Dataset Information

Remotely Assessing Fraction of Photosynthetically Active Radiation (FPAR) for Wheat Canopies Based on Hyperspectral Vegetation Indexes.

ABSTRACT: Fraction of photosynthetically active radiation (FPAR), as an important index for evaluating yields and biomass production, is key to providing the guidance for crop management. However, the shortage of good hyperspectral data can frequently result in the hindrance of accurate and reliable FPAR assessment, especially for wheat. In the present research, aiming at developing a strategy for accurate FPAR assessment, the relationships between wheat canopy FPAR and vegetation indexes derived from concurrent ground-measured hyperspectral data were explored. FPAR revealed the most strongly correlation with normalized difference index (NDI), and scaled difference index (N*). Both NDI and N* revealed the increase as the increase of FPAR; however, NDI value presented the stagnation as FPAR value beyond 0.70. On the other hand, N* showed a decreasing tendency when FPAR value was higher than 0.70. This special relationship between FPAR and vegetation index could be employed to establish a piecewise FPAR assessment model with NDI as a regression variable during FPAR value lower than 0.70, or N* as the regression variable during FPAR value higher than 0.70. The model revealed higher assessment accuracy up to 16% when compared with FPAR assessment models based on a single vegetation index. In summary, it is feasible to apply NDI and N* for accomplishing wheat canopy FPAR assessment, and establish an FPAR assessment model to overcome the limitations from vegetation index saturation under the condition with high FPAR value.

SUBMITTER: Tan C

PROVIDER: S-EPMC5999760 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Remotely Assessing Fraction of Photosynthetically Active Radiation (<i>FPAR</i>) for Wheat Canopies Based on Hyperspectral Vegetation Indexes.

Tan Changwei C Wang Dunliang D Zhou Jian J Du Ying Y Luo Ming M Zhang Yongjian Y Guo Wenshan W

Frontiers in plant science 20180607

Fraction of photosynthetically active radiation (FPAR), as an important index for evaluating yields and biomass production, is key to providing the guidance for crop management. However, the shortage of good hyperspectral data can frequently result in the hindrance of accurate and reliable FPAR assessment, especially for wheat. In the present research, aiming at developing a strategy for accurate FPAR assessment, the relationships between wheat canopy FPAR and vegetation indexes derived from con ...[more]

PMID: 29930568

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Project description:Background: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. Results:The Modified Datt (MDATT) index, which is the ratio of reflectance difference defined as (R?3 - R?1)/(R?3 - R?2), exhibited the strongest correlation (R2 = 0.856, RMSE = 6.872 ?g/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 R2 between 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 ?1, while the red-edge region (697-771 nm) was optimal for ?2 and ?3. In these optimal wavelength regions, when the MDATT index was used to estimate LCC, an R2 higher than 0.8 could be obtained. The correlation of the MDATT index with LCC was the same when the positions of ?2 and ?3 were exchanged in the index. Conclusions: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 ?1, as well as the red-edge region (697-771 nm) for ?2 or ?3, are considered to be the most robust for constructing the MDATT index for estimating LCC, regardless of the leaf surface or plant species.

Dataset Information

Remotely Assessing Fraction of Photosynthetically Active Radiation (FPAR) for Wheat Canopies Based on Hyperspectral Vegetation Indexes.

Publications

Remotely Assessing Fraction of Photosynthetically Active Radiation (<i>FPAR</i>) for Wheat Canopies Based on Hyperspectral Vegetation Indexes.

Similar Datasets

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