Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China

doi:10.3390/rs10010031

	Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China
	Liu, Yangxiaoyue 1,2; Yang, Yaping 1,3; Jing, Wenlong 4,5,6; Yue, Xiafang 1,3
刊名	REMOTE SENSING
	2018
卷号	10 期号:1 页码:23
关键词	soil moisture ESA CCI downscaling machine learning monthly
ISSN号	2072-4292
DOI	10.3390/rs10010031
通讯作者	Yang, Yaping(yangyp@igsnrr.ac.cn)
英文摘要	Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART), K-nearest neighbors (KNN), Bayesian (BAYE), and random forests (RF), were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI) SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature), normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band), and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values.
资助项目	Geographic Resources and Ecology Knowledge Service System of China Knowledge Center for Engineering Sciences and Technology[CKCEST-2015-1-4] ; National Special Program on Basic Science and Technology Research of China[2013FY110900] ; National Data Sharing Infrastructure of Earth System Science ; National Natural Science Foundation of China[41401430]
WOS关键词	LOESS PLATEAU ; RANDOM FOREST ; SCIKIT-LEARN ; MODIS ; WATER ; VEGETATION ; MISSION ; SCALE ; CLASSIFICATION ; CLASSIFIERS
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000424092300030
资助机构	Geographic Resources and Ecology Knowledge Service System of China Knowledge Center for Engineering Sciences and Technology ; National Special Program on Basic Science and Technology Research of China ; National Data Sharing Infrastructure of Earth System Science ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/56962]
专题	中国科学院地理科学与资源研究所
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 4.Guangzhou Inst Geog, Guangzhou 510070, Guangdong, Peoples R China 5.Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangzhou 510070, Guangdong, Peoples R China 6.Guangdong Open Lab Geospatial Informat Technol &, Guangzhou 510070, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yangxiaoyue,Yang, Yaping,Jing, Wenlong,et al. Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China[J]. REMOTE SENSING,2018,10(1):23.
APA	Liu, Yangxiaoyue,Yang, Yaping,Jing, Wenlong,&Yue, Xiafang.(2018).Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China.REMOTE SENSING,10(1),23.
MLA	Liu, Yangxiaoyue,et al."Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China".REMOTE SENSING 10.1(2018):23.