Open Access
| Issue |
BIO Web Conf.
Volume 68, 2023
44th World Congress of Vine and Wine
|
|
|---|---|---|
| Article Number | 01022 | |
| Number of page(s) | 9 | |
| Section | Viticulture | |
| DOI | https://doi.org/10.1051/bioconf/20236801022 | |
| Published online | 06 December 2023 | |
- Matese et al., Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sensing 7(3), 2971–2990 (2015) [Google Scholar]
- A. Khaliq, et al., Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment, Remote Sensing 11(4), 436 (2019) https://doi.org/10.3390/rs11040436 [CrossRef] [Google Scholar]
- L. Pastonchi, et al., Comparison between satellite and ground data with UAV-based information to analyse vineyard spatio-temporal variability, Oeno One 54, 4 (2020), https://doi.org/10.20870/oeno-one.2020.54.4.4028 [Google Scholar]
- M. Sozzi, et al., Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform, Oeno One 54, 2 (2020) https://doi.org/10.20870/oeno-one.2020.54.1.2557 [Google Scholar]
- K. R. Knipper, et al., Using High-Spatiotemporal Thermal Satellite ET Retrievals for Operational Water Use and Stress Monitoring in a California Vineyard, Remote Sensing 11(18), 2124 (2019) https://www.mdpi.com/2072-4292/11/18/2124?type=check_update&version=2 [CrossRef] [Google Scholar]
- V. Garcia-Gutiérrez, et al., Evaluation of Penman-Monteith Model Based on Sentinel-2 Data for the Estimation of Actual Evapotranspiration in Vineyards, Remote Sensing 13(3), 478 (2021) https://www.mdpi.com/2072-4292/13/3/478?type=check_update&version=4 [CrossRef] [Google Scholar]
- M. Kalua, et al., Remote Sensing of Vineyard Evapotranspiration Quantifies Spatiotemporal Uncertainty in Satellite-Borne ET Estimates, Remote Sensing 12(19), 3251 (2020) https://www.mdpi.com/2072-4292/12/19/3251?type=check_update&version=1 [CrossRef] [Google Scholar]
- E. Laroche-Pinel, et al., Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images, Remote Sensing 13(9), 1837 (2021) https://www.mdpi.com/2072-|4292/13/9/1837?type=check_update&version=1 [CrossRef] [Google Scholar]
- João Araújo, et al., Innovation co-development for viticulture and enology: novel tele-detection web-service fuses vineyard data, BIO Web Conf. 56, 43rd World Congress of Vine and Wine, 2023 https://www.bio-conferences.org/articles/bioconf/full_html/2023/01/bioconf_oiv2022_01006/bioconf_oiv2022_01006.html [Google Scholar]
- J.B. MacQueen, Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press 1, 281-297 MR 0214227. Zbl 0214.46201. Retrieved 2009-04-07. [Google Scholar]
- M. Kazmierski, et al., Temporal stability of within-field patterns of NDVI in non-irrigated Mediterranean vineyards Oeno One 45(2), 61–73 (2011) [CrossRef] [Google Scholar]
- Georgios D. Evangelidis, Emmanouil Z. Psarakis, Parametric image alignment using enhanced correlation coefficient maximization Pattern Analysis and Machine Intelligence, IEEE Transactions on 30(10), 1858–1865 (2008) [CrossRef] [PubMed] [Google Scholar]
- Cohen, Y., et al. Can time series of multispectral satellite images be used to estimate stem water potential in vineyards? Precision agriculture’19. Wageningen Academic Publishers, 2019. 1-5. https://doi.org/10.3920/978-90-8686-888-9_55 [Google Scholar]
- Tang, Zhehan, et al. Vine water status mapping with multispectral UAV imagery and machine learning Irrigation Science 40, 4-5 (2022) https://doi.org/10.1007/s00271-022-00788-w [Google Scholar]
- Romero, Maria, et al. Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management Computers and electronics in agriculture 147, 109-117 (2018) https://doi.org/10.1016/j.compag.2018.02.013 [CrossRef] [Google Scholar]
- Poblete, Tomas, et al. Arti7ficial neural network to predict vine water status spatial variability using multispectral information obtained from an unmanned aerial vehicle (UAV) Sensors 17(11), 2488 (2017) https://doi.org/10.3390/s17112488 [CrossRef] [PubMed] [Google Scholar]
- Cogato, Alessia, et al. Evaluating the Spectral and Physiological Responses of Grapevines (Vitis vinifera L.) to Heat and Water Stresses under Different Vineyard Cooling and Irrigation Strategies 11(10), 1940 (2012) https://doi.org/10.3390/agronomy11101940 [Google Scholar]
- Tosin, R., et al. Estimation of grapevine predawn leaf water potential based on hyperspectral reflectance data in Douro wine region. Vitis 278, 585, 28-77 (2020) https://doi.org/10.5073/vitis.2020.59.9-18 [Google Scholar]
- Delval, Louis, et al. Quantification of intra-plot variability of vine water status using Sentinel-2: case study of two Belgian vineyards EGU General Assembly Conference Abstracts 2022, https://doi.org/10.5194/egusphere-egu22-3908 [Google Scholar]
- Giovos, Rigas, et al. Remote sensing vegetation indices in viticulture: A critical review Agriculture 11.5, 547 (2021) https://www.mdpi.com/2077-0472/11/5/457 [Google Scholar]
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