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Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China
Liu, Yangxiaoyue1,2; Yang, Yaping1,3; Jing, Wenlong4,5,6; Yue, Xiafang1,3
2018
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:1Pages:23
Corresponding AuthorYang, Yaping(yangyp@igsnrr.ac.cn)
AbstractAlthough 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.
Keywordsoil moisture ESA CCI downscaling machine learning monthly
DOI10.3390/rs10010031
WOS KeywordLOESS PLATEAU ; RANDOM FOREST ; SCIKIT-LEARN ; MODIS ; WATER ; VEGETATION ; MISSION ; SCALE ; CLASSIFICATION ; CLASSIFIERS
Indexed BySCI
Language英语
Funding ProjectGeographic 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]
Funding OrganizationGeographic 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
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000424092300030
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/56963
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYang, Yaping
Affiliation1.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
Recommended Citation
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.
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