File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

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

TitleAn Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors
Authors
KeywordsA 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)
Issue Date2016
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2016, v. 54, n. 11, p. 6481-6496 How to Cite?
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.
Persistent Identifierhttp://hdl.handle.net/10722/327110
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403

 

DC FieldValueLanguage
dc.contributor.authorZeng, Yelu-
dc.contributor.authorLi, Jing-
dc.contributor.authorLiu, Qinhuo-
dc.contributor.authorHuete, Alfredo R.-
dc.contributor.authorXu, Baodong-
dc.contributor.authorYin, Gaofei-
dc.contributor.authorZhao, Jing-
dc.contributor.authorYang, Le-
dc.contributor.authorFan, Weiliang-
dc.contributor.authorWu, Shengbiao-
dc.contributor.authorYan, Kai-
dc.date.accessioned2023-03-31T05:28:52Z-
dc.date.available2023-03-31T05:28:52Z-
dc.date.issued2016-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2016, v. 54, n. 11, p. 6481-6496-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/327110-
dc.description.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.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectA posteriori variance estimation-
dc.subjectbidirectional reflectance distribution function (BRDF)-
dc.subjectcost function-
dc.subjectEOS Land Validation Core Sites-
dc.subjectmodel inversion-
dc.subjectnormalized difference vegetation index (NDVI)-
dc.subjectOrdinary least squares (OLS)-
dc.titleAn Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2016.2585301-
dc.identifier.scopuseid_2-s2.0-84978811415-
dc.identifier.volume54-
dc.identifier.issue11-
dc.identifier.spage6481-
dc.identifier.epage6496-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats