An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors

	An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors
	Zeng, Yelu 1; Li, Jing 1; Liu, Qinhuo 1; Huete, Alfredo R.1; Xu, Baodong 1; Yin, Gaofei 1; Zhao, Jing 1; Yang, Le 1; Fan, Weiliang 1; Wu, Shengbiao 1
刊名	IEEE Transactions on Geoscience and Remote Sensing
	2016
卷号	54 期号:11 页码:6481-6496
关键词	LEAF-AREA INDEX LAND-SURFACE TEMPERATURE FOREST REFLECTANCE MODEL CANOPY REFLECTANCE DIRECTIONAL REFLECTANCE CORRECT ESTIMATION RETRIEVAL IMPACT INFORMATION SIMULATION
通讯作者	Li, Jing (lijing01@radi.ac.cn)
英文摘要	Current 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. © 2016 IEEE.
学科主题	Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology
类目[WOS]	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20163002643680
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39180]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 2.100101, China 3. Center for Global Change Studies, Beijing 4.100875, China 5. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 6.100049, China 7. Beijing Normal University, Beijing 8.100875, China 9. Plant Functional Biology and Climate Change Cluster, University of Technology, Sydney 10.NSW
推荐引用方式 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.