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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 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 indices and the leaf chlorophyll content for both surfaces of both species. (A) λ3=719nm, (B) λ3=750nm, (C) λ3=850nm.
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Figure 6: The map for coefficient of determination (R2) between the MDATT indices and the leaf chlorophyll content for both surfaces of both species. (A) λ3=719nm, (B) λ3=750nm, (C) λ3=850nm.

Mentions: To identify optimal parameters for estimating LCC, further analysis was conducted on the MDATT indices, which were based on the same dataset analysed for the two-band indices. Each combination of three bands was used to compose an MDATT index that was then correlated with the LCC. The best R2 values between the LCC and the MDATT indices generated from combinations of wavelengths λ1, λ2 and λ3 are shown in Fig. 5, and the performance of the MDATT indices in estimating LCC is also shown in Table 2. The results indicated that the MDATT indices that had good correlations with LCC were derived primarily from the red edge wavelength regions. For example, the MDATT indices with R2 greater than 0.90 were derived from the wavelengths of λ1 (721~746nm), λ2 (705~758nm) and λ3 (699~798nm); the indices with R2 greater than 0.92 were generated from the wavelengths of λ1 (726~728nm), λ2 (743nm) and λ3 (717~720nm); and the best-performing index overall was (R719−R726)/(R719−R743), which generated the most significant linear relationships with LCC (R2=0.92, RMSE=5.23 μg/cm2) (Table 2). The maps for R2 between the MDATT indices (λ3 fixed at 719nm, 750nm and 850nm) and the LCC for both surfaces of both species combined are shown in Fig. 6. The (R719−R732)/(R719−R726) and (R719−R747)/(R719−R721) indices were strongly related to LCC for the two-surface datasets of white poplar (R2=0.94, RMSE=4.67 μg/cm2) and Chinese elm (R2=0.91, RMSE=4.53 μg/cm2).


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 indices and the leaf chlorophyll content for both surfaces of both species. (A) λ3=719nm, (B) λ3=750nm, (C) λ3=850nm.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 6: The map for coefficient of determination (R2) between the MDATT indices and the leaf chlorophyll content for both surfaces of both species. (A) λ3=719nm, (B) λ3=750nm, (C) λ3=850nm.
Mentions: To identify optimal parameters for estimating LCC, further analysis was conducted on the MDATT indices, which were based on the same dataset analysed for the two-band indices. Each combination of three bands was used to compose an MDATT index that was then correlated with the LCC. The best R2 values between the LCC and the MDATT indices generated from combinations of wavelengths λ1, λ2 and λ3 are shown in Fig. 5, and the performance of the MDATT indices in estimating LCC is also shown in Table 2. The results indicated that the MDATT indices that had good correlations with LCC were derived primarily from the red edge wavelength regions. For example, the MDATT indices with R2 greater than 0.90 were derived from the wavelengths of λ1 (721~746nm), λ2 (705~758nm) and λ3 (699~798nm); the indices with R2 greater than 0.92 were generated from the wavelengths of λ1 (726~728nm), λ2 (743nm) and λ3 (717~720nm); and the best-performing index overall was (R719−R726)/(R719−R743), which generated the most significant linear relationships with LCC (R2=0.92, RMSE=5.23 μg/cm2) (Table 2). The maps for R2 between the MDATT indices (λ3 fixed at 719nm, 750nm and 850nm) and the LCC for both surfaces of both species combined are shown in Fig. 6. The (R719−R732)/(R719−R726) and (R719−R747)/(R719−R721) indices were strongly related to LCC for the two-surface datasets of white poplar (R2=0.94, RMSE=4.67 μg/cm2) and Chinese elm (R2=0.91, RMSE=4.53 μg/cm2).

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