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	A spatial downscaling algorithm for satellite-based precipitation over the tibetan plateau based on ndvi, dem, and land surface temperature
	Jing, Wenlong1,2; Yang, Yaping1,3; Yue, Xiafang1,3; Zhao, Xiaodan1,3
刊名	Remote sensing
	2016-08-01
卷号	8期号:8页码:19
关键词	Precipitation Spatial downscaling Land surface temperature Random forests Svm
ISSN号	2072-4292
DOI	10.3390/rs8080655
通讯作者	Yang, yaping(yangyp@igsnrr.ac.cn)
英文摘要	Precipitation is an important controlling parameter for land surface processes, and is crucial to ecological, environmental, and hydrological modeling. in this study, we propose a spatial downscaling approach based on precipitation-land surface characteristics. land surface temperature features were introduced as new variables in addition to the normalized difference vegetation index (ndvi) and digital elevation model (dem) to improve the spatial downscaling algorithm. two machine learning algorithms, random forests (rf) and support vector machine (svm), were implemented to downscale the yearly tropical rainfall measuring mission 3b43 v7 (trmm 3b43 v7) precipitation data from 25 km to 1 km over the tibetan plateau area, and the downscaled results were validated on the basis of observations from meteorological stations and comparisons with previous downscaling algorithms. according to the validation results, the rf and svm-based models produced higher accuracy than the exponential regression (er) model and multiple linear regression (mlr) model. the downscaled results also had higher accuracy than the original trmm 3b43 v7 dataset. moreover, models including land surface temperature variables (lsts) performed better than those without lsts, indicating the significance of considering precipitation-land surface temperature when downscaling trmm 3b43 v7 precipitation data. the rf model with only ndvi and dem produced much worse accuracy than the svm model with the same variables. this indicates that the random forests algorithm is more sensitive to lsts than the svm when downscaling yearly trmm 3b43 v7 precipitation data over tibetan plateau. moreover, the precipitation-lsts relationship is more instantaneous, making it more likely to downscale precipitation at a monthly or weekly temporal scale.
WOS关键词	COVER CLASSIFICATION ; RANDOM FORESTS ; MACHINE ; CHINA ; RAIN ; VARIABILITY ; VEGETATION ; NETWORKS ; SCALES
WOS研究方向	Remote Sensing
WOS类目	Remote Sensing
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000382458700042
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2375693
专题	中国科学院大学
通讯作者	Yang, Yaping
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, Beijing 100049, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Jing, Wenlong,Yang, Yaping,Yue, Xiafang,et al. A spatial downscaling algorithm for satellite-based precipitation over the tibetan plateau based on ndvi, dem, and land surface temperature[J]. Remote sensing,2016,8(8):19.
APA	Jing, Wenlong,Yang, Yaping,Yue, Xiafang,&Zhao, Xiaodan.(2016).A spatial downscaling algorithm for satellite-based precipitation over the tibetan plateau based on ndvi, dem, and land surface temperature.Remote sensing,8(8),19.
MLA	Jing, Wenlong,et al."A spatial downscaling algorithm for satellite-based precipitation over the tibetan plateau based on ndvi, dem, and land surface temperature".Remote sensing 8.8(2016):19.

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