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A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons

Overview of attention for article published in Plant Methods, February 2018
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Title
A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons
Published in
Plant Methods, February 2018
DOI 10.1186/s13007-018-0281-z
Pubmed ID
Authors

Shan Lu, Fan Lu, Wenqiang You, Zheyi Wang, Yu Liu, Kenji Omasa

Abstract

Leaf chlorophyll content (LCC) provides valuable information about plant physiology. Most of the published chlorophyll vegetation indices at the leaf level have been based on the spectral characteristics of the adaxial leaf surface, thus, they are not appropriate for estimating LCC when both the adaxial and abaxial leaf surfaces influence the spectral reflectance. We attempted to address this challenge by measuring the spectral reflectance of the adaxial and abaxial leaf surfaces of several plant species at different growth stages using a portable field spectroradiometer. The relationships between more than 30 published reflectance indices with LCC were analyzed to determine which index estimated LCC most effectively. Additionally, since the relationships determined on one set of samples might have poor predictive performances when applied to other samples, a robust wavelength region is required to render the spectral index generally applicable, regardless of the leaf surface or plant species. The Modified Datt (MDATT) index, which is the ratio of reflectance difference defined as (Rλ3 - Rλ1)/(Rλ3 - Rλ2), exhibited the strongest correlation (R2 = 0.856, RMSE = 6.872 μg/cm2), with LCC of all the indices tested when all the leaf samples from the adaxial and abaxial surfaces were combined. The optimal wavelength regions, which were derived from the contour maps of R2between the MDATT index and LCC for the datasets of one side or both leaf surfaces of each plant species and their intersection, indicated that the red-edge to near-infrared wavelength (723-885 nm) was optimal for λ1, while the red-edge region (697-771 nm) was optimal for λ2and λ3. In these optimal wavelength regions, when the MDATT index was used to estimate LCC, an R2higher than 0.8 could be obtained. The correlation of the MDATT index with LCC was the same when the positions of λ2and λ3were exchanged in the index. MDATT is proposed as an optimal index for the remote estimation of vegetation chlorophyll content across several plant species in different growth stages when reflectance from both leaf surfaces is considered. The red-edge to near-infrared wavelength (723-885 nm) for λ1, as well as the red-edge region (697-771 nm) for λ2or λ3, are considered to be the most robust for constructing the MDATT index for estimating LCC, regardless of the leaf surface or plant species.

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The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 19%
Unspecified 5 8%
Student > Master 5 8%
Researcher 5 8%
Student > Bachelor 4 6%
Other 9 15%
Unknown 22 35%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 19%
Environmental Science 6 10%
Unspecified 5 8%
Earth and Planetary Sciences 4 6%
Engineering 3 5%
Other 4 6%
Unknown 28 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 February 2018.
All research outputs
#13,228,623
of 23,023,224 outputs
Outputs from Plant Methods
#597
of 1,088 outputs
Outputs of similar age
#214,698
of 446,257 outputs
Outputs of similar age from Plant Methods
#13
of 29 outputs
Altmetric has tracked 23,023,224 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,088 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 446,257 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.