The research on the seasonal spatial and temporal distribution of precipitation is of great significance to the ecological protection and agricultural production in northeast China. Based on the correlation between vegetation index, topographical factors and precipitation, this paper utilizes deep learning models to downscale TRMM_3B43 products to 0.01° (about 1 km) in January, April, July, and October during 2009—2018, and uses site measured data to make accuracy correction and fill areas above 50 ° N which are not covered by TRMM. The results show that the model is better than random forest and can effectively obtain the precipitation distribution in the study area with higher spatial resolution and accuracy in each season. The corrected global determination coefficient R2 is between 0.881 and 0.952, the root mean square error (RMSE) is between 1.222 mm and 13.11 mm, and the mean relative error (MRE) is between 7.425% and 28.41%, among which the fitting degree is good in April and October, and relatively poor in January and July.
杜方洲, 石玉立, 盛夏. 基于深度学习的TRMM降水产品降尺度研究——以中国东北地区为例[J]. 国土资源遥感, 2020, 32(4): 145-153.
DU Fangzhou, SHI Yuli, SHENG Xia. Research on downscaling of TRMM precipitation products based on deep learning: Exemplified by northeast China. Remote Sensing for Land & Resources, 2020, 32(4): 145-153.
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