# Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting

^{1}

^{2}

^{*}

## Abstract

**:**

_{2}) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Description of the Study Sites

^{2}and growing degree days (GDD) over a base temperature of 10 °C reaches 1600.

#### 2.2. Canopy Leaf Distributions Generated with and without Hail Protection

#### 2.3. LAI Estimation

^{2}= 0.905) was calculated and used to determine the leaf area of all samples [34]. LAI was then calculated by dividing the LA of each sample by the canopy cover area measured by carefully delineating its surface on the NIR image in each case.

#### 2.4. Spectral Data Acquisition

#### 2.5. Multispectral Image Analysis

^{2}, so spectral calculations for VIs were performed with over 400,000 pixels per sample. As previously mentioned, the area was used to calculate LAI. Brightness values were converted to reflectance.

#### 2.6. Vegetation Indices

_{2}) [24], soil-adjusted vegetation index 2 [35], and the perpendicular vegetation index (PVI) [36] are examples, but with the exception of MSAVI

_{2}, they require knowledge of the gradient of the soil line [17], so all the data collected over sunlit soil at both vineyards were used in their determination (Figure 3). In the case of MSAVI, the determination of the L coefficient originally developed for SAVI (soil-adjusted vegetation index) is also required [24], but MSAVI

_{2}calculation avoids the need for a priori soil line determination. SAVI

_{2}, MSAVI, and MSAVI

_{2}are examples of indices developed to minimise the effect of soil reflectance, as is PVI. The wide dynamic range vegetation index (WDRVI) [18] has been included due to its sensitivity to changes at high biomass and is calculated with two NIR weighting coefficients (α = 0.05 and α = 0.3). The inclusion of a chlorophyll index, CI

_{rededge}, was considered because, aside from performing as a good LAI estimator in maize, it has been demonstrated to be a good estimator of gross primary production [25,37], and this would make it useful for VRB assessment, particularly since the recent availability of RE bands in several satellite-borne sensors. The VIs studied were selected among those that are widely used and that enhance sensitivity, reduce soil noise, employ either typical or unusual spectral bands, and are representative of different calculation procedures. The main characteristics of the VIs chosen for this study are listed in Table 1.

#### 2.7. Data Analysis

_{0}is the incoming radiation at the top of the canopy, k is a parameter that combines the transmission coefficient for any given wavelength and a geometrical component accounting for leaf distribution and orientation, and L is the leaf area above the plane. The parameter k is often considered a constant for a given crop [38], as can the Q/Q

_{0}ratio at any given moment. As this ratio is associated to the fraction of photosynthetically active radiation that VIs are known to be closely associated with, considering this assumption and the exponential/logarithmic relationship between the dependent and independent variables, Equation (1) may be modified to

_{1}, and a

_{2}are constants and ln(LAI) is the natural logarithm of the LAI.

_{1}and b

_{2}are constants.

_{1}of the linear functions obtained were used to compare each VI’s sensitivity, with NDVI as the reference, using the expression

## 3. Results

#### 3.1. VI Accuracy Comparison

^{2}for all the VIs tested as a function of LAI, at all sites with no hail protection. The root-mean-square error (RMSE) for LAI prediction from each VI is also shown.

^{2}values and correspond in both cases to NDVI.

#### 3.2. VI Sensitivity to LAI Changes

#### 3.3. Effect of Grenbiule Hail-Protection Netting on the VI–LAI Relationship

## 4. Discussion

#### 4.1. VI Accuracy Comparison

^{2}was used as a means to estimate the accuracy of the VIs. Results shown in Table 2 indicate that ratio indices are substantially more accurate than perpendicular or chlorophyll indices, some of which show very poor or no linear relationship whatsoever with LAI [3]. As the practical application of these relationships aims to estimate LAI as the dependent variable, the functions were inverted, and an RMSE for LAI was calculated, with similar results in order of accuracy (Table 2). Exclusion of bare soil values exposes the influence that the no-leaf data has in accuracy (R

^{2}= 0.48 for linear regression with no soil data vs. R

^{2}= 0.98 for the logarithmic function including soil values with NDVI). This effect is consistent with studies that have shown that at low vegetation cover values, soil backscattering can contribute to more signal than vegetation cover, particularly with some indices such as NDVI [24]. Moreover, inspection of the soil lines shown in Figure 3 illustrates that bare soil data can vary somewhat within the study region, which in turn would influence the y-intercept and regression functions obtained for otherwise identical canopies. From a practical standpoint, if RS data available have sufficient resolution to allow canopy values to be separated from soil values, the high LAI values typical of VSPs should occlude soil response and VIs adjusted for soil response would be expected to show little improvement in the accuracy of LAI estimation. In turn, this would preclude the need for soil line determination for the index calculation. However, SAVI

_{2}exhibits the lowest RMSE in LAI estimation with linear models that exclude bare soil values, a result that is difficult to interpret and needs to be addressed with further studies.

#### 4.2. VI Sensitivity to LAI Changes

_{2}presents better Sr than NDVI, an unexpected finding considering its development was aimed specifically at correcting bias shown by NDVI with low vegetation cover. Its use has borne good results in orchard vegetation cover estimations [41] as well as in forest biomass estimations [42] and appears promising to estimate land cover in a sparsely vegetated area like shrubland. In summary, NDVI seems to be the least sensitive of the studied indices, even if the most accurate. These results suggest a WDRVI to be an adequate compromise between accuracy and sensitivity. This effect is in overall agreement with studies showing that in situations with dense green biomass (NDVI > 0.4), the sensitivity of the WDRVI may be 47% greater than that of the NDVI in several types of vegetation cover, including woodlands, sub-humid to humid grasslands, and croplands [29].

#### 4.3. Effect of Grenbiule Hail-Protection Netting on the VI–LAI Relationship

## 5. Conclusions

_{2}and MSAVIs) and the chlorophyll index (CI

_{rededge}). NDVI exhibited the highest accuracy. Logarithmic models, including the no-leaf values at each site, presented very high adjusted R

^{2}values (over 0.80), but values above an LAI of one followed a linear distribution. Considering that LAI values above 1 occlude direct soil reflectance and that pure canopy field values are commonly well above this value, a linear model for the VI–LAI relationship is acceptable above this value and avoids possible soil contribution to canopy values. This effect is desirable because soil lines in the same area may vary.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Ravaz, M.L. L’effeuillage de la vigne. Annales de L’Ecole Nationale d’agriculture de Montpellier
**1911**, 11, 216–244. [Google Scholar] - Watson, D.J. Comparative Physiological Studies on the Growth of Field Crops: I. Variation in Net Assimilation Rate and Leaf Area between Species and Varieties, and within and between Years. Ann. Bot.
**1947**, 11, 41–76. [Google Scholar] [CrossRef] - Milthorpe, F.L.; Moorby, J. An Introduction to Crop Physiology; Cambridge University Press: Cambridge, UK, 1974. [Google Scholar]
- Hall, A.; Lamb, D.W.; Holzapfel, B.; Louis, J. Optical remote sensing applications in viticulture—A Review. Aust. J. Grape Wine Res.
**2002**, 8, 36–47. [Google Scholar] [CrossRef] - Johnson, L.F. Temporal stability of an NDVI-LAI relationship in a Napa Valley vineyard. Aust. J. Grape Wine Res.
**2003**, 9, 96–101. [Google Scholar] [CrossRef] [Green Version] - Hall, A.; Lamb, D.; Holzapfel, B.P.; Louis, J.P. Within-season temporal variation in correlations between vineyard canopy and winegrape composition and yield. Precis. Agric.
**2011**, 12, 103–117. [Google Scholar] [CrossRef] - Towers, P.C.; Strever, A.; Poblete-Echeverría, C. Estimation of Vine Pruning Weight using Remote Sensing Data: Relative Contribution of Variables. In Proceedings of the 20th GiESCO International Meeting, Mendoza, Argentina, 5–10 November 2017. [Google Scholar]
- Howell, G.S. Sustainable Grape Productivity and Growth-Yield Relationship: A Review. Am. J. Enol. Vitic.
**2001**, 52, 165–174. [Google Scholar] - Kliewer, W.M.; Dokoozlian, N.K. Leaf area/crop Weight ratios of grapevines: Influence on fruit composition and wine quality. Am. J. Enol. Vitic.
**2005**, 56, 170–181. [Google Scholar] - Bramley, R.G.V. Understanding variability in winegrape production systems-2. Within vineyard variation in quality over several vintages. Aust. J. Grape Wine Res.
**2005**, 11, 33–42. [Google Scholar] [CrossRef] - Rydberg, A.M. Potential Crop Growth Assessment from Remotely Sensed Images Compared to Ordinary Yield Maps. In Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, 16–19 July 2000. [Google Scholar]
- Machado, S.; Bynum, E.D., Jr.; Archer, T.L.; Lascano, R.J.; Bordovsky, J.; Bronson, K.; Nesmith, D.M.; Segarra, E.; Rosenow, D.T.; Peterson, G.C.; et al. Spatial and temporal variability of sorghum and corn yield: Interactions of biotic and abiotic factors. In Proceedings of the Fifth International Conference on Precision Agriculture, American Society of Agronomy, Bloomington, MN, USA, 16–19 July 2000. [Google Scholar]
- Kravchenko, A.N.; Robertson, G.P.; Thelen, K.D.; Harwood, R.R. Management, Topographical, and Weather Effects on Spatial Variability of Crop Grain Yields. Agron. J.
**2005**, 97, 515–523. [Google Scholar] [CrossRef] - Hall, A.; Louis, J.P.; Lamb, D.W. Low-resolution remotely sensed images of winegrape vineyards map spatial variability in planimetric canopy area instead of leaf area index. Aust. J. Grape Wine Res.
**2008**, 14, 9–17. [Google Scholar] [CrossRef] - Walthall, C.L.; Pachepsky, Y.; Dulaney, W.P.; Timlin, D.J.; Daughtry, C.S.T. Exploitation of spatial information in high resolution digital imagery to map leaf area index. Precis. Agric.
**2007**, 8, 311–321. [Google Scholar] [CrossRef] [Green Version] - Poblete-Echeverría, C.; Olmedo, G.F.; Ingram, B.; Bardeen, M. Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard. Remote Sens.
**2017**, 9, 268. [Google Scholar] [CrossRef] - Jackson, R.D.; Huete, A.R. Interpreting Vegetation Indices. Prev. Vet. Med.
**1991**, 11, 185–200. [Google Scholar] [CrossRef] - Gitelson, A.A. Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation. J. Plant Physiol.
**2004**, 161, 165–173. [Google Scholar] [CrossRef] [PubMed] - Huete, A.R. A Soil-Adjusted Vegetation Index (SAVI). Remote Sens. Environ.
**1988**, 25, 295–309. [Google Scholar] [CrossRef] - Qi, J.; Kerr, Y.; Chehbouni, A. External factor consideration in vegetation index development. In Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, Val D’Isere, France, 17–22 January 1994; pp. 723–730. [Google Scholar]
- Ray, T.W. A FAQ on Vegetation in Remote Sensing. Division of Geological and Planetary Sciences, California Institute of Technology. 1994. Available online: http://www.yale.edu/ceo/Documentation/rsvegfaq.html (accessed on 15 August 2018).
- Proffitt, T.; Pearse, B. Adding value to the wine business precisely: Using precision viticulture technology in Margaret River. Managing vineyard variation—Precision viticulture workshop. In Proceedings of the 12th Australian Wine Industry Technical Conference; The Australian and New Zealand Grapegrower and Winemaker: Broadview, Australia, 2004; pp. 40–44. [Google Scholar]
- Richardson, A.J.; Wiegand, C.L. Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens.
**1977**, 43, 1541–1552. [Google Scholar] - Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S. A Modified Soil Adjusted Vegetation Index. Remote Sens. Environ.
**1994**, 48, 119–126. [Google Scholar] [CrossRef] - Gitelson, A.A.; Viña, A.; Arkebauer, T.J.; Rundquist, D.C.; Keydan, G.P.; Leavitt, B. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett.
**2003**, 30. [Google Scholar] [CrossRef] [Green Version] - Steele, M.R.; Gitelson, A.A.; Rundquist, D. Nondestructive Estimation of Leaf Chlorophyll Content in Grapes. Am. J. Enol. Vitic.
**2008**, 59, 299–305. [Google Scholar] - Gitelson, A.A.; Kaufman, Y.J.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ.
**2002**, 80, 76–87. [Google Scholar] [CrossRef] [Green Version] - Viña, A.; Henebry, G.M.; Gitelson, A.A. Satellite monitoring of vegetation dynamics: Sensitivity enhancement by the wide dynamic range vegetation index. Geophys. Res. Lett.
**2004**, 31. [Google Scholar] [CrossRef] [Green Version] - Keller, M. The Science of Grapevines. Anatomy and Physiology; Academic Press: London, UK, 2010; pp. 125–158. [Google Scholar]
- Hatfield, J.L.; Gitelson, A.A.; Schepers, J.S.; Walthall, C.L. Application of Spectral Remote Sensing for Agronomic Decisions. Agron. J.
**2008**, 100, S-117–S-131. [Google Scholar] [CrossRef] - Bannari, A.; Morin, D.; Bonn, F.; Huete, A.R. A review of vegetation indices. Remote Sens. Rev.
**1995**, 13, 95–120. [Google Scholar] [CrossRef] - Lobos, G.A.; Poblete Echeverría, C. Spectral Knowledge (SK-UTALCA): Software for Exploratory Analysis of High-Resolution Spectral Reflectance Data on Plant Breeding. Front. Plant Sci.
**2017**, 7, 1996. [Google Scholar] [CrossRef] [PubMed] - Chanda, S.V.; Singh, Y.D. Estimation of Leaf Area in Wheat Using Linear Measurements. Plant Breed. Seed Sci.
**2002**, 46, 75–79. [Google Scholar] - Broge, N.H.; Leblanc, E. 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.
**2000**, 76, 156–172. [Google Scholar] [CrossRef] - Wiegand, C.L.; Richardson, A.J.; Escobar, D.E.; Gerbermann, A.H. Vegetation Indices in Crop Assessments. Remote Sens. Environ.
**1991**, 35, 105–119. [Google Scholar] [CrossRef] - Peng, Y.; Gitelson, A.A.; Keydan, G.; Rundquist, D.C.; Moses, W. Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content. Remote Sens. Environ.
**2011**, 115, 978–989. [Google Scholar] [CrossRef] - Glenn, E.P.; Huete, A.R.; Nagler, P.L.; Nelson, S.G. Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape. Sensors
**2008**, 8, 2136. [Google Scholar] [CrossRef] - Carlson, T.N.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ.
**1997**, 62, 241–252. [Google Scholar] [CrossRef] - Johnson, L.F.; Roczen, D.; Youkhana, S. Vineyard Canopy Density Mapping with IKONOS Satellite Imagery. In Proceedings of the Third International Conference on Geospatial Information in Agriculture and Forestry, Denver, CO, USA, 5–7 November 2001. [Google Scholar]
- Perry, A.; Weber, K. Land Cover Change Analysis Using MSAVI2 for Orchard Project; Orchard LCC Project 2015; Idaho State University: Pocatello, ID, USA, 2015. [Google Scholar]
- Laosuwan, T.; Uttaruk, Y. Estimating Tree Biomass via Remote Sensing, MSAVI 2, and Fractional Cover Model. IETE Tech. Rev.
**2014**, 31, 362–368. [Google Scholar] [CrossRef] - Ahmad, F. Spectral vegetation indices performance evaluated for Cholistan Desert. J. Geogr. Reg. Plan.
**2012**, 5, 165–172. [Google Scholar]

**Figure 1.**(

**a**) Example of a vertically shoot positioned (VSP) vineyard (cv. Malbec); (

**b**) Tetracam ADC multispectral camera mounted on a boom on a quadricycle in preparation for a nadir-viewing capture on the canopy in a VSP vineyard.

**Figure 2.**Defoliation procedure: (

**a**–

**g**) Progressive uniform defoliation of a hail-protected VSP, removing the netting to implement the defoliation (netting was reinstalled before each image capture); and (

**h**) Stepwise progressive defoliation of a hail-protected VSP; in this case, the netting remained installed and shoot tips extending over the net were defoliated first to simulate tipping, and canopy under the netting was defoliated later.

**Figure 3.**Soil lines for the two sites used in this study: (orange) soil line for Site 1 and (blue) soil line for Site 2. The linear regression function gradient obtained from plotting R against NIR reflectance of sunlit soil is used to calculate the perpendicular vegetation index (PVI), the Modified soil-adjusted vegetation index (MSAVI), and the Soil-adjusted vegetation index 2 (SAVI

_{2}).

**Figure 6.**Logarithmic regressions of NDVI as a function of LAI including bare soil values, for VSPs with and without hail protection and different defoliation strategies: (

**a**) UOPD—unprotected overall progressive defoliation (same curve presented in Figure 5a, this figure was included to aid with the comparisons), (

**b**) POPD—protected overall progressive defoliation, and (

**c**) PSPD—protected stepwise progressive defoliation. Bars indicate one standard deviation of the canopy NDVI values of each sample.

**Table 1.**List of the vegetation indices used in this study, including acronyms, means of calculation, and salient features.

Index | Formula | Features |
---|---|---|

NDVI—Normalized difference vegetation index | $\left(\mathrm{NIR}-\mathrm{R}\right)/\left(\mathrm{NIR}+\mathrm{R}\right)$ | Robust but insensitive at high leaf area index (LAI) values |

RVI—Ratio vegetation index | $\mathrm{NIR}/\mathrm{R}$ | Sensitive over a broad range |

WDRVI—Wide dynamic range veg. index [6] | $\left(\mathsf{\alpha}\mathrm{NIR}-\mathrm{R}\right)/\left(\mathsf{\alpha}\mathrm{NIR}+\mathrm{R}\right)$ | Sensitive at high LAI |

MSAVI—Modified soil-adjusted vegetation index [24] | $\left(\mathrm{NIR}-\mathrm{R}\right)/\left(\mathrm{NIR}+\mathrm{R}+\mathrm{L}\right)\left(1+\mathrm{L}\right)$^{(1)} | Corrects influence of soil and provides a variable value for L |

MSAVI_{2}—Second modified soil-adjusted vegetation index [24] | $\frac{1}{2}\left[2\left(\mathrm{NIR}+1\right)-\sqrt{2{\left(\mathrm{NIR}+1\right)}^{2}-8\left(\mathrm{NIR}-\mathrm{R}\right)}\right]$ | Corrects influence of soil and provides and does not require L ^{(1)} |

PVI —Perpendicular vegetation index [23] | $\sqrt{{\left(\mathrm{Rs}-\mathrm{Rv}\right)}^{2}+{\left(\mathrm{NIRv}-\mathrm{Rs}\right)}^{2}}$^{(2)} | Affected by atmospheric attenuation and soil dampness |

CI_{rededge}—Red edge chlorophyll index [25] | $\frac{\mathrm{NIR}}{\mathrm{RE}}-1$ | Sensitive in corn, good gross primary productivity estimator |

SAVI_{2—}Soil-adjusted vegetation index 2 [35] | $\frac{\mathrm{NIR}}{\mathrm{R}+\frac{\mathrm{b}}{\mathrm{a}}}$^{(3)} | Sensitive in corn, good gross primary productivity estimator |

^{(1)}$\mathrm{L}=1-2\mathrm{sNDVI}\left(\mathrm{NIR}-\mathrm{sR}\right)$, where s is the slope of the soil line;

^{(2)}Subscripts s and v refer to soil and vegetation, respectively.

^{(3)}a is the soil line slope, and b is the soil line y-intercept.

**Table 2.**Logarithmic and linear (with no-leaf values excluded) regression functions and their corresponding adjusted determination coefficient (R

^{2}) and root-mean-square error (RMSE) of leaf area index (LAI) prediction as a function of each vegetation index. Sampling areas with no hail netting.

Vegetation Index | Logarithmic Regression Model | Adjusted R^{2} | RMSE | Linear Regression Model | Adjusted R^{2} | RMSE |
---|---|---|---|---|---|---|

NDVI | 0.069 ln(LAI) – 0.72 | 0.98 | 0.59 | 0.03 LAI + 0.71 | 0.48 | 0.61 |

WDRVI ^{(1)} | 0.098 ln(LAI) + 0.33 | 0.97 | 0.87 | 0.06 LAI + 0.27 | 0.48 | 0.69 |

MSAVI_{2} | 0.064 ln(LAI) + 0.67 | 0.96 | 1.10 | 0.04 LAI + 0.63 | 0.36 | 0.71 |

WDRVI ^{(2)} | 0.056 ln(LAI) – 0.47 | 0.91 | 2.71 | 0.05 LAI – 0.56 | 0.47 | 0.80 |

RVI | 0.866 ln(LAI) + 7.48 | 0.83 | 5.63 | 1.02 LAI + 5.55 | 0.46 | 0.91 |

SAVI_{2} | 2.886 ln(LAI) + 21.10 | 0.73 | 10.04 | 4.93 LAI + 11.96 | 0.45 | 0.55 |

MSAVI | 0.106 ln(LAI)+ 0.95 | 0.69 | 2.63 | 0.21 LAI + 0.58 | 0.33 | 0.80 |

PVI | 0.027 ln(LAI) + 0.52 | 0.67 | 7.10 | 0.03 LAI + 0.46 | 0.15 | 0.89 |

CI_{rededge} | 0.056 ln(LAI) – 1.05 | 0.41 | 5.40 | - | - | - |

^{2}lower than 0.10 have been omitted.

Vegetation Index | Relative Sensitivity (Sr) |
---|---|

NDVI (Reference) | 1.0 |

RVI | 2.74 |

WDRVI (α = 0.05) | 2.07 |

MSAVI_{2} | 1.45 |

WDRVI (α = 0.3) | 1.42 |

**Table 4.**Logarithmic regression parameter estimations and significance for all data, separated with auxiliary variables and unprotected canopy values as the reference.

NDVI | WDRVI (α = 0.05) | RVI | ||||
---|---|---|---|---|---|---|

Coefficient | Estimation | p-Value | Estimation | p-Value | Estimation | p-Value |

Constant | 0.72 | <0.0001 | −0.48 | <0.0001 | 7.26 | <0.0001 |

lnLAI | 0.07 | <0.001 | 0.05 | <0.0001 | 0.83 | <0.0001 |

POPD | 0.01 | 0.3265 | −0.02 | 0.3435 | −0.5 | 0.1888 |

PSPD | −0.06 | <0.0001 | −0.01 | <0.0001 | −1.73 | <0.0001 |

POPD_lnLAI | −0.01 | 0.0112 | −0.01 | 0.2878 | −0.13 | 0.3308 |

PSPD_lnLAI | 0.0036 | 0.4518 | −0.01 | 0.1026 | −0.22 | 0.0874 |

**Table 5.**Linear regression parameter estimations and significance for all data, separated with auxiliary variables and unprotected canopy values as the reference.

NDVI | WDRVI (α = 0.05) | RVI | ||||
---|---|---|---|---|---|---|

Coefficient | Estimation | p-Value | Estimation | p-Value | Estimation | p-Value |

Constant | 0.72 | <0.0001 | −0.54 | <0.0001 | 5.78 | <0.0001 |

LAI | 0.02 | 0.0212 | 0.05 | 0.0014 | 0.92 | 0.0004 |

POPD | −0.02 | 0.6117 | −0.02 | 0.7242 | −0.18 | 0.8194 |

PSPD | −0.19 | <0.0001 | −0.21 | 0.0001 | −3.32 | 0.0005 |

POPD_LAI | 0.01 | 0.6887 | −0.0042 | 0.8640 | 0.0045 | 0.9916 |

PSPD_LAI | 0.05 | 0.0021 | −0.05 | 0.0443 | 0.61 | 0.1269 |

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**MDPI and ACS Style**

Towers, P.C.; Strever, A.; Poblete-Echeverría, C.
Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting. *Remote Sens.* **2019**, *11*, 1073.
https://doi.org/10.3390/rs11091073

**AMA Style**

Towers PC, Strever A, Poblete-Echeverría C.
Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting. *Remote Sensing*. 2019; 11(9):1073.
https://doi.org/10.3390/rs11091073

**Chicago/Turabian Style**

Towers, Pedro C., Albert Strever, and Carlos Poblete-Echeverría.
2019. "Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting" *Remote Sensing* 11, no. 9: 1073.
https://doi.org/10.3390/rs11091073