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Article: Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance

TitleUse of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance
Authors
KeywordsGeneral regression neural networks (GRNNs)
Leaf area index (LAI)
Moderate-Resolution Imaging Spectroradiometer (MODIS)
Retrieval
Time series
Issue Date2014
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2014, v. 52, n. 1, p. 209-223 How to Cite?
AbstractLeaf area index (LAI) products at regional and global scales are being routinely generated from individual instrument data acquired at a specific time. As a result of cloud contamination and other factors, these LAI products are spatially and temporally discontinuous and are also inaccurate for some vegetation types in many areas. A better strategy is to use multi-temporal data. In this paper, a method was developed to estimate LAI from time-series remote sensing data using general regression neural networks (GRNNs). A database was generated from Moderate-Resolution Imaging Spectroradiometer (MODIS) and CYCLOPES LAI products as well as MODIS reflectance products of the BELMANIP sites during the period from 2001-2003. The effective CYCLOPES LAI was first converted to true LAI, which was then combined with the MODIS LAI according to their uncertainties determined from the ground-measured true LAI. The MODIS reflectance was reprocessed to remove remaining effects. GRNNs were then trained over the fused LAI and reprocessed MODIS reflectance for each biome type to retrieve LAI from time-series remote sensing data. The reprocessed MODIS reflectance data from an entire year were inputted into the GRNNs to estimate the 1-year LAI profiles. Extensive validations for all biome types were carried out, and it was demonstrated that the method is able to estimate temporally continuous LAI profiles with much improved accuracy compared with that of the current MODIS and CYCLOPES LAI products. This new method is being used to produce the Global Land Surface Satellite LAI products in China. © 1980-2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/321544
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXiao, Zhiqiang-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorWang, Jindi-
dc.contributor.authorChen, Ping-
dc.contributor.authorYin, Xuejun-
dc.contributor.authorZhang, Liqiang-
dc.contributor.authorSong, Jinling-
dc.date.accessioned2022-11-03T02:19:39Z-
dc.date.available2022-11-03T02:19:39Z-
dc.date.issued2014-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2014, v. 52, n. 1, p. 209-223-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/321544-
dc.description.abstractLeaf area index (LAI) products at regional and global scales are being routinely generated from individual instrument data acquired at a specific time. As a result of cloud contamination and other factors, these LAI products are spatially and temporally discontinuous and are also inaccurate for some vegetation types in many areas. A better strategy is to use multi-temporal data. In this paper, a method was developed to estimate LAI from time-series remote sensing data using general regression neural networks (GRNNs). A database was generated from Moderate-Resolution Imaging Spectroradiometer (MODIS) and CYCLOPES LAI products as well as MODIS reflectance products of the BELMANIP sites during the period from 2001-2003. The effective CYCLOPES LAI was first converted to true LAI, which was then combined with the MODIS LAI according to their uncertainties determined from the ground-measured true LAI. The MODIS reflectance was reprocessed to remove remaining effects. GRNNs were then trained over the fused LAI and reprocessed MODIS reflectance for each biome type to retrieve LAI from time-series remote sensing data. The reprocessed MODIS reflectance data from an entire year were inputted into the GRNNs to estimate the 1-year LAI profiles. Extensive validations for all biome types were carried out, and it was demonstrated that the method is able to estimate temporally continuous LAI profiles with much improved accuracy compared with that of the current MODIS and CYCLOPES LAI products. This new method is being used to produce the Global Land Surface Satellite LAI products in China. © 1980-2012 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectGeneral regression neural networks (GRNNs)-
dc.subjectLeaf area index (LAI)-
dc.subjectModerate-Resolution Imaging Spectroradiometer (MODIS)-
dc.subjectRetrieval-
dc.subjectTime series-
dc.titleUse of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2013.2237780-
dc.identifier.scopuseid_2-s2.0-84890435028-
dc.identifier.volume52-
dc.identifier.issue1-
dc.identifier.spage209-
dc.identifier.epage223-
dc.identifier.isiWOS:000328938400017-

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