Literature DB >> 19067463

Optimal waveband identification for estimation of leaf area index of paddy rice.

Fu-min Wang1, Jing-feng Huang, Qi-fa Zhou, Xiu-zhen Wang.   

Abstract

The objectives of the study were to select suitable wavebands for rice leaf area index (LAI) estimation using the data acquired over a whole growing season, and to test the efficiency of the selected wavebands by comparing them with feature positions of rice canopy spectra. In this study, the field experiment in 2002 growing season was conducted at the experimental farm of Zhejiang University, Hangzhou, China. Measurements of hyperspectral reflectance (350 approximately 2500 nm) and corresponding LAI were made for a paddy rice canopy throughout the growing season. And three methods were employed to identify the optimal wavebands for paddy rice LAI estimation: correlation coefficient-based method, vegetation index-based method, and stepwise regression method. This research selected 15 wavebands in the region of 350~2 500 nm, which appeared to be the optimal wavebands for the paddy rice LAI estimation. Of the selected wavebands, the most frequently occurring wavebands were centered around 554, 675, 723, and 1 633 nm. They were followed by 444, 524, 576, 594, 804, 849, 974, 1 074, 1 219, 1 510, and 2 194 nm. Most of them made physical sense and had their counterparts in spectral known feature positions, which indicates the promising potential of the 15 selected wavebands for the retrieval of paddy rice LAI.

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Year:  2008        PMID: 19067463      PMCID: PMC2596287          DOI: 10.1631/jzus.B0820211

Source DB:  PubMed          Journal:  J Zhejiang Univ Sci B        ISSN: 1673-1581            Impact factor:   3.066


  2 in total

1.  Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation.

Authors:  Anatoly A Gitelson
Journal:  J Plant Physiol       Date:  2004-02       Impact factor: 3.549

2.  Identification of optimal hyperspectral bands for estimation of rice biophysical parameters.

Authors:  Fu-Min Wang; Jing-Feng Huang; Xiu-Zhen Wang
Journal:  J Integr Plant Biol       Date:  2008-03       Impact factor: 7.061

  2 in total
  4 in total

1.  Spatial and seasonal characterization of net primary productivity and climate variables in southeastern China using MODIS data.

Authors:  Dai-liang Peng; Jing-feng Huang; Alfredo R Huete; Tai-ming Yang; Ping Gao; Yan-chun Chen; Hui Chen; Jun Li; Zhan-yu Liu
Journal:  J Zhejiang Univ Sci B       Date:  2010-04       Impact factor: 3.066

2.  Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches.

Authors:  A M Stellacci; A Castrignanò; A Troccoli; B Basso; G Buttafuoco
Journal:  Environ Monit Assess       Date:  2016-02-27       Impact factor: 2.513

3.  Retrieval of aboveground crop nitrogen content with a hybrid machine learning method.

Authors:  Katja Berger; Jochem Verrelst; Jean-Baptiste Féret; Tobias Hank; Matthias Wocher; Wolfram Mauser; Gustau Camps-Valls
Journal:  Int J Appl Earth Obs Geoinf       Date:  2020-10-01

4.  Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance.

Authors:  Armando Sterling; Julio A Di Rienzo
Journal:  Plants (Basel)       Date:  2022-01-26
  4 in total

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