Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance

doi:10.3390/ijgi7070242

	Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance
	Yin, Gaofei 1; Li, Ainong 1; Wu, Chaoyang 2; Wang, Jiyan 3; Xie, Qiaoyun 4; Zhang, Zhengjian 1; Nan, Xi 1; Jin, Huaan 1; Bian, Jinhu 1; Lei, Guangbin 1
刊名	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2018-07-01
卷号	7 期号:7 页码:14
关键词	aboveground biomass (AGB) uncertainty consistent adjustment of the climatology to actual observations (CACAO) Gaussian process regression (GPR)
ISSN号	2220-9964
DOI	10.3390/ijgi7070242
通讯作者	Li, Ainong(ainongli@imde.ac.cn)
英文摘要	The 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.
资助项目	National 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]
WOS关键词	LEAF-AREA INDEX ; REMOTE-SENSING DATA ; TIME-SERIES ; SURFACE REFLECTANCE ; VEGETATION INDEX ; RETRIEVAL ; MODIS ; LAI ; CHINA ; MODEL
WOS研究方向	Physical Geography ; Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000445150900009
资助机构	National 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
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/52949]
专题	中国科学院地理科学与资源研究所
通讯作者	Li, Ainong
作者单位	1.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
推荐引用方式 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.