A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates

doi:10.3390/rs13142693

	A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates
	Zhao, Na 1,2,3; Jiao, Yimeng 1,2
刊名	REMOTE SENSING
	2021-07-01
卷号	13 期号:14 页码:20
关键词	satellite precipitation estimates downscaling IMERG
DOI	10.3390/rs13142693
通讯作者	Zhao, Na(zhaon@lreis.ac.cn)
英文摘要	Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed a new downscaling method, the high accuracy surface modeling-based downscaling method (HASMD), to derive high-quality monthly precipitation estimates at a spatial resolution of 0.01 degrees by downscaling the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation estimates in China. A scale transformation equation was introduced in HASMD, and the initial value was set by including the explanatory variables related to precipitation. The performance of HASMD was evaluated by comparing the results yielded by HASM and the combined method of HASM, Kriging, IDW and the geographical weighted regression (GWR) method (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis results indicated that HASMD performed better than the other four methods. High agreement was achieved for HASMD, with bias values ranging from 0.07 to 0.29, root mean square error (RMSE) values ranging from 9.53 mm to 47.03 mm, and R-2 values ranging from 0.75 to 0.96. Compared with the original IMERG precipitation products, the downscaling accuracy with HASMD improved up to 47%, 47%, and 14% according to bias, RMSE and R-2, respectively. HASMD was able to capture the spatial variation in monthly precipitation in a vast region, and it might be potentially applicable for enhancing the spatial resolution and accuracy of remotely sensed precipitation data and facilitating their application at large scales.
资助项目	National Natural Science Foundation of China[42071374] ; National Natural Science Foundation of China[41930647] ; Program of Frontier Sciences of Chinese Academy of Sciences[ZDBS-LY-DQC005]
WOS关键词	REGRESSION
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000677130500001
资助机构	National Natural Science Foundation of China ; Program of Frontier Sciences of Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/164787]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhao, Na
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100101, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Na,Jiao, Yimeng. A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates[J]. REMOTE SENSING,2021,13(14):20.
APA	Zhao, Na,&Jiao, Yimeng.(2021).A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates.REMOTE SENSING,13(14),20.
MLA	Zhao, Na,et al."A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates".REMOTE SENSING 13.14(2021):20.