Generating 1 km Spatially Seamless and Temporally Continuous Air Temperature Based on Deep Learning over Yangtze River Basin, China

Rui Si Li, Tailai Huang, Yu Song, Shuzhe Huang, Xiang Zhang

Remote Sensing · 29 September 2021

In one sentence. Deep belief network fuses satellite, model and station data to produce 1 km daily air temperature over the Yangtze basin, with RMSE 1.96 °C.

Abstract

Air temperature is one of the most essential variables in understanding global warming as well as variations of climate, hydrology, and eco-systems. However, current products and assimilation approaches alone can provide temperature data with high resolution, high spatio-temporal continuity, and high accuracy simultaneously (refer to 3H data). To explore this kind of potential, we proposed an integrated temperature downscaling framework by fusing multiple remotely sent, model-based, and in-situ datasets, which was inspired by point-surface data fusion and deep learning. First, all of the predictor variables were processed to maintain spatial seamlessness and temporal continuity. Then, a deep belief neural network was applied to downscale temperature with a spatial resolution of 1 km. To further enhance the model performance, calibration techniques were adopted by integrating station-based data. The results of the validation over the Yangtze River Basin indicated that the average Pearson correlation coefficient, RMSE, and MAE of downscaled temperature achieved 0.983, 1.96 °C, and 1.57 °C, respectively. After calibration, the RMSE and MAE were further decreased by ~20%. In general, the results and comparative analysis confirmed the effectiveness of the framework for generating 3H temperature datasets, which would be valuable for earth science studies.

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