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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (4) : 145-153     DOI: 10.6046/gtzyyg.2020.04.19
Research on downscaling of TRMM precipitation products based on deep learning: Exemplified by northeast China
DU Fangzhou(), SHI Yuli(), SHENG Xia
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
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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.

Keywords TRMM      northeast China      NDVI      deep learning     
:  TP79  
Corresponding Authors: SHI Yuli     E-mail:;
Issue Date: 23 December 2020
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Fangzhou DU
Yuli SHI
Cite this article:   
Fangzhou DU,Yuli SHI,Xia SHENG. Research on downscaling of TRMM precipitation products based on deep learning: Exemplified by northeast China[J]. Remote Sensing for Land & Resources, 2020, 32(4): 145-153.
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Fig.1  Topographic and meteorological station distribution in the study area
Fig.2  DFNN structure diagram
L N epochs 训练集 测试集
R2 RMSE/mm R2 RMSE/mm
4 400 150 0.866 1.402 0.850 1.423
4 400 200 0.868 1.229 0.863 1.341
4 400 250 0.874 1.221 0.869 1.310
5 400 150 0.900 1.109 0.891 1.146
5 400 200 0.911 0.995 0.901 1.054
5 400 250 0.899 1.098 0.893 1.152
6 400 150 0.889 1.173 0.885 1.261
6 400 200 0.915 0.947 0.894 1.079
6 400 250 0.898 1.097 0.892 1.149
Tab.1  Model parameter adjustment results
Fig.3  Comparison of 0.25°×0.25° TRMM precipitation with 0.01°×0.01° downscale precipitation during 2009—2018
Fig.4  Scatter plots of the station’s measured precipitation with TRMM precipitation and downscale precipitation
月份 R2 RMSE/mm MRE/%
1月 0.798 0.826 2.819 2.503 79.76 71.31
4月 0.879 0.935 5.608 4.161 20.92 19.42
7月 0.784 0.817 20.960 16.870 12.37 11.08
10月 0.858 0.908 5.649 4.398 13.43 10.28
Tab.2  Comparison of accuracy before and after downscaling
月份 R2 RMSE/mm MRE/%
1月 0.324 2.813 54.130
4月 0.556 3.331 19.220
7月 0.653 10.580 6.315
10月 0.800 2.251 29.730
Tab.3  TRMM data uncovered area prediction accuracy verification
月份 R2 RMSE/mm MRE/%
1月 0.787 0.826 2.561 2.503 77.79 71.31
4月 0.893 0.935 5.438 4.161 20.26 19.42
7月 0.809 0.817 18.94 16.870 13.66 11.08
10月 0.892 0.908 4.935 4.398 11.99 10.28
Tab.4  Precision evaluation between RF and DFNN
月份 R2 RMSE/mm MRE/%
校正前 校正后 校正前 校正后 校正前 校正后
1月 0.833 0.881 2.413 1.222 70.86 28.410
4月 0.939 0.952 4.297 3.508 20.41 9.473
7月 0.825 0.891 16.460 13.110 11.66 7.555
10月 0.924 0.932 4.474 3.301 10.34 7.425
Tab.5  Accuracy index before and after monthly downscaling results correction
Fig.5  Spatial distribution of precipitation downscaled by 0.01°×0.01° in Jan., Apr., Jul., and Oct. after station correction
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