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An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors
Zeng, Yelu1,2,3; Li, Jing1,2,4; Liu, Qinhuo1,2,4; Huete, Alfredo R.5; Xu, Baodong1,2,3; Yin, Gaofei6; Zhao, Jing1,2,4; Yang, Le1,2,4; Fan, Weiliang7; Wu, Shengbiao1,2,3; Yan, Kai1,2,4
Corresponding AuthorLi, J ; Liu, QH
2016
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
EISSN1558-0644
Volume54Issue:11Pages:6481-6496
SubtypeArticle
AbstractCurrent bidirectional reflectance distribution function (BRDF) inversions using ordinary least squares (OLS) criterion can be easily contaminated by observations with residual cloud and undetected high aerosols, which leads to abrupt fluctuations in the normalized difference vegetation index (NDVI) time series. The OLS criterion assumes the noise has Gaussian distribution, which is often violated due to positive noise biases caused by clouds and high aerosols. A changing-weight iterative BRDF/ NDVI inversion algorithm (CWI) based on a posteriori variance estimation of observation errors is presented to explicitly consider the asymmetrically distributed noise and observations with unequal accuracy in the BRDF retrieval. CWI employs a posteriori variance estimation and an NDVI-based indicator to iteratively adjust the weight of each observation according to its noise level. The validation results suggest CWI performs better than the Li-Gao and OLS approaches. The rmse was reduced from 0.074 to 0.028, and the relative error decreased from 13.4% to 3.8% at the U.S. Department of Agriculture Beltsville Agricultural Research Center site. Similarly, at the Harvard Forest site, the rmse was reduced from 0.086 to 0.031, and the relative error decreased from 9.5% to 2.7%. The average noise and relative noise of the CWI NDVI time series over ten EOS Land Validation Core Sites from 2003-2009 was smaller (0.028, 3.7%) than those of MOD13A2 (0.041, 5.2%), MYD13A2 (0.039, 4.9%) and MCD43B4 (0.030, 4.4%). The results demonstrate the robustness of the CWI approach in suppressing the influence of contaminated observations in BRDF retrievals by producing results that are less affected by undetected clouds and high aerosols.
Keyworda Posteriori Variance Estimation Bidirectional Reflectance Distribution Function (Brdf) Cost Function Eos Land Validation Core Sites Model Inversion Normalized Difference Vegetation Index (Ndvi) Ordinary Least Squares (Ols)
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2016.2585301
WOS Subject ExtendedGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
Indexed BySCI
WOS KeywordNDVI TIME-SERIES ; MONITORING VEGETATION ; SURFACE REFLECTANCE ; BIDIRECTIONAL NDVI ; BRDF INVERSION ; MODIS ; RETRIEVAL ; PRODUCTS ; ALBEDO ; INDEX
Language英语
Quartile2区
TOP
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000385188200019
Funding OrganizationNational Basic Research Program of China(2013CB733401) ; Action Plan of Western Development of the Chinese Academy of Sciences(KZCX2-XB3-15-2) ; National Natural Science Foundation of China(41271366) ; National High Technology Research and Development Program of China(2012AA12A304)
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/18129
Collection数字山地与遥感应用中心
Affiliation1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
2.Joint Ctr Global Change Studies, Beijing 100875, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
4.Beijing Normal Univ, Beijing 100875, Peoples R China
5.Univ Technol, Plant Funct Biol & Climate Change Cluster C3, Sydney, NSW 2007, Australia
6.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China
7.Zhejiang A&F Univ, Sch Environm & Resources Sci, Hangzhou 311300, Zhejiang, Peoples R China
Recommended Citation
GB/T 7714
Zeng, Yelu,Li, Jing,Liu, Qinhuo,et al. An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2016,54(11):6481-6496.
APA Zeng, Yelu.,Li, Jing.,Liu, Qinhuo.,Huete, Alfredo R..,Xu, Baodong.,...&Yan, Kai.(2016).An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,54(11),6481-6496.
MLA Zeng, Yelu,et al."An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 54.11(2016):6481-6496.
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