# Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (Triticum aestivum L.) Nitrogen Status Based on Canopy Spectrum

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and methods

#### 2.1. Field Experiments

^{−1}·a

^{−1}(marked as N0, N1, N2, and N3, respectively), 3 levels of phosphorus (P): 0, 32.5, and 64 kg(P)·ha

^{−1}·a

^{−1}(marked as P0, P1, and P2, respectively), and 2 levels of potassium (K): 0 and 50 kg(K)·ha

^{−1}·a

^{−1}(marked as K0 and K2, respectively). 1/4 of the total amount of N fertilizer was applied as winter wheat base fertilizer, and 1/4 was applied as a top dressing at the jointing stage of winter wheat, with the other 1/2 being applied as a top dressing for maize. 1/2 of the total K fertilizer was applied as the base fertilizer for winter wheat, and the other 1/2 was applied as the top dressing for maize. All P fertilizers were applied as base fertilizer for winter wheat. A total of 16 treatments were selected from the orthogonal incomplete design. A randomized 48 plots (8 × 14 m

^{2}each) with 3 replications of each treatment were arranged (as shown in Figure 1 and Table 1). The variety of the winter wheat used was “Kelong199”.

#### 2.2. Aerial Photography of UAVMC and the Reference VIs

#### 2.3. Taking Photos of Winter Wheat with a Smartphone and the Reference Color-Based VIs

#### 2.4. Measurements of Nitrogen (N) Status of Winter Wheat

^{2}in each plot, and total N (TN) was measured using the Kjeldahl method. Winter wheat root zone soil layers in each plot were collected at the depths of 0–30 cm, 30–60 cm, and 60–90 cm, respectively. These soil samples were extracted with 1 mol⋅L

^{−1}KCl and the nitrate nitrogen content was measured using the ultraviolet (UV) spectrometry method. 30 winter wheat samples were randomly selected in each plot. The SPAD value of the first fully expanded leaf of each sample was measured in the field with a SPAD-502 chlorophyll meter, and the average value was recorded as the SPAD value for this plot.

#### 2.5. Analytical Methods

_{Green}and GMR were calculated using the R, G, and B of the winter wheat canopy photos referring to the equations in Table 4. According to the threshold (SAVI

_{Green}> 0 and GMR > 0), the leaf mask for each photo was obtained [44,45]. Using the corresponding leaf mask, the average values of 10 VIs of winter wheat in Table 4 were calculated for each canopy photo. There were 3 photos for each plot, and the average value of the 10 VIs in the 3 photos was taken as the value for this plot. Similarly, based on the correlations between the 10 VIs, the TN of winter wheat, and the soil nitrate nitrogen content for each layer, the VI with the largest correlation coefficient was selected to establish estimation models of soil nitrate nitrogen content in root layers.

^{2}) and root mean square error (RMSE). The RMSE was calculated from:

## 3. Results

#### 3.1. Variation of CNS in the Fertilizer Level Experiment

#### 3.2. Estimation Models for the Method of UAVMC

- Estimation model for 0–30 cm:$${\mathrm{Y}}_{\text{0\u201330}}=659.65{\mathrm{GNDVI}}^{4.667}\text{}{\mathrm{R}}^{2}=0.61$$
- Estimation model for 30–60 cm:$${\mathrm{Y}}_{\text{30\u201360}}=218.88{\mathrm{GNDVI}}^{5.033}\text{}{\mathrm{R}}^{2}=0.60$$
- Estimation model for 60–90 cm:$${\mathrm{Y}}_{\text{60\u201390}}=782.74{\mathrm{GNDVI}}^{7.747}\text{}{\mathrm{R}}^{2}=0.54$$
- Estimation model for 0–90 cm:$${\mathrm{Y}}_{\text{0\u201390}}=415.16{\mathrm{GNDVI}}^{4.984}\text{}{\mathrm{R}}^{2}=0.63$$

#### 3.3. Estimation Models for the SPAD Method

- Estimation model for 0–30 cm:$${\mathrm{Y}}_{\text{0\u201330}}=3.79\ast {10}^{-5}{\mathrm{SPAD}}^{3.530}\text{}{\mathrm{R}}^{2}=0.55$$
- Estimation model for 60–30 cm:$${\mathrm{Y}}_{\text{30\u201360}}=6.52\ast {10}^{-6}{\mathrm{SPAD}}^{3.639}\text{}{\mathrm{R}}^{2}=0.45$$
- Estimation model for 60–90 cm:$${\mathrm{Y}}_{\text{60\u201390}}=5.19\ast {10}^{-9}{\mathrm{SPAD}}^{5.360}\text{}{\mathrm{R}}^{2}=0.45$$
- Estimation model for 0–90 cm:$${\mathrm{Y}}_{\text{0\u201390}}=1.11\ast {10}^{-5}{\mathrm{SPAD}}^{3.676}\text{}{\mathrm{R}}^{2}=0.54$$

#### 3.4. Estimation Models for the PHONEP Method

- Estimation model for 0–30 cm:$${\mathrm{Y}}_{\text{0\u201330}}=4311.7{\mathrm{VARI}}^{2.1796}\text{}{\mathrm{R}}^{2}=0.82$$
- Estimation model for 30–60 cm:$${\mathrm{Y}}_{\text{30\u201360}}=2580.3{\mathrm{VARI}}^{2.4889}\text{}{\mathrm{R}}^{2}=0.71$$
- Estimation model for 60–90 cm:$${\mathrm{Y}}_{\text{60\u201390}}=6750.4{\mathrm{VARI}}^{3.286}\text{}{\mathrm{R}}^{2}=0.67$$
- Estimation model for 0–90 cm:$${\mathrm{Y}}_{\text{0\u201390}}=2904.2{\mathrm{VARI}}^{2.3097}\text{}{\mathrm{R}}^{2}=0.81$$

#### 3.5. Validation

^{2}= 0.93 and RMSE = 9.80 mg/kg). The SPAD method had the lowest estimation accuracy (R

^{2}= 0.61 and RMSE = 19.80 mg/kg). For the method of UAVMC, R

^{2}is 0.86 and RMSE is 12.40 mg/kg. As shown in Figure 5, all three methods had relatively high estimation accuracy in the low-value areas of soil nitrate nitrogen content. While in the high-value areas of soil nitrate nitrogen content, the estimated values of the three methods were significantly lower than the measured values.

## 4. Discussion

#### 4.1. Comparison of the Three Estimation Methods

#### 4.2. Effect of P Fertilizer Shortage on CNS Estimation

#### 4.3. The Saturation Response of the Estimation Indices

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Mogollón, J.; Lassaletta, L.; Beusen, A.; Van Grinsven, H.; Westhoek, H.; Bouwman, A. Assessing future reactive nitrogen inputs into global croplands based on the shared socioeconomic pathways. Environ. Res. Lett.
**2018**, 13. [Google Scholar] [CrossRef] - Hawkesford, M.J. Reducing the reliance on nitrogen fertilizer for wheat production. J. Cereal Sci.
**2014**, 59, 276–283. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Raun, W.R.; Johnson, G.V. Improving nitrogen use efficiency for cereal production. Agron. J.
**1999**, 91, 357–363. [Google Scholar] [CrossRef] [Green Version] - Cameira, M.; Mota, M. Nitrogen related diffuse pollution from horticulture production—Mitigation practices and assessment strategies. Horticulturae
**2017**, 3, 25. [Google Scholar] [CrossRef] [Green Version] - Feng, W.; Zhu, Y.; Yao, X.; Tian, Y.; Zhuang, S.; Cao, W. Monitoring plant nitrogen accumulation dynamics with hyperspectral remote sensing in wheat. Sci. Agric. Sin.
**2008**, 41, 1937–1946. [Google Scholar] - Qi, Y.; Leng, Y.; Wang, M.; Hu, Y.; Bai, Y. Design of decision support system for soil testing and formula fertilization based on the intelligent agriculture. In Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications, Xi’an, China, 10–11 December 2016. [Google Scholar]
- Li, G.; Zhu, L.; Li, J. Present status of research and application of non-destructive measurement of nitrogen nutrition diagnosis. Heilongjiang Agric. Sci.
**2008**, 4, 127–129. [Google Scholar] - Ali, M.; Al-Ani, A.; Eamus, D.; Tan, D.K. Leaf nitrogen determination using non-destructive techniques—A review. J. Plant Nutr.
**2017**, 40, 928–953. [Google Scholar] [CrossRef] - Shou, L.; Jia, L.; Cui, Z.; Chen, X.; Zhang, F. Using high-resolution satellite imaging to evaluate nitrogen status of winter wheat. J. Plant Nutr.
**2017**, 30, 1669–1680. [Google Scholar] [CrossRef] - Wright, D.L.; Rasmussen, V.P.; Ramsey, R.D.; Baker, D.J.; Ellsworth, J.W. Canopy reflectance estimation of wheat nitrogen content for grain protein management. GISci. Remote Sens.
**2004**, 41, 287–300. [Google Scholar] [CrossRef] - Eitel, J.; Long, D.; Gessler, P.; Smith, A. Using in-situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status. Int. J. Remote Sens.
**2007**, 28, 4183–4190. [Google Scholar] [CrossRef] - Jia, Y.; Su, Z.; Shen, W.; Yuan, J.; Xu, Z. UAV remote sensing image mosaic and its application in agriculture. Int. J. Smart Home
**2016**, 10, 159–170. [Google Scholar] [CrossRef] - Dash, J.; Pearse, G.; Watt, M. UAV multispectral imagery can complement satellite data for monitoring forest health. Remote Sens.
**2018**, 10, 1216. [Google Scholar] [CrossRef] [Green Version] - Hassan, M.A.; Yang, M.; Rasheed, A.; Yang, G.; Reynolds, M.; Xia, X.; Xiao, Y.; He, Z. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Sci.
**2019**, 282, 95–103. [Google Scholar] [CrossRef] [PubMed] - Liu, C.; Wang, Z.; Chen, Z.; Zhou, L.; Yue, X.; Miao, Y. Nitrogen monitoring of winter wheat based on unmanned aerial vehicle remote sensing image. Trans. Chin. Soc. Agric. Mach.
**2018**, 49, 207–214. [Google Scholar] - Li, H.; Li, J.; Lei, Y.; Zhang, Y. Diagnosis of nitrogen nutrition of winter wheat and summer corn using images from digital camera equipped on unmanned aerial vehicle. Chin. J. Eco Agric.
**2017**, 25, 1832–1841. [Google Scholar] - Monostori, I.; Árendás, T.; Hoffman, B.; Galiba, G.; Gierczik, K.; Szira, F.; Vágújfalvi, A. Relationship between SPAD value and grain yield can be affected by cultivar, environment and soil nitrogen content in wheat. Euphytica
**2016**, 211, 103–112. [Google Scholar] [CrossRef] [Green Version] - Röll, G.; Hartung, J.; Graeff-Hönninger, S. Determination of plant nitrogen content in wheat plants via spectral reflectance measurements: Impact of leaf number and leaf position. Remote Sens.
**2019**, 11, 2794. [Google Scholar] [CrossRef] [Green Version] - Li, Z.; Jin, X.; Yang, G.; Drummond, J.; Yang, H.; Clark, B.; Li, Z.; Zhao, C. Remote sensing of leaf and canopy nitrogen status in winter wheat (Triticum aestivum L.) based on N-PROSAIL model. Remote Sens.
**2018**, 10, 1463. [Google Scholar] [CrossRef] [Green Version] - Liu, C.; Fang, Z.; Chen, Z.; Zhou, L.; Yue, X.; Wang, Z.; Wang, C.; Miao, Y. Nitrogen nutrition diagnosis of winter wheat based on ASD Field Spec3. Trans. Chin. Soc. Agric. Eng.
**2018**, 34, 162–169. [Google Scholar] - Jia, L.; Chen, X.; Zhang, F.; Buerkert, A.; Roemheld, V. Optimum nitrogen fertilization of winter wheat based on color digital camera images. Commun. Soil Sci. Plant Anal.
**2007**, 38, 1385–1394. [Google Scholar] [CrossRef] - Xia, S.; Zhang, C.; Li, H.; Zhang, Y.; Hu, C. Study on nitrogen diagnosis and fertilization recommendation of winter wheat using canopy digital images from cellphone camera. Chin. J. Eco Agric.
**2018**, 26, 538–546. [Google Scholar] - Kaur, N.; Singh, D. Android based mobile application to estimate nitrogen content in rice crop. Int. J. Comput. Trends Technol. IJCTT
**2016**, 38, 87–91. [Google Scholar] [CrossRef] - Intaravanne, Y.; Sumriddetchkajorn, S. Android-based rice leaf color analyzer for estimating the needed amount of nitrogen fertilizer. Comput. Electron. Agric.
**2015**, 116, 228–233. [Google Scholar] [CrossRef] - Padilla, F.M.; de Souza, R.; Peña-Fleitas, M.T.; Gallardo, M.; Giménez, C.; Thompson, R.B. Different responses of various chlorophyll meters to increasing nitrogen supply in sweet pepper. Front. Plant Sci.
**2018**, 9, 1752. [Google Scholar] [CrossRef] [Green Version] - Daughtry, C.S.T.; Walthall, C.L.; Kim, M.S.; Brown De Colstoun, E.; McMurtrey, J.E., III. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ.
**2000**, 74, 229–239. [Google Scholar] [CrossRef] - Navarro, G.; Caballero, I.; Silva, G.; Parra, P.C.; Vázquez, Á.; Caldeira, R. Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. Int. J. Appl. Earth Obs. Geoinf.
**2017**, 58, 97–106. [Google Scholar] [CrossRef] [Green Version] - Gong, P.; Pu, R.; Biging, G.S.; Larrieu, M.R. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Trans. Geosci. Remote Sens.
**2003**, 41, 1355–1362. [Google Scholar] [CrossRef] [Green Version] - Qi, J.; Chehbouni, A.; Huete, A.; Kerr, Y.; Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ.
**1994**, 48, 119–126. [Google Scholar] [CrossRef] - Chen, J. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens.
**1996**, 22, 229–242. [Google Scholar] [CrossRef] - Fieuzal, R.; Sicre, C.M.; Baup, F. Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks. Int. J. Appl. Earth Obs. Geoinf.
**2017**, 57, 14–23. [Google Scholar] [CrossRef] - Li, X.; Xu, X.; Bao, Y.; Huang, W.; Luo, J.; Dong, Y.; Song, X.; Wang, J. Retrieving LAI of winter wheat based on sensitive vegetation index by the segmentation method. Sci. Agric. Sin.
**2012**, 45, 3486–3496. [Google Scholar] - Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ.
**1996**, 55, 95–107. [Google Scholar] [CrossRef] - Roujean, J.L.; Breon, F.M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ.
**1995**, 51, 375–384. [Google Scholar] [CrossRef] - Jordan, C.F. Derivation of leaf-area index from quality of light on the forest floor. Ecology
**1969**, 50, 663–666. [Google Scholar] [CrossRef] - Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ.
**1988**, 25, 295–309. [Google Scholar] [CrossRef] - Baresel, J.P.; Rischbeck, P.; Hu, Y.; Kipp, S.; Barmeier, G.; Mistele, B.; Schmidhalter, U. Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat. Comput. Electron. Agric.
**2017**, 140, 25–33. [Google Scholar] [CrossRef] - Pagola, M.; Ortiz, R.; Irigoyen, I.; Bustince, H.; Barrenechea, E.; Aparicio-Tejo, P.; Lamsfus, C.; Lasa, B. New method to assess barley nitrogen nutrition status based on image colour analysis: Comparison with SPAD-502. Comput. Electron. Agric.
**2009**, 65, 213–218. [Google Scholar] [CrossRef] - Karcher, D.E.; Richardson, M.D. Quantifying turfgrass color using digital image analysis. Crop Sci.
**2003**, 43, 943–951. [Google Scholar] [CrossRef] - Guerrero, J.M.; Pajares, G.; Montalvo, M.; Romeo, J.; Guijarro, M. Support vector machines for crop/weeds identification in maize fields. Expert Syst. Appl.
**2012**, 39, 11149–11155. [Google Scholar] [CrossRef] - Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ.
**1979**, 8, 127–150. [Google Scholar] [CrossRef] [Green Version] - Wang, Y.; Wang, D.; Zhang, G.; Wang, C. Digital camera-based image segmentation of rice canopy and diagnosis of nitrogen nutrition. Trans. Chin. Soc. Agric. Eng.
**2012**, 28, 131–136. [Google Scholar] - Beniaich, A.; Naves Silva, M.L.; Avalos, F.A.P.; Menezes, M.D.; Candido, B.M. Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an onboard digital photographic camera. Semin. Cienc. Agrar.
**2019**, 40, 49–66. [Google Scholar] [CrossRef] [Green Version] - Wang, Y.; Wang, D.; Zhang, G.; Wang, J. Estimating nitrogen status of rice using the image segmentation of GR thresholding method. Field Crops Res.
**2013**, 149, 33–39. [Google Scholar] [CrossRef] - Li, Y.; Chen, D.; Walker, C.N.; Angus, J.F. Estimating the nitrogen status of crops using a digital camera. Field Crops Res.
**2010**, 118, 221–227. [Google Scholar] [CrossRef] - Gitelson, A.A.; Viña, A.; Arkebauer, T.J.; Rundquist, D.C.; Keydan, G.; 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] - Zhang, X. Crop Roots and Utilization of Soil Water; China Meteorological Press: Beijing, China, 1999. [Google Scholar]
- Gao, L.; Yang, G.; Li, H.; Li, Z.; Feng, H.; Wang, L.; Dong, J.; He, P. Winter wheat LAI estimation using unmanned aerial vehicle RGB-imaging. Chin. J. Eco Agric.
**2016**, 24, 1254–1264. [Google Scholar] - Yu, C.; Qin, J.; Xu, J.; Nie, H.; Luo, Z.; Cen, K. Straw combustion in circulating fluidized bed at low-temperature: Transformation and distribution of potassium. Can. J. Chem. Eng.
**2010**, 88, 874–880. [Google Scholar] [CrossRef] - Gu, Y.; Wylie, B.K.; Howard, D.M.; Phuyal, K.P.; Ji, L. NDVI saturation adjustment: A new approach for improving cropland performance estimates in the Greater Platte River Basin, USA. Ecol. Indic.
**2013**, 30, 1–6. [Google Scholar] [CrossRef] - De Souza, R.; Peña-Fleitas, M.T.; Thompson, R.B.; Gallardo, M.; Grasso, R.; Padilla, F.M. The use of chlorophyll meters to assess crop N status and derivation of sufficiency values for sweet pepper. Sensors
**2019**, 19, 2949. [Google Scholar] [CrossRef] [Green Version]

**Figure 2.**Pseudo color multispectral image of the experimental area on 4 April 2019 (Red-Green-Near Infrared).

**Figure 3.**Winter wheat canopy photos. (

**a**–

**d**) Are from the plots with N3, N2, N1, and N0, respectively.

**Figure 4.**The distribution of TN of winter wheat and 0–90 cm soil nitrate nitrogen content in different fertilizer level experimental plots (2018 and 2019).

**Figure 5.**Correlations between nitrate nitrogen content in the 0–90 cm soil layers (mg/kg) obtained by the laboratory testing method and estimated by the methods of UAVMC, SPAD, and PHONEP.

**Figure 6.**The impacts of P fertilizer shortage on N estimation model for the PHONEP method. (

**a**,

**b**) Are the distributions between VARI and TN of plant for the data including and excluding P0 plots, respectively. (

**c**,

**d**) Are the distributions between VARI and nitrate nitrogen content in the 0–90 cm soil layer for the data including and excluding P0 plots, respectively.

**Figure 7.**Analyses of the saturation response of GNDVI, SPAD, and VARI. (

**a**–

**c**) Show the saturation response for the method of GNDVI, SPAD, and VARI, respectively.

Treatment | Plot Number in Figure 1 | Treatment | Plot Number in Figure 1 | Treatment | Plot Number in Figure 1 | Treatment | Plot Number in Figure 1 |
---|---|---|---|---|---|---|---|

N3P1K1 | 1, 2, 3 | N2P2K1 | 4, 5, 6 | N1P1K1 | 7, 8, 9 | N0P0K1 | 10, 11, 12 |

N3P0K0 | 22, 23, 24 | N2P0K0 | 19, 20, 21 | N1P0K0 | 16, 17, 18 | N0P0K0 | 13, 14, 15 |

N3P1K0 | 25, 26, 27 | N2P1K0 | 28, 29, 30 | N1P1K0 | 31, 32, 33 | N0P1K0 | 34, 35, 36 |

N3P2K0 | 46, 47, 48 | N2P2K0 | 43, 44, 45 | N1P2K0 | 40, 41, 42 | N0P2K0 | 37, 38, 39 |

Band | Band Width (nm) | Wave Width (nm) | Image Resolution | Field of View H° × V° |
---|---|---|---|---|

Green | 40 | 550 | 1280 × 960 | 62.2 × 48.7 |

Red | 40 | 660 | 1280 × 960 | 62.2 × 48.7 |

Red edge | 40 | 735 | 1280 × 960 | 62.2 × 48.7 |

Near Infrared | 40 | 790 | 1280 × 960 | 62.2 × 48.7 |

Name of VI | Abbreviation | Equation | Reference |
---|---|---|---|

Difference vegetation index | DVI | $\mathrm{DVI}={R}_{NIR}-{R}_{RED}$ | [26] |

Green normalized difference vegetation index | GNDVI | $\mathrm{GNDVI}=\left({R}_{NIR}-{R}_{GRE}\right)/\left({R}_{NIR}+{R}_{GRE}\right)$ | [27] |

Modified non-linear vegetation index | MNLI | $\mathrm{MNLI}=\left(1.5{R}_{NIR}^{2}-1.5{R}_{GRE}\right)/\left({R}_{NIR}^{2}+{R}_{RED}+0.5\right)$ | [28] |

The second modified soil-adjusted vegetation index | MSAVI2 | $\mathrm{MSAVI}2=\frac{{2\mathit{R}}_{\mathit{NIR}}+1-\sqrt{{\left({2\mathit{R}}_{\mathit{NIR}}+2\right)}^{2}-8\left({\mathit{R}}_{\mathit{NIR}}{-\mathit{R}}_{\mathit{RED}}\right)}}{2}$ | [29] |

Modified simple ratio | MSR | $\mathrm{MSR}=\raisebox{1ex}{$\left(\frac{{R}_{NIR}}{{R}_{RED}}-1\right)$}\!\left/ \!\raisebox{-1ex}{$\left(\sqrt{\frac{{R}_{NIR}}{{R}_{RED}}}+1\right)$}\right.$ | [30] |

Normalized vegetation index | NDVI | $\mathrm{NDVI}=\left({R}_{NIR}-{R}_{RED}\right)/\left({R}_{NIR}+{R}_{RED}\right)$ | [31] |

Non-linear vegetation index | NLI | $\mathrm{NLI}=\left({R}_{NIR}^{2}-{R}_{RED}\right)/\left({R}_{NIR}^{2}+{R}_{RED}\right)$ | [32] |

Optimized soil-adjusted vegetation index | OSAVI | $\mathrm{OSAVI}=\left({R}_{NIR}-{R}_{RED}\right)/\left({R}_{NIR}+{R}_{RED}+0.16\right)$ | [33] |

Renormalized difference vegetation index | RDVI | $\mathrm{RNDVI}=\left({R}_{NIR}-{R}_{RED}\right)/\sqrt{\left({R}_{NIR}+{R}_{RED}\right)}$ | [34] |

Ratio vegetation index | RVI | $\mathrm{RVI}={R}_{NIR}/{R}_{RED}$ | [35] |

Soil-adjusted vegetation index | SAVI | $\mathrm{SAVI}=1.5\left({R}_{NIR}-{R}_{RED}\right)/\left({R}_{NIR}+{R}_{RED}+0.5\right)$ | [36] |

_{RED}, R

_{GRE}, and R

_{NIR}are reflectances of the red, green, and near-infrared bands, respectively.

Name of VI | Abbreviation | Equation | Reference |
---|---|---|---|

The dark green color index | DGCI | $\mathrm{DGCI}=\left(\left(\mathrm{H}-60\right)/60+\left(1-\mathrm{S}\right)+\left(1-\mathrm{B}\right)\right)/3$ | [39] |

Excess green index | EXG | $\mathrm{EXG}=\left(2\mathrm{G}-\mathrm{R}-\mathrm{B}\right)/\left(\mathrm{R}+\mathrm{G}+\mathrm{B}\right)$ | [40] |

Green leaf index | GLI | $\mathrm{GLI}=\left(2\mathrm{G}-\mathrm{R}-\mathrm{B}\right)/\left(2\mathrm{G}+\mathrm{R}+\mathrm{G}\right)$ | [41] |

The difference between green and red | GMR | $\mathrm{GMR}=\left(\mathrm{G}-\mathrm{R}\right)/\left(\mathrm{R}+\mathrm{G}+\mathrm{B}\right)$ | [42,43] |

Green-red vegetation index | GRVI | $\mathrm{GRVI}=\left(\mathrm{G}-\mathrm{R}\right)/\left(\mathrm{G}+\mathrm{R}\right)$ | [41] |

Normalized blueness intensity | NBI | $\mathrm{NBI}=\mathrm{B}/\left(\mathrm{R}+\mathrm{G}+\mathrm{B}\right)$ | [44] |

Normalized greenness intensity | NGI | $\mathrm{NGI}=\mathrm{G}/\left(\mathrm{R}+\mathrm{G}+\mathrm{B}\right)$ | [44] |

Normalized redness intensity | NRI | $\mathrm{NRI}=\mathrm{R}/\left(\mathrm{R}+\mathrm{G}+\mathrm{B}\right)$ | [44] |

SAVI green | SAVIGreen | ${\mathrm{SAVI}}_{\mathrm{Green}}=1.5\left(\mathrm{G}-\mathrm{R}\right)/\left(\left(\mathrm{G}+\mathrm{R}+0.5\right)\right)$ | [45] |

Visible atmospherically resistant index | VARI | $\mathrm{VARI}=\left(\mathrm{G}-\mathrm{R}\right)/\left(\mathrm{R}+\mathrm{G}-\mathrm{B}\right)$ | [46] |

The dark green color index | DGCI | $\mathrm{DGCI}=\left(\left(\mathrm{H}-60\right)/60+\left(1-\mathrm{S}\right)+\left(1-\mathrm{B}\right)\right)/3$ | [39] |

Spectral VIs | With TN of Plants | With Soil Nitrate Nitrogen Content | |||
---|---|---|---|---|---|

0–30 cm | 30–60 cm | 60–90 cm | 0–90 cm | ||

DVI | 0.88 ** | 0.49 ** | 0.46 * | 0.38 * | 0.50 ** |

GNDVI | 0.90 ** | 0.52 ** | 0.48 ** | 0.42 ** | 0.52 ** |

MNLI | 0.87 ** | 0.51 ** | 0.47 ** | 0.38 * | 0.51 ** |

MSAVI_{2} | 0.87 ** | 0.50 ** | 0.46 ** | 0.37 * | 0.51 ** |

MSR | 0.89 ** | 0.51 ** | 0.48 ** | 0.39 ** | 0.52 ** |

NDVI | 0.88 ** | 0.47 ** | 0.43 ** | 0.37 * | 0.48 ** |

NLI | 0.89 ** | 0.44 ** | 0.39 * | 0.34 * | 0.44 ** |

OSAVI | 0.89 ** | 0.45 ** | 0.39 * | 0.34 * | 0.45 ** |

RDVI | 0.89 ** | 0.51 ** | 0.43 ** | 0.37 * | 0.50 ** |

RVI | 0.83 ** | 0.50 ** | 0.38 * | 0.26 * | 0.46 ** |

SAVI | 0.88 ** | 0.45 ** | 0.40 ** | 0.35 * | 0.45 ** |

With TN of Plants | With Soil Nitrate Nitrogen Content | ||||
---|---|---|---|---|---|

0–30 cm | 30–60 m | 60–90 cm | 0–90 cm | ||

SPAD | 0.85 ** | 0.57 ** | 0.50 ** | 0.43 ** | 0.55 ** |

Color-Based VIs | With TN of Plants | With Soil Nitrate Nitrogen Content | |||
---|---|---|---|---|---|

0–30 cm | 30–60 cm | 60–90 cm | 0–90 cm | ||

DGCI | 0.70 ** | 0.64 ** | 0.63 ** | 0.62 ** | 0.66 ** |

EXG | −0.71 ** | −0.65 ** | −0.64 ** | −0.59 ** | −0.64 ** |

GLI | −0.54 ** | −0.64 ** | −0.61 ** | −0.56 ** | −0.61 ** |

GMR | 0.68 ** | 0.45 ** | 0.33 * | 0.21 * | 0.40 ** |

GRVI | 0.83 ** | 0.55 ** | 0.55 ** | 0.53 ** | 0.57 ** |

NBI | 0.65 ** | 0.67 ** | 0.60 ** | 0.60 ** | 0.64 ** |

NGI | −0.49 ** | −0.57 ** | −0.54 ** | −0.50 ** | 0.54 ** |

NRI | −0.77 ** | −0.64 ** | −0.62 ** | −0.54 ** | 0.65 ** |

SAVI_{Green} | 0.68 ** | 0.54 ** | 0.48 ** | 0.38 * | 0.52 ** |

VARI | 0.91 ** | 0.72 ** | 0.67 ** | 0.60 ** | 0.72 ** |

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Li, H.; Zhang, Y.; Lei, Y.; Antoniuk, V.; Hu, C.
Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (*Triticum aestivum* L.) Nitrogen Status Based on Canopy Spectrum. *Remote Sens.* **2020**, *12*, 95.
https://doi.org/10.3390/rs12010095

**AMA Style**

Li H, Zhang Y, Lei Y, Antoniuk V, Hu C.
Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (*Triticum aestivum* L.) Nitrogen Status Based on Canopy Spectrum. *Remote Sensing*. 2020; 12(1):95.
https://doi.org/10.3390/rs12010095

**Chicago/Turabian Style**

Li, Hongjun, Yuming Zhang, Yuping Lei, Vita Antoniuk, and Chunsheng Hu.
2020. "Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (*Triticum aestivum* L.) Nitrogen Status Based on Canopy Spectrum" *Remote Sensing* 12, no. 1: 95.
https://doi.org/10.3390/rs12010095