Reconstructing Satellite-Based Monthly Precipitation over Northeast China Using Machine Learning Algorithms

doi:10.3390/rs9080781

	Reconstructing Satellite-Based Monthly Precipitation over Northeast China Using Machine Learning Algorithms
	Jing, Wenlong 1,2,3; Zhang, Pengyan 4; Jiang, Hao 1,2,3; Zhao, Xiaodan 5
刊名	REMOTE SENSING
	2017-08-01
卷号	9 期号:8 页码:17
关键词	reconstruction satellite-based precipitation
ISSN号	2072-4292
DOI	10.3390/rs9080781
通讯作者	Zhang, Pengyan(pengyanzh@henu.edu.cn)
英文摘要	Attaining accurate precipitation data is critical to understanding land surface processes and global climate change. The development of satellite sensors and remote sensing technology has resulted in multi-source precipitation datasets that provide reliable estimates of precipitation over un-gauged areas. However, gaps exist over high latitude areas due to the limited spatial extent of several satellite-based precipitation products. In this study, we propose an approach for the reconstruction of the Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation data over Northeast China based on the interaction between precipitation and surface environment. Two machine learning algorithms, support vector machine (SVM) and random forests (RF), are implemented to detect possible relationships between precipitation and normalized difference vegetation index (NDVI), land surface temperature (LST), and digital elevation model (DEM). The relationships between precipitation and geographical location variations based on longitude and latitude are also considered in the reconstruction model. The reconstruction of monthly precipitation in the study area is conducted in two spatial resolutions (25 km and 1 km). The validation is performed using in-situ observations from eight meteorological stations within the study area. The results show that the RF algorithm is robust and not sensitive to the choice of parameters, while the training accuracy of the SVM algorithm has relatively large fluctuations depending on the parameter settings and month. The precipitation data reconstructed with RF show strong correlation with in situ observations at each station and are more accurate than that obtained using the SVM algorithm. In general, the accuracy of the estimated precipitation at 1 km resolution is slightly lower than that of data at 25 km resolution. The estimation errors are positively related to the average precipitation.
资助项目	National Natural Science Foundation of China[41601175] ; Foundation and Advanced Technology Research Plan of Henan Province[152300410067] ; University Science and Technology Innovation Team Support Plan of Henan Province[16IRTSTHN012] ; key scientific research project of Henan province[16A610001] ; Natural Science Foundation of Guangdong Academy of Sciences[QNJJ201601] ; GDAS' Special Project of Science and Technology Development[2017GDASCX-0801] ; GDAS' Special Project of Science and Technology Development[2017GDASCX-0804]
WOS关键词	TIBETAN PLATEAU ; SURFACE-TEMPERATURE ; RANDOM FORESTS ; TIME-SCALES ; RAIN ; VALIDATION ; VEGETATION ; DROUGHT ; RETRIEVAL ; CLASSIFICATION
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000408605600019
资助机构	National Natural Science Foundation of China ; Foundation and Advanced Technology Research Plan of Henan Province ; University Science and Technology Innovation Team Support Plan of Henan Province ; key scientific research project of Henan province ; Natural Science Foundation of Guangdong Academy of Sciences ; GDAS' Special Project of Science and Technology Development
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/61817]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhang, Pengyan
作者单位	1.Guangzhou Inst Geog, Guangzhou 510070, Guangdong, Peoples R China 2.Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangzhou 510070, Guangdong, Peoples R China 3.Guangdong Open Lab Geospatial Informat Technol &, Guangzhou 510070, Guangdong, Peoples R China 4.Henan Univ, Coll Environm & Planning, Kaifeng 475004, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Jing, Wenlong,Zhang, Pengyan,Jiang, Hao,et al. Reconstructing Satellite-Based Monthly Precipitation over Northeast China Using Machine Learning Algorithms[J]. REMOTE SENSING,2017,9(8):17.
APA	Jing, Wenlong,Zhang, Pengyan,Jiang, Hao,&Zhao, Xiaodan.(2017).Reconstructing Satellite-Based Monthly Precipitation over Northeast China Using Machine Learning Algorithms.REMOTE SENSING,9(8),17.
MLA	Jing, Wenlong,et al."Reconstructing Satellite-Based Monthly Precipitation over Northeast China Using Machine Learning Algorithms".REMOTE SENSING 9.8(2017):17.