Research on downscaling of TRMM precipitation products based on deep learning: Exemplified by northeast China

doi:10.6046/gtzyyg.2020.04.19

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Abstract

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: dufangzhou0101@163.com;ylshi@nuist.edu.cn

Issue Date: 23 December 2020

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	Fangzhou DU
	Yuli SHI
	Xia SHENG

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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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.04.19 OR https://www.gtzyyg.com/EN/Y2020/V32/I4/145

Fig.1 Topographic and meteorological station distribution in the study area

Fig.2 DFNN structure diagram

$L$	$N$	$epoc h s$	训练集		测试集
$L$	$N$	$epoc h s$	$R 2$	$RMSE$ /mm	$R 2$	$RMSE$ /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	$R 2$		$RMSE$ /mm		$MRE$ /%
月份	降尺度前	降尺度后	降尺度前	降尺度后	降尺度前	降尺度后
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	$R 2$	$RMSE$ /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	$R 2$		$RMSE$ /mm		$MRE$ /%
月份	RF	DFNN	RF	DFNN	RF	DFNN
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	$R 2$		$RMSE$ /mm		$MRE$ /%
月份	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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