# 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

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**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