曾也鲁等：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

作者:Zeng, YL (Zeng, Yelu)[ 1,2,3 ] ; Li, J (Li, Jing)[ 1,2,4 ] ; Liu, QH (Liu, Qinhuo)[ 1,2,4 ] ; Huete, AR (Huete, Alfredo R.)[ 5 ] ; Xu, BD (Xu, Baodong)[ 1,2,3 ] ; Yin, GF (Yin, Gaofei)[ 6 ] ; Zhao, J (Zhao, Jing)[ 1,2,4 ] ; Yang, L (Yang, Le)[ 1,2,4 ] ; Fan, WL (Fan, Weiliang)[ 7 ] ; Wu, SB (Wu, Shengbiao)[ 1,2,3 ]  ; Yan, K (Yan, Kai)[ 1,2,4 ]

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

卷: 54  期: 11  页: 6481-6496

DOI: 10.1109/TGRS.2016.2585301

出版年: NOV 2016

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.

通讯作者地址: Li, J; Liu, QH (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

通讯作者地址: Li, J; Liu, QH (通讯作者)

  Joint Ctr Global Change Studies, Beijing 100875, Peoples R China.

地址:

[ 1 ] 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