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. 2017 May 22:8:820.
doi: 10.3389/fpls.2017.00820. eCollection 2017.

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

Mairaj Din  1   2 , Wen Zheng  1   2 , Muhammad Rashid  3 , Shanqin Wang  1   2 , Zhihua Shi  1   2
Affiliations

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

Mairaj Din et al. Front Plant Sci. .

Abstract

Hyperspectral reflectance derived vegetation indices (VIs) are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent accounted for the sensitivity of the VIs and MSAVI, MTVI2, and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived VIs under different N management practices.

Keywords: LAI; N-nutrition; hyperspectral reflectance; phenology; rice.

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Figures

FIGURE 1
FIGURE 1
Photographs of mid season rice in 2015 and 2016 (A). Meichuan Experimental Station and field view (B). Before heading (C) at maturity.
FIGURE 2
FIGURE 2
Correlation among LAI and the canopy reflectance at wavelength from 440 to 900 nm using data collected in 2015 and 2016.
FIGURE 3
FIGURE 3
Change in canopy reflectance spectra under different N rates at critical growth stages of rice in 2015 (A) 2016 (B), where N0, N3, N5.5, N8.5, N11, N14, N16.5, and N19.5 represents 0, 45, 83, 128, 165, 210, 248, and 293 kg N ha-1, respectively.
FIGURE 4
FIGURE 4
Changes in canopy reflectance spectra from tillering to maturity of rice in 2015 (A) elongation, booting, and heading stages in 2016 (B) under different N rates.
FIGURE 5
FIGURE 5
Changes in LAI over the phenological period from tillering to maturity of rice under different N rates in 2015 (A) and 2016 (B), where N0, N3, N5.5, N8.5, N11, N14, N16.5 and N19.5 represents 0, 45, 83, 128, 165, 210, 248, and 293 kg N ha-1 respectively.
FIGURE 6
FIGURE 6
Changes in LAI over two growing seasons in rice under varied N levels 0, 45, 83, 128, 165, 210, 248, and 293 kg N ha-1 in 2015 (A) and 2016 (B) respectively.
FIGURE 7
FIGURE 7
Best-fit models between vegetation indices (VI) and LAI at elongation stage.
FIGURE 8
FIGURE 8
Best-fit models between VIs and LAI at booting stage.
FIGURE 9
FIGURE 9
Best-fit models between VIs and LAI at heading stage.
FIGURE 10
FIGURE 10
Relationship between measured LAI and estimated LAI at elongation.
FIGURE 11
FIGURE 11
Relationship between measured LAI and estimated LAI at booting.
FIGURE 12
FIGURE 12
Relationship between measured LAI and estimated LAI at heading.
FIGURE 13
FIGURE 13
Sensitivity analysis for nine spectral VIs tested to LAI.
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References

    1. Abdel-Rahman E. M., Ahmed F. B., van den Berg M. (2010). Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy. Int. J. Appl. Earth Obs. 12 S52–S57. 10.1016/j.jag.2009.11.003 - DOI
    1. Bajwa S. G., Mishra A. R., Norman R. J. (2010). Canopy reflectance response to plant nitrogen accumulation in rice. Precis. Agric. 11 488–506. 10.1007/s11119-009-9142-0 - DOI
    1. Broge N. H., Leblanc E. (2000). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76 156–172. 10.1016/0034-4257(95)00132-K - DOI
    1. Carvalho S., van der Putten W. H., Hol W. H. (2016). The potential of hyperspectral patterns of winter wheat to detect changes in soil microbial community composition. Front. Plant Sci. 7:759 10.3389/fpls.2016.00759 - DOI - PMC - PubMed
    1. Casa R., Varella H., Buis S., Guérif M., De Solan B., Baret F. (2012). Forcing a wheat crop model with LAI data to access agronomic variables: evaluation of the impact of model and LAI uncertainties and comparison with an empirical approach. Eur. J. Agron. 37 1–10. 10.1016/j.eja.2011.09.004 - DOI