Limits...
Comparing vegetation indices for remote chlorophyll measurement of white poplar and Chinese elm leaves with different adaxial and abaxial surfaces.

Lu S, Lu X, Zhao W, Liu Y, Wang Z, Omasa K - J. Exp. Bot. (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China.

No MeSH data available.


Related in: MedlinePlus

The map for coefficient of determination (R2) between the MDATT index [MDATT=(Rλ3−Rλ1)/(Rλ3−Rλ2)] and leaf chlorophyll content for the adaxial and abaxial surfaces of white poplar. MDATT indices for adaxial surface with λ3 equal to (A) 719nm, (B) 750nm and (C) 850nm. MDATT indices for abaxial surface with λ3 equal to (D) 719nm, (E) 750nm and (F) 850nm.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4585420&req=5

Figure 8: The map for coefficient of determination (R2) between the MDATT index [MDATT=(Rλ3−Rλ1)/(Rλ3−Rλ2)] and leaf chlorophyll content for the adaxial and abaxial surfaces of white poplar. MDATT indices for adaxial surface with λ3 equal to (A) 719nm, (B) 750nm and (C) 850nm. MDATT indices for abaxial surface with λ3 equal to (D) 719nm, (E) 750nm and (F) 850nm.

Mentions: Analysis of the datasets for each leaf surface was also conducted separately for each species to verify the effect of leaf surface on Chl content estimation. The distributions of R2 for the MDATT indices when λ3 was set to 719nm, 750nm and 850nm for white poplar and Chinese elm leaves are shown in Figs 8 and 9. The LCC-sensitive region on adaxial or abaxial surfaces was occupied by the whole bottom right area when λ3 was 719nm. However, the sensitive range was narrower when λ3 was set to 750nm and 850nm. In addition, the LCC-sensitive region was broader on adaxial than on abaxial leaf surface.


Comparing vegetation indices for remote chlorophyll measurement of white poplar and Chinese elm leaves with different adaxial and abaxial surfaces.

Lu S, Lu X, Zhao W, Liu Y, Wang Z, Omasa K - J. Exp. Bot. (2015)

The map for coefficient of determination (R2) between the MDATT index [MDATT=(Rλ3−Rλ1)/(Rλ3−Rλ2)] and leaf chlorophyll content for the adaxial and abaxial surfaces of white poplar. MDATT indices for adaxial surface with λ3 equal to (A) 719nm, (B) 750nm and (C) 850nm. MDATT indices for abaxial surface with λ3 equal to (D) 719nm, (E) 750nm and (F) 850nm.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4585420&req=5

Figure 8: The map for coefficient of determination (R2) between the MDATT index [MDATT=(Rλ3−Rλ1)/(Rλ3−Rλ2)] and leaf chlorophyll content for the adaxial and abaxial surfaces of white poplar. MDATT indices for adaxial surface with λ3 equal to (A) 719nm, (B) 750nm and (C) 850nm. MDATT indices for abaxial surface with λ3 equal to (D) 719nm, (E) 750nm and (F) 850nm.
Mentions: Analysis of the datasets for each leaf surface was also conducted separately for each species to verify the effect of leaf surface on Chl content estimation. The distributions of R2 for the MDATT indices when λ3 was set to 719nm, 750nm and 850nm for white poplar and Chinese elm leaves are shown in Figs 8 and 9. The LCC-sensitive region on adaxial or abaxial surfaces was occupied by the whole bottom right area when λ3 was 719nm. However, the sensitive range was narrower when λ3 was set to 750nm and 850nm. In addition, the LCC-sensitive region was broader on adaxial than on abaxial leaf surface.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China.

No MeSH data available.


Related in: MedlinePlus