IGSNRR OpenIR
Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging
Jin, Yan1,2; Ge, Yong1,2; Wang, Jianghao1,2; Chen, Yuehong3; Heuvelink, Gerard B. M.4; Atkinson, Peter M.5,6,7
2018-04-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume56Issue:4Pages:2362-2376
Corresponding AuthorWang, Jianghao(wangjh@lreis.ac.cn)
AbstractSoil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM. However, the application of such data is generally restricted because of their coarse spatial resolution. Downscaling methods have been applied to predict fine-resolution SM from original data with coarse spatial resolution. Commonly, SM is highly spatially variable and, consequently, such local spatial heterogeneity should be considered in a downscaling process. Here, a hybrid geostatistical approach, which integrates geographically weighted regression and area-to-area kriging, is proposed for downscaling microwave SM products. The proposed geographically weighted area-to-area regression kriging (GWATARK) method combines fine-spatial-resolution optical remote sensing data and coarse-spatial-resolution passive microwave remote sensing data, because the combination of both information sources has great potential for mapping fine-spatial-resolution near-surface SM. The GWATARK method was evaluated by producing down-scaled SM at 1-km resolution from the 25-km-resolution daily AMSR-2 SM product. Comparison of the downscaled predictions from the GWATARK method and two benchmark methods on three sets of covariates with in situ observations showed that the GWATARK method is more accurate than the two benchmarks. On average, the root-mean-square error value decreased by 20%. The use of additional covariates further increased the accuracy of the downscaled predictions, particularly when using topography-corrected land surface temperature and vegetation-temperature condition index covariates.
KeywordCovariance matrices geospatial analysis high-resolution imaging remote sensing spatial resolution
DOI10.1109/TGRS.2017.2778420
WOS KeywordLAND-SURFACE TEMPERATURE ; HIGH-RESOLUTION ; SCALE ; SMOS ; ASSIMILATION ; MODEL ; DISAGGREGATION ; PERFORMANCE ; AUSTRALIA ; PRODUCTS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41531174] ; National Natural Science Foundation of China[41531179]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000428673800039
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/57383
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Jianghao
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, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China
4.Wageningen Univ, Soil Geog & Landscape Grp, NL-6700 AA Wageningen, Netherlands
5.Univ Lancaster, Fac Sci & Technol, Lancaster LA1 4YR, England
6.Queens Univ Belfast, Sch Nat & Built Environm, Belfast BT7 1NN, Antrim, North Ireland
7.Univ Southampton, Geog & Environm, Southampton S017 1BJ, Hants, England
Recommended Citation
GB/T 7714
Jin, Yan,Ge, Yong,Wang, Jianghao,et al. Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(4):2362-2376.
APA Jin, Yan,Ge, Yong,Wang, Jianghao,Chen, Yuehong,Heuvelink, Gerard B. M.,&Atkinson, Peter M..(2018).Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(4),2362-2376.
MLA Jin, Yan,et al."Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.4(2018):2362-2376.
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