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Comparing vegetation indices for remote chlorophyll measurement of white poplar and Chinese elm leaves with different adaxial and abaxial surfaces.
Lu, Shan; Lu, Xingtong; Zhao, Wenli; Liu, Yu; Wang, Zheyi; Omasa, Kenji.
  • Lu S; School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China.
  • Lu X; School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China.
  • Zhao W; School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China.
  • Liu Y; School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China.
  • Wang Z; School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China.
  • Omasa K; Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japan aomasa@mail.ecc.u-tokyo.ac.jp.
J Exp Bot ; 66(18): 5625-37, 2015 Sep.
Article en En | MEDLINE | ID: mdl-26034132
ABSTRACT
Quick non-destructive assessment of leaf chlorophyll content (LCC) is important for studying phenotypes related to plant growth and stress resistance. This study was undertaken to investigate the quantitative relationship between LCC and different vegetation indices (VIs) on both adaxial and abaxial surfaces of white poplar (Populus alba), which has dense tubular hairs on its abaxial surface, and Chinese elm (Ulmus pumila var. pendula), which does not show obvious superficial differences except for lighter colour on the abaxial surface. Some published and newly developed VIs were tested to relate them to LCC. The results showed that most of the published VIs had strong relationships with LCC on the one-surface dataset, but did not show a clear relationship with LCC when both adaxial and abaxial surface reflectance data were included. Among the reflectance indices tested, the modified Datt index, (R719-R726)/(R719-R743), performed best and is proposed as a new index for remote estimation of chlorophyll content in plants with varying leaf surface structures. It explained 92% of LCC variation in this research, and the root mean square error of the LCC prediction was 5.23 µg/cm(2). This new index is insensitive to the effects of adaxial and abaxial leaf surface structures and is strongly related to the variation in reflectance caused by chlorophyll content.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Espectrofotometría / Clorofila / Populus / Ulmus / Tecnología de Sensores Remotos Tipo de estudio: Prognostic_studies Idioma: En Año: 2015 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Espectrofotometría / Clorofila / Populus / Ulmus / Tecnología de Sensores Remotos Tipo de estudio: Prognostic_studies Idioma: En Año: 2015 Tipo del documento: Article