IGSNRR OpenIR
Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance
Yin, Gaofei1; Li, Ainong1; Wu, Chaoyang2; Wang, Jiyan3; Xie, Qiaoyun4; Zhang, Zhengjian1; Nan, Xi1; Jin, Huaan1; Bian, Jinhu1; Lei, Guangbin1
2018-07-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume7Issue:7Pages:14
Corresponding AuthorLi, Ainong(ainongli@imde.ac.cn)
AbstractThe spatially explicit aboveground biomass (AGB) generated through upscaling field measurements is critical for carbon cycle simulation and optimized management of grasslands. However, the spatial gaps that exist in the optical remote sensing data, underutilization of the multispectral data cube and unavailability of uncertainty information hinder the generation of seamless and accurate AGB maps. This study proposes a novel framework to address the above challenges. The proposed framework filled the spatial gaps in the remote sensing data via the consistent adjustment of the climatology to actual observations (CACAO) method. Gaussian process regression (GPR) was used to fully exploit the multispectral data cube and generated the pixelwise uncertainty concurrent with the AGB estimation. A case study in a 100 km x 100 km area located in the Zoige Plateau, China was used to evaluate this framework. The results show that the CACAO method can fill almost all of the gaps, accounting for 93.1% of the study area, with satisfactory accuracy. The generated AGB map from the GPR was characterized by a relatively high accuracy (R-2 = 0.64, RMSE = 48.13 g/m(2)) compared to vegetation index-derived ones, and was accompanied by a corresponding uncertainty map that provides a new source of information on the credibility of each pixel. This study demonstrates the potential of the joint use of gap-filling and machine-learning methods to generate spatially explicit AGB.
Keywordaboveground biomass (AGB) uncertainty consistent adjustment of the climatology to actual observations (CACAO) Gaussian process regression (GPR)
DOI10.3390/ijgi7070242
WOS KeywordLEAF-AREA INDEX ; REMOTE-SENSING DATA ; TIME-SERIES ; SURFACE REFLECTANCE ; VEGETATION INDEX ; RETRIEVAL ; MODIS ; LAI ; CHINA ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41571373] ; National Natural Science Foundation of China[41601403] ; National Natural Science Foundation of China[41631180] ; National Natural Science Foundation of China[41531174] ; GF6 Project[30-Y20A03-90030-17/18] ; National Key Research and Development Program of China[2016YFA0600103] ; Youth Talent Team Program of the Institute of Mountain Hazards and Environment, CAS[SDSQB-2015-02]
Funding OrganizationNational Natural Science Foundation of China ; GF6 Project ; National Key Research and Development Program of China ; Youth Talent Team Program of the Institute of Mountain Hazards and Environment, CAS
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000445150900009
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52950
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLi, Ainong
Affiliation1.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
3.Southwest Petr Univ, Sch Civil Engn & Architecture, Chengdu 610500, Sichuan, Peoples R China
4.Univ Technol Sydney, C3, Sydney, NSW 2007, Australia
Recommended Citation
GB/T 7714
Yin, Gaofei,Li, Ainong,Wu, Chaoyang,et al. Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2018,7(7):14.
APA Yin, Gaofei.,Li, Ainong.,Wu, Chaoyang.,Wang, Jiyan.,Xie, Qiaoyun.,...&Lei, Guangbin.(2018).Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,7(7),14.
MLA Yin, Gaofei,et al."Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 7.7(2018):14.
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