Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform
Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and vari...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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International Viticulture and Enology Society
2020-04-01
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| Series: | OENO One |
| Subjects: | |
| Online Access: | https://oeno-one.eu/article/view/2557 |
| _version_ | 1818876681441509376 |
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| author | Marco Sozzi Ahmed Kayad Francesco Marinello James Taylor Bruno Tisseyre |
| author_facet | Marco Sozzi Ahmed Kayad Francesco Marinello James Taylor Bruno Tisseyre |
| author_sort | Marco Sozzi |
| collection | DOAJ |
| description | Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index.
Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated.
Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index.
Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics. |
| first_indexed | 2024-12-19T13:46:15Z |
| format | Article |
| id | doaj.art-4aba322232364de7ba8665a3a5f8b40c |
| institution | Directory Open Access Journal |
| issn | 2494-1271 |
| language | English |
| last_indexed | 2024-12-19T13:46:15Z |
| publishDate | 2020-04-01 |
| publisher | International Viticulture and Enology Society |
| record_format | Article |
| series | OENO One |
| spelling | doaj.art-4aba322232364de7ba8665a3a5f8b40c2022-12-21T20:18:51ZengInternational Viticulture and Enology SocietyOENO One2494-12712020-04-0154210.20870/oeno-one.2020.54.1.2557Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platformMarco Sozzi0Ahmed Kayad1Francesco Marinello2James Taylor3Bruno Tisseyre4University of Padova, Dept. LEAF, Viale dell'Università 16, Legnaro (PD)University of Padova, Dept. LEAF, Viale dell'Università 16, Legnaro (PD)University of Padova, Dept. LEAF, Viale dell'Università 16, Legnaro (PD) and 2NEOS srl, Spin-off of the University of Padova, Piazzetta Modin, 12, Padova (PD)ITAP, IRSTEA, Montpellier SupAgro, University of Montpellier, Bât. 21, 2 Pl. Pierre Viala, 34060 MontpellierITAP, IRSTEA, Montpellier SupAgro, University of Montpellier, Bât. 21, 2 Pl. Pierre Viala, 34060 MontpellierAim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index. Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated. Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index. Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics.https://oeno-one.eu/article/view/2557Precision ViticultureRemote SensingSpatial CorrelationVineyard Segmentation |
| spellingShingle | Marco Sozzi Ahmed Kayad Francesco Marinello James Taylor Bruno Tisseyre Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform OENO One Precision Viticulture Remote Sensing Spatial Correlation Vineyard Segmentation |
| title | Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform |
| title_full | Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform |
| title_fullStr | Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform |
| title_full_unstemmed | Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform |
| title_short | Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform |
| title_sort | comparing vineyard imagery acquired from sentinel 2 and unmanned aerial vehicle uav platform |
| topic | Precision Viticulture Remote Sensing Spatial Correlation Vineyard Segmentation |
| url | https://oeno-one.eu/article/view/2557 |
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