# Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Satellite Time Series Images

#### 2.2. UAV-Based Imagery

^{®}software (Agisoft

^{©}, 2018 [56]) processing imagery blocks of more than 1000 aerial images acquired with an airborne Parrot Sequoia

^{®}multispectral camera (Parrot

^{©}SA, 2017 [57]). The UAV path was planned to maintain the flight height close to 35 m with respect to the terrain by properly defining waypoint sets for each mission block on the drone guidance platform on the base of the GIS cropland map. With this specification, the aerial images GSD resulted to be 5 cm (Figure 2).

#### 2.3. In-Field Vigour Assessment

#### 2.4. Data Processing

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Nomenclature

$d\left(\mathrm{u},\mathrm{v}\right)$ | pixel in the row $\mathrm{u}$ and column $\mathrm{v}$ of the raster matrix $\mathcal{D}$ |

$\mathcal{D}$ | High-resolution multispectral imagery from UAV platform |

$\mathcal{G}\left(\mathrm{i},\mathrm{j}\right)$ | Subset of UAV pixels $d\left(\mathrm{u},\mathrm{v}\right)$ representing the same area of satellite pixel $s\left(\mathrm{i},\mathrm{j}\right)$ |

${\mathcal{G}}_{\mathrm{int}}\left(\mathrm{i},\mathrm{j}\right)$ | Subset of UAV pixels $d\left(\mathrm{u},\mathrm{v}\right)$ representing only inter-row surfaces |

${\mathcal{G}}_{\mathrm{vin}}\left(\mathrm{i},\mathrm{j}\right)$ | Subset of UAV pixels $d\left(\mathrm{u},\mathrm{v}\right)$ representing only vines canopy |

$NDV{I}_{\mathrm{sat}}\left(\mathrm{i},\mathrm{j}\right)$ | NDVI computed using satellite imagery $\mathcal{S}$ |

$NDV{I}_{\mathrm{uav}}\left(\mathrm{i},\mathrm{j}\right)$ | Comprehensive NDVI computed considering all the UAV pixels in $\mathcal{G}\left(\mathrm{i},\mathrm{j}\right)$ |

$NDV{I}_{\mathrm{vin}}\left(\mathrm{i},\mathrm{j}\right)$ | NDVI computed considering only the UAV pixels ${\mathcal{G}}_{\mathrm{vin}}\left(\mathrm{i},\mathrm{j}\right)$ representing vines canopy |

$NDV{I}_{\mathrm{int}}\left(\mathrm{i},\mathrm{j}\right)$ | NDVI computed considering only the UAV pixels ${\mathcal{G}}_{\mathrm{int}}\left(\mathrm{i},\mathrm{j}\right)$ representing inter-row surface |

${m}_{\mathrm{N}}\left(\mathrm{i},\mathrm{j}\right)$ | digital numbers in the near infrared band of pixel $d\left(\mathrm{u},\mathrm{v}\right)$ |

${m}_{\mathrm{R}}\left(\mathrm{i},\mathrm{j}\right)$ | digital numbers in the red band of pixel $d\left(\mathrm{u},\mathrm{v}\right)$ |

${n}_{\mathrm{N}}\left(\mathrm{i},\mathrm{j}\right)$ | digital numbers in the near infrared band of pixel $s\left(\mathrm{i},\mathrm{j}\right)$ |

${n}_{\mathrm{R}}\left(\mathrm{i},\mathrm{j}\right)$ | digital numbers in the red band of pixel $s\left(\mathrm{i},\mathrm{j}\right)$ |

$s\left(\mathrm{i},\mathrm{j}\right)$ | pixel in the row $\mathrm{i}$ and column $\mathrm{j}$ of the raster matrix $\mathcal{S}$ |

$\mathcal{S}$ | Decametric resolution multispectral imagery from satellite platform |

${\alpha}_{d}\left(\mathrm{u},\mathrm{v}\right)$ | latitude coordinate (expressed in WGS84) of pixel $d\left(\mathrm{u},\mathrm{v}\right)$ centre |

${\alpha}_{s}\left(\mathrm{i},\mathrm{j}\right)$ | latitude coordinate (expressed in WGS84) of the upper left corner of pixel $s\left(\mathrm{i},\mathrm{j}\right)$ |

${\beta}_{d}\left(\mathrm{u},\mathrm{v}\right)$ | longitude coordinate (expressed in WGS84) of pixel $d\left(\mathrm{u},\mathrm{v}\right)$ centre |

${\beta}_{s}\left(\mathrm{i},\mathrm{j}\right)$ | longitude coordinate (expressed in WGS84) of the upper left corner of pixel $s\left(\mathrm{i},\mathrm{j}\right)$ |

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**Figure 1.**Selected test field located in Serralunga d’Alba (Piedmont, northwest of Italy). The boundaries of the three considered parcels, named “Parcel-A”, ”Parcel-B” and “Parcel-C”, are marked with solid green polygons. The cropland region, represented by pixel ${s}_{8,20}$ of the Sentinel-2 tile, is highlighted by a yellow square. The map is represented in false colours (NIR, Red and Green channels).

**Figure 2.**Enlargement of UAV-based multispectral imagery, represented in false colours (NIR, Red and Green channels), of: (

**a**): “Parcel-A”; (

**b**): ”Parcel-B”; and (

**c**): “Parcel-C”.

**Figure 3.**(

**a**) Ordered grid of pixels $s\left(\mathrm{i},\mathrm{j}\right)$ belonging to satellite tile $\mathcal{S}$, located at latitude and longitude coordinates ${\alpha}_{s}\left(\mathrm{i},\mathrm{j}\right)$ and ${\beta}_{s}\left(\mathrm{i},\mathrm{j}\right);$ and (

**b**) ordered grid of pixels $d\left(\mathrm{u},\mathrm{v}\right)$ belonging to satellite imagery $\mathcal{D}$, located at ${\alpha}_{d}\left(\mathrm{i},\mathrm{j}\right)$ and ${\beta}_{d}\left(\mathrm{i},\mathrm{j}\right)$. Selected UAV pixels belonging to $\mathcal{G}\left(\mathrm{i},\mathrm{j}\right)$, used for comparison to satellite pixel $s\left(\mathrm{i},\mathrm{j}\right)$, are highlighted in light green.

**Figure 4.**Comprehensive (

**a**) $NDV{I}_{\mathrm{sat}}$ map, computed from satellite imagery ${\mathcal{S}}_{2}$, and (

**b**) $NDV{I}_{\mathrm{uav}}$ derived from UAV imagery ${\mathcal{D}}_{2}$. (

**c**) Enhanced vineyard $NDV{I}_{\mathrm{vin}}$ map, processing UAV imagery ${\mathcal{D}}_{2}$ by considering only canopy pixels ${\mathcal{G}}_{\mathrm{vin}}$ and (

**d**) $NDV{I}_{\mathrm{int}}$ map considering only inter-row surface ${\mathcal{G}}_{\mathrm{int}}$. In all represented NDVI maps, only pixels (i, j) completely included within “Parcel A”, “Parcel B” and “Parcel C” boundaries are shown.

**Figure 5.**(

**a**) Enlargement of subset $\mathcal{G}\left(8,20\right)$ of UAV map ${\mathcal{D}}_{2}$, highlighted by a yellow square in Figure 1, is represented in false colours (NIR, Red and Green channels); (

**b**) classification of pixels $d\left(\mathrm{u},\mathrm{v}\right)\subset \mathcal{G}\left(8,20\right)$ into two classes: ${\mathcal{G}}_{\mathrm{vin}}$, representing vine canopies (green), and ${\mathcal{G}}_{\mathrm{int}}$, representing inter-row surfaces (brown); (

**c**) computed NDVI values of vine canopies pixels ${\mathcal{G}}_{\mathrm{vin}}$; and (

**d**) inter-row surface ${\mathcal{G}}_{\mathrm{int}}$.

**Figure 6.**Scatter plots of NDVI values from $NDV{I}_{\mathrm{sat}}$ map (x-axis) and: (

**a**) the comprehensive $NDV{I}_{\mathrm{uav}}$ map (y-axis); (b) the enhanced NDVI values of map $NDV{I}_{\mathrm{vin}}$ (y-axis); and (

**c**) the enhanced NDVI values of $NDV{I}_{\mathrm{int}}$ map (y-axis), using imagery pair data ${\mathcal{D}}_{2}/{\mathcal{S}}_{2}$. The regression model and data pair correlation coefficients are also reported.

**Figure 7.**Vineyard test site classification into three vigour classes on the basis of the observed in-field vigour assessment. Classes “L”, “M” and “H” refer to low, medium and high vigour, respectively.

**Figure 8.**Box plots representation of: (

**a**) $NDV{I}_{\mathrm{sat}}$ values derived from satellite imagery; and (

**b**) enhanced $NDV{I}_{\mathrm{vin}}$ values derived from UAV imagery, considering only canopy pixels, divided into three groups on the basis of the observed in-field vigour classes “L”, “M” and “H”.

Satellite | UAV | |||
---|---|---|---|---|

Platform | Sentinel-2 | 8-rotors custom UAV | ||

Sensors | Multispectral Imager | Parrot sequoia Multispectral camera | ||

Number of channels | 13 | 4 | ||

Band name | Range | Band name | Range | |

Spectral band details | B4-Red B8-NIR | 650–680 nm 785–900 nm | B2-Red B4-NIR | 640–680 nm 770–810 nm |

GSD per band | B4, B8 = 10 m | 5 cm | ||

Flight altitude | 786 km | 35 m | ||

Field of view | 290 km | 70.6° HFOV | ||

Image Ground Dimension | 100 km × 100 km | 64 m × 48 m | ||

Number of images to cover vineyards test site | 1 | >1000 |

Dataset Name | Acquisition Date | Data Source | Time Difference (days) |
---|---|---|---|

${\mathcal{D}}_{1}$ | 5 May 2017 | UAV | $+5$ |

${\mathcal{S}}_{1}$ | 30 April 2017 | Satellite | $-5$ |

${\mathcal{D}}_{2}$ | 29 June 2017 | UAV | $-7$ |

${\mathcal{S}}_{2}$ | 6 July 2017 | Satellite | $+7$ |

${\mathcal{D}}_{3}$ | 1 August 2017 | UAV | $-4$ |

${\mathcal{S}}_{3}$ | 5 August 2017 | Satellite | $+4$ |

${\mathcal{D}}_{4}$ | 13 September 2017 | UAV | $-4$ |

${\mathcal{S}}_{4}$ | 17 September 2017 | Satellite | $+4$ |

${\mathit{R}}_{\mathit{S}\mathit{a}\mathit{t}/\mathit{U}\mathit{A}\mathit{V}}$ | ${\mathit{R}}_{\mathit{S}\mathit{a}\mathit{t}/\mathit{v}\mathit{i}\mathit{n}}$ | ${\mathit{R}}_{\mathit{S}\mathit{a}\mathit{t}/\mathit{i}\mathit{n}\mathit{t}}$ | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Map pair | ${\mathcal{D}}_{1}/{\mathcal{S}}_{1}$ | ${\mathcal{D}}_{2}/{\mathcal{S}}_{2}$ | ${\mathcal{D}}_{3}/{\mathcal{S}}_{3}$ | ${\mathcal{D}}_{4}/{\mathcal{S}}_{4}$ | ${\mathcal{D}}_{1}/{\mathcal{S}}_{1}$ | ${\mathcal{D}}_{2}/{\mathcal{S}}_{2}$ | ${\mathcal{D}}_{3}/{\mathcal{S}}_{3}$ | ${\mathcal{D}}_{4}/{\mathcal{S}}_{4}$ | ${\mathcal{D}}_{1}/{\mathcal{S}}_{1}$ | ${\mathcal{D}}_{2}/{\mathcal{S}}_{2}$ | ${\mathcal{D}}_{3}/{\mathcal{S}}_{3}$ | |

Parcel A | 0.63 | 0.71 | 0.58 | 0.55 | 0.31 | 0.33 | 0.45 | 0.40 | 0.52 | 0.65 | 0.56 | 0.49 |

Parcel B | 0.60 | 0.68 | 0.62 | 0.65 | 0.39 | 0.40 | 0.37 | 0.38 | 0.56 | 0.61 | 0.60 | 0.62 |

Parcel C | 0.64 | 0.67 | 0.60 | 0.72 | 0.41 | 0.61 | 0.28 | 0.51 | 0.59 | 0.67 | 0.54 | 0.66 |

**Table 4.**Results of the ANOVA of UAV based $NDV{I}_{\mathrm{vin}}$ map in relation to the three vigour classes from in-field assessment.

Source | DF | SS | MS | F-Value | P-Value | |
---|---|---|---|---|---|---|

Parcel A | classes | 2 | 1.360807 | 0.680403 | 30.092543 | 5.461188 × 10^{−8} |

Error | 31 | 0.700921 | 0.022610 | |||

Total | 33 | 2.061721 | ||||

Parcel B | classes | 2 | 2.713501 | 1.356750 | 71.166427 | 6.867305 × 10^{−7} |

Error | 63 | 1.201062 | 0.019064 | |||

Total | 65 | 3.914563 | ||||

Parcel C | classes | 2 | 0.867121 | 0.433560 | 9.199357 | 0.00247 |

Error | 15 | 0.706941 | 0.047129 | |||

Total | 17 | 1.57406 |

**Table 5.**Results of the ANOVA of satellite based $NDV{I}_{\mathrm{sat}}$ map in relation to three vigour classes from in-field assessment.

Source | DF | SS | MS | F-Value | P-Value | |
---|---|---|---|---|---|---|

Parcel A | classes | 2 | 0.308368 | 0.154184 | 3.458293 | 0.044081 |

Error | 31 | 1.382101 | 0.044584 | |||

Total | 33 | 1.690464 | ||||

Parcel B | classes | 2 | 0.393805 | 0.196903 | 4.892817 | 0.010587 |

Error | 63 | 2.535323 | 0.040243 | |||

Total | 65 | 2.929128 | ||||

Parcel C | classes | 2 | 0.198502 | 0.099251 | 1.455564 | 0.264401 |

Error | 15 | 1.022811 | 0.068187 | |||

Total | 17 | 1.221313 |

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## Share and Cite

**MDPI and ACS Style**

Khaliq, A.; Comba, L.; Biglia, A.; Ricauda Aimonino, D.; Chiaberge, M.; Gay, P.
Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment. *Remote Sens.* **2019**, *11*, 436.
https://doi.org/10.3390/rs11040436

**AMA Style**

Khaliq A, Comba L, Biglia A, Ricauda Aimonino D, Chiaberge M, Gay P.
Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment. *Remote Sensing*. 2019; 11(4):436.
https://doi.org/10.3390/rs11040436

**Chicago/Turabian Style**

Khaliq, Aleem, Lorenzo Comba, Alessandro Biglia, Davide Ricauda Aimonino, Marcello Chiaberge, and Paolo Gay.
2019. "Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment" *Remote Sensing* 11, no. 4: 436.
https://doi.org/10.3390/rs11040436