DBN) deep learning model to fuse multi-source data and then generated air temperature data with 3H characteristics: High resolution, High spatio-temporal continuity (spatially seamless and temporally continuous), and High accuracy simultaneously. The DBN model was developed and applied for two different urban regions: Wuhan Metropolitan Area (WMA) in China, and Austin, Texas, USA. The model has a excellent ability to fit the complex nonlinear relationship between temperature and different predictive variables. After various adjustments to the model structure and different combinations of input variables, the daily 500-m air temperature in Wuhan Metropolitan Area (WMA) was initially generated by fusing remote sensing, reanalysis and in situ measurement products. The ten-fold cross-validation results indicated that the DBN model achieved promising results with the RMSE of 1.086 °C, MAE of 0.839 °C, and R2 of 0.986. Compared with conventional data fusion algorithms, the DBN model also exhibited better performance. In addition, the detailed evaluation of the model on spatial and temporal scales proved the advantages of using DBN model to generate 3H temperature data. The spatial transferability of the model was tested by conducting a validation experiment for Austin, USA. In general, the results and fine-scale analyses show that the employed framework is effective to generate 3H temperature, which is valuable for urban climate and urban heat island research.