Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion

doi:10.3390/rs11030244

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 数字山地与遥感应用中心

	Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion
	Yin, Gaofei1 ; Verger, Aleixandre 2; Qu, Yonghua 3; Zhao, Wei4 ; Xu, Baodong 5; Zeng, Yelu 6; Liu, Ke 7; Li, Jing 8; Liu, Qinhuo 8
刊名	REMOTE SENSING
	2019-02-01
卷号	11 期号:3 页码:18
关键词	leaf area index uncertainty Gaussian processes wireless sensor network data fusion Landsat MODIS validation
ISSN号	2072-4292
DOI	10.3390/rs11030244
通讯作者	Yin, Gaofei(yingf@swjtu.edu.cn) ; Li, Jing(lijing01@radi.ac.cn)
英文摘要	Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R-2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.
资助项目	National Natural Science Foundation of China[41601403] ; National Natural Science Foundation of China[41631180] ; National Natural Science Foundation of China[41531174] ; GF6 Project[30-Y20A03-9003-17/18] ; Youth Innovation Promotion Association CAS[2016333] ; China Postdoctoral Science Foundation[2018T110996] ; Innovation Ability Promotion Program of the Sichuan Provincial Department of Finance[2016QNJJ-023] ; EC Copernicus Global Land Service[CGLOPS-1] ; EC Copernicus Global Land Service[199494-JRC]
WOS关键词	VEGETATION BIOPHYSICAL PARAMETERS ; CHLOROPHYLL CONTENT ; PRODUCT VALIDATION ; LAI PRODUCTS ; MODIS ; SENTINEL-2 ; SURFACE ; PERFORMANCE ; DERIVATION ; ALGORITHM
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000459944400032
资助机构	National Natural Science Foundation of China ; GF6 Project ; Youth Innovation Promotion Association CAS ; China Postdoctoral Science Foundation ; Innovation Ability Promotion Program of the Sichuan Provincial Department of Finance ; EC Copernicus Global Land Service
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/25321]
专题	成都山地灾害与环境研究所_数字山地与遥感应用中心
通讯作者	Yin, Gaofei; Li, Jing
作者单位	1.Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Sichuan, Peoples R China 2.CREAF, Cerdanyola Del Valles 08193, Catalonia, Spain 3.Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing Key Lab Remote Sensing Environm & Digital, Inst Remote Sensing Sci & Engn,Fac Geog Sci, Beijing 100875, Peoples R China 4.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610010, Sichuan, Peoples R China 5.Huazhong Agr Univ, Coll Resource & Environm, Macro Agr Res Inst, Wuhan 430070, Hubei, Peoples R China 6.Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA 7.Sichuan Acad Agr Sci, Inst Remote Sensing Applicat, Chengdu 610066, Sichuan, Peoples R China 8.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Yin, Gaofei,Verger, Aleixandre,Qu, Yonghua,et al. Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion[J]. REMOTE SENSING,2019,11(3):18.
APA	Yin, Gaofei.,Verger, Aleixandre.,Qu, Yonghua.,Zhao, Wei.,Xu, Baodong.,...&Liu, Qinhuo.(2019).Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion.REMOTE SENSING,11(3),18.
MLA	Yin, Gaofei,et al."Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion".REMOTE SENSING 11.3(2019):18.