Satellite Solutions for Precision Viticulture: Enhancing Sustainability and Efficiency in Vineyard Management
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. The Evolution of Satellite Missions to Assess Vineyard Conditions, Detect Pests and Diseases, and Optimize Resource Use
- However, the use of Sentinel-2 data for monitoring qualitative parameters like total soluble solids and total acidity in grape berries is limited due to the low correlation of those with NDVI [35]. As those require high precision, the conventional methods for the analysis of basic grape ripeness parameters remain essential, especially in vertically positioned training systems where satellite technologies face limitations.
3.2. Satellite Images Pre-Processing and Processing
3.3. Integration of Satellite Data with Ground-Based Measurements and Models to Achieve Accurate and Reliable Prescription Maps for Variable Rate Treatments
3.4. The Most Commonly Used Vegetative Indices from Satellite Images
3.5. Challenges in the Use of Satellite Data in Vineyards
4. Conclusions
Conclusions and Future Direction of Use of Satellite Data in Viticulture
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Cataldo, E.; Fucile, M.; Mattii, G.B. A Review: Soil Management, Sustainable Strategies and Approaches to Improve the Quality of Modern Viticulture. Agronomy 2021, 11, 2359. [Google Scholar] [CrossRef]
- Chen, M.; Brun, F.; Raynal, M.; Makowski, D. Delaying the first grapevine fungicide application reduces exposure on operators by half. Sci. Rep. 2020, 10, 6404. [Google Scholar] [CrossRef]
- Geiger, F.; Bengtsson, J.; Berendse, F.; Weisser, W.W.; Emmerson, M.; Morales, M.B.; Ceryngier, P.; Liira, J.; Tscharntke, T.; Winqvist, C.; et al. Persistent negative effects of pesticides on biodiversity and biological control potential on European farmland. Basic Appl. Ecol. 2010, 11, 97–105. [Google Scholar] [CrossRef]
- Damalas, C.A. Pesticide drift: Seeking reliable environmental indicators of exposure assessment. In Environmental Indicators; Springer: Dordrecht, The Netherland, 2015; pp. 251–261. [Google Scholar]
- Schaeffer, A.; Wijntjes, C. Changed degradation behavior of pesticides when present in mixtures. Eco-Environ. Health 2022, 1, 23–30. [Google Scholar] [CrossRef] [PubMed]
- Pascuzzi, S.; Bulgakov, V.; Santoro, F.; Anifantis, A.S.; Ivanovs, S.; Holovach, I. A study on the drift of spray droplets dipped in airflows with different directions. Sustainability 2020, 12, 4644. [Google Scholar] [CrossRef]
- EC. EU Pesticides Database—Active Substances. Available online: https://ec.europa.eu/food/plant/pesticides/eu-pesticides-database/start/screen/active-substances (accessed on 26 March 2024).
- da Graça Silva, F.V.A. Pesticide Residues in EU Soils and Related Risks. Ph.D. Thesis, Wageningen University, Wageningen, The Netherlands, 2022. [Google Scholar] [CrossRef]
- Münzel, T.; Hahad, O.; Daiber, A.; Landrigan, P.J. Soil and water pollution and human health: What should cardiologists worry about? Cardiovasc. Res. 2023, 119, 440–449. [Google Scholar] [CrossRef]
- United Nations. The Sustainable Development Agenda. Available online: https://www.un.org/sustainabledevelopment/development-agenda/ (accessed on 31 January 2024).
- FAO. The State of the World’s Land and Water Resources for Food and Agriculture—Systems at Breaking Point (SOLAW 2021); FAO: Rome, Italy, 2021. [Google Scholar]
- Silva, V.; Mol, H.G.; Zomer, P.; Tienstra, M.; Ritsema, C.J.; Geissen, V. Pesticide residues in European agricultural soils–A hidden reality unfolded. Sci. Total Environ. 2019, 653, 1532–1545. [Google Scholar] [CrossRef] [PubMed]
- Bäckstrand, K. Towards a climate-neutral union by 2050? The European green deal, climate law, and green recovery. In Routes to a Resilient European Union: Interdisciplinary European Studies; Springer International Publishing: Cham, Switzerland, 2022; pp. 39–61. [Google Scholar]
- Peng, J.; Wang, X.; Wang, H.; Li, X.; Zhang, Q.; Wang, M.; Yan, J. Advances in understanding grapevine downy mildew: From pathogen infection to disease management. Mol. Plant Pathol. 2024, 25, e13401. [Google Scholar] [CrossRef]
- Fermaud, M.; Smits, N.; Merot, A.; Roudet, J.; Thiery, D.; Wéry, J.; Delbac, L. New multipest damage indicator to assess protection strategies in grapevine cropping systems. Aust. J. Grape Wine Res. 2016, 22, 450–461. [Google Scholar] [CrossRef]
- Simonovici, M. Enquête Pratiques Phytosanitaires en Viticulture en 2016: Nombre de Traitements et Indicateurs de Fréquence de Traitement; Ministère de L’agriculture: Paris, France, 2019; p. 50. [Google Scholar]
- Puelles, M.; Arbizu-Milagro, J.; Castillo-Ruiz, F.J.; Peña, J.M. Predictive models for grape downy mildew (Plasmopara viticola) as a decision support system in Mediterranean conditions. Crop Prot. 2024, 175, 106450. [Google Scholar] [CrossRef]
- Brischetto, C.; Bove, F.; Fedele, G.; Rossi, V. A weather-driven model for predicting infections of grapevines by sporangia of Plasmopara viticola. Front. Plant Sci. 2021, 12, 636607. [Google Scholar] [CrossRef] [PubMed]
- Geppert, C.; da Cruz, M.; Alma, A.; Andretta, L.; Anfora, G.; Battaglia, D.; Burgio, G.; Caccavo, V.; Giorgia, C.S.; Cinquatti, F.; et al. Climate and landscape composition explain agronomic practices, pesticide use and grape yield in vineyards across Italy. Agric. Syst. 2024, 215, 103853. [Google Scholar] [CrossRef]
- Jarmain, C.; Avenant, E.; van Niekerk, A.; Stephenson, G.; Mohlotsane, M.; Muller, J.; Vermeulen, D.; Jordaan, H.; Prins, A.; Poblete-Echeverría, C.; et al. Water Footprint as an Indicator of Sustainable Table and Wine Grape Production; Report to the Water Research Commission (WRC); Water Research Commission: Gezina, South Africa, 2020. [Google Scholar]
- Zhao, K.; Lan, Y.; Shi, Y.; Duan, C.; Yu, K. Metabolite and transcriptome analyses reveal the effects of salinity stress on the biosynthesis of proanthocyanidins and anthocyanins in grape suspension cells. Front. Plant Sci. 2024, 15, 1351008. [Google Scholar] [CrossRef] [PubMed]
- Global Map of Salt-Affected Soils (GSASmap). Available online: https://www.fao.org/global-soil-partnership/gsasmap/en/ (accessed on 26 March 2024).
- Tassopoulos, D.; Kalivas, D.; Giovos, R.; Lougkos, N.; Priovolou, A. Sentinel-2 imagery monitoring vine growth related to topography in a protected designation of origin region. Agriculture 2021, 11, 785. [Google Scholar] [CrossRef]
- Stolarski, O.; Fraga, H.; Sousa, J.J.; Pádua, L. Synergistic Use of Sentinel-2 and UAV Multispectral Data to Improve and Optimize Viticulture Management. Drones 2022, 6, 366. [Google Scholar] [CrossRef]
- Delogu, G.; Caputi, E.; Perretta, M.; Ripa, M.N.; Boccia, L. Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support. Sustainability 2023, 15, 13786. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
- Wulder, M.A.; Loveland, T.R.; Roy, D.P.; Crawford, C.J.; Masek, J.G.; Woodcock, C.E.; Allen, R.G.; Anderson, M.C.; Belward, A.S.; Cohen, W.B.; et al. Current status of Landsat program, science, and applications. Remote Sens. Environ. 2019, 225, 127–147. [Google Scholar] [CrossRef]
- Masek, J.G.; Wulder, M.A.; Markham, B.; McCorkel, J.; Crawford, C.J.; Storey, J.; Jenstrom, D.T. Landsat 9: Empowering open science and applications through continuity. Remote Sens. Environ. 2020, 248, 111968. [Google Scholar] [CrossRef]
- Davitt, A.; Tesser, D.; Gamarro, H.; Anderson, M.; Knipper, K.; Xue, J.; Kustas, W.; Alsina, M.M.; Podest, E.; McDonald, K. The complementary uses of Sentinel-1A SAR and ECOSTRESS datasets to identify vineyard growth and conditions: A case study in Sonoma County, California. Irrig. Sci. 2022, 40, 655–681. [Google Scholar] [CrossRef]
- Devaux, N.; Crestey, T.; Leroux, C.; Tisseyre, B. Potential of Sentinel-2 satellite images to monitor vine fields grown at a territorial scale. OENO One 2019, 53, 51–58. [Google Scholar] [CrossRef]
- Irons, J.R.; Dwyer, J.L.; Barsi, J.A. The next Landsat satellite: The Landsat data continuity mission. Remote Sens. Environ. 2012, 122, 11–21. [Google Scholar] [CrossRef]
- Gerace, A.; Montanaro, M. Derivation and validation of the stray light correction algorithm for the Thermal Infrared Sensor onboard Landsat 8. Remote Sens. Environ. 2017, 191, 246–257. [Google Scholar] [CrossRef]
- Xiao, F.; Guarnieri, A.M.; Ding, Z.; Manzoni, M. Improving the Split-Spectrum Method for Sentinel-1 Differential TOPSAR Interferometry. IEEE Geosci. Remote Sens. Lett. 2022, 19, 4504605. [Google Scholar] [CrossRef]
- Mendes, M.P.; Matias, M.; Gomes, R.C.; Falcão, A.P. Delimitation of low topsoil moisture content areas in a vineyard using remote sensing imagery (Sentinel-1 and Sentinel-2) in a Mediterranean-climate region. Soil Water Res. 2021, 16, 85–94. [Google Scholar] [CrossRef]
- Di Gennaro, S.F.; Dainelli, R.; Palliotti, A.; Toscano, P.; Matese, A. Sentinel-2 validation for spatial variability assessment in overhead trellis system viticulture versus UAV and agronomic data. Remote Sens. 2019, 11, 2573. [Google Scholar] [CrossRef]
- D’Urso, G.; Bolognesi, S.F.; Kustas, W.P.; Knipper, K.R.; Anderson, M.C.; Alsina, M.M.; Belfiore, O.R. Determining evapotranspiration by using combination equation models with Sentinel-2 data and comparison with thermal-based energy balance in a California irrigated Vineyard. Remote Sens. 2021, 13, 3720. [Google Scholar] [CrossRef]
- Knipper, K.R.; Kustas, W.P.; Anderson, M.C.; Alsina, M.M.; Hain, C.R.; Alfieri, J.G.; Prueger, J.H.; Gao, F.; McKee, L.G.; Sanchez, L.A. Using High-Spatiotemporal Thermal Satellite ET Retrievals for Operational Water Use and Stress Monitoring in a California Vineyard. Remote Sens. 2019, 11, 2124. [Google Scholar] [CrossRef]
- Comparetti, A.; Marques da Silva, J.R. Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard. Sustainability 2022, 14, 1688. [Google Scholar] [CrossRef]
- Cogato, A.; Meggio, F.; Collins, C.; Marinello, F. Medium-resolution multispectral data from Sentinel-2 to assess the damage and the recovery time of late frost on vineyards. Remote Sens. 2020, 12, 1896. [Google Scholar] [CrossRef]
- Li, W.; Huang, J.; Yang, L.; Chen, Y.; Fang, Y.; Jin, H.; Huang, R. A practical remote sensing monitoring framework for late frost damage in wine grapes using multi-source satellite data. Remote Sens. 2021, 13, 3231. [Google Scholar] [CrossRef]
- Cogato, A.; Pagay, V.; Marinello, F.; Meggio, F.; Grace, P.; De Antoni Migliorati, M. Assessing the feasibility of using sentinel-2 imagery to quantify the impact of heatwaves on irrigated vineyards. Remote Sens. 2019, 11, 2869. [Google Scholar] [CrossRef]
- Brown, L.A.; Dash, J.; Lidón, A.L.; Lopez-Baeza, E.; Dransfeld, S. Synergetic exploitation of the Sentinel-2 missions for validating the Sentinel-3 ocean and land color instrument terrestrial chlorophyll index over a vineyard dominated Mediterranean environment. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 2244–2251. [Google Scholar] [CrossRef]
- Bellvert, J.; Jofre-Ĉekalović, C.; Pelechá, A.; Mata, M.; Nieto, H. Feasibility of using the two-source energy balance model (TSEB) with Sentinel-2 and Sentinel-3 images to analyze the spatio-temporal variability of vine water status in a vineyard. Remote Sens. 2020, 12, 2299. [Google Scholar] [CrossRef]
- Kang, Y.; Gao, F.; Anderson, M.; Kustas, W.; Nieto, H.; Knipper, K.; Karnieli, A. Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation. Irrig. Sci. 2022, 40, 531–551. [Google Scholar] [CrossRef] [PubMed]
- Jawak, S.D.; Luis, A.J. Improved land cover mapping using high resolution multiangle 8-band WorldView-2 satellite remote sensing data. J. Appl. Remote Sens. 2013, 7, 073573. [Google Scholar] [CrossRef]
- Wei, H.E.; Grafton, M.; Bretherton, M.; Irwin, M.; Sandoval, E. Evaluation of the use of two-stage calibrated PlanetScope images and environmental variables for the development of the grapevine water status prediction model. Technol. Agron. 2023, 3, 6. [Google Scholar] [CrossRef]
- Helman, D.; Bahat, I.; Netzer, Y.; Ben-Gal, A.; Alchanatis, V.; Peeters, A.; Cohen, Y. Using time series of high-resolution planet satellite images to monitor grapevine stem water potential in commercial vineyards. Remote Sens. 2018, 10, 1615. [Google Scholar] [CrossRef]
- Dave, C.P.; Joshi, R.; Srivastava, S.S. A survey on geometric correction of satellite imagery. Int. J. Comput. Appl. 2015, 116, 24–27. [Google Scholar]
- Ducati, J.R.; Sarate, R.E.; Fachel, J.M. Application of remote sensing techniques to discriminate between conventional and organic vineyards in the Loire Valley, France. OENO One 2014, 48, 135–144. [Google Scholar] [CrossRef]
- Berk, A.; Anderson, G.P.; Acharya, P.K.; Bernstein, L.S.; Muratov, L.; Lee, J.; Shettle, E.P. MODTRAN5: 2006 update. Algorithms Technol. Multispectral. Hyperspectral. Ultraspectral. Imag. XII 2006, 6233, 508–515. [Google Scholar]
- Tanré, D.; Deroo, C.; Duhaut, P.; Herman, M.; Morcrette, J.J.; Perbos, J.; Deschamps, P.Y. Technical note Description of a computer code to simulate the satellite signal in the solar spectrum: The 5S code. Int. J. Remote Sens. 1990, 11, 659–668. [Google Scholar] [CrossRef]
- Kotchenova, S.Y.; Vermote, E.F.; Matarrese, R.; Klemm, F.J., Jr. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance. Appl. Opt. 2006, 45, 6762–6774. [Google Scholar] [CrossRef]
- Matese, A.; Toscano, P.; Di Gennaro, S.F.; Genesio, L.; Vaccari, F.P.; Primicerio, J.; Belli, C.; Zaldei, A.; Bianconi, R.; Gioli, B. Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture. Remote Sens. 2015, 7, 2971–2990. [Google Scholar] [CrossRef]
- Sterckx, S.; Knaeps, S.; Kratzer, S.; Ruddick, K. SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters. Remote Sens. Environ. 2015, 157, 96–110. [Google Scholar] [CrossRef]
- Frantz, D.; Röder, A.; Stellmes, M.; Hill, J. An operational radiometric Landsat preprocessing framework for large-area time series applications. IEEE Trans. Geosci. Remote Sens. 2016, 54, 3928–3943. [Google Scholar] [CrossRef]
- Wilson, R.T. Py6S: A Python interface to the 6S radiative transfer model. Comput. Geosci. 2013, 51, 166–171. [Google Scholar] [CrossRef]
- Masek, J.G. Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS); NASA: Washington, DC, USA, 2006.
- Vermote, E.; Justice, C.; Claverie, M.; Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 2016, 185, 46–56. [Google Scholar] [CrossRef] [PubMed]
- Sozzi, M.; Kayad, A.; Taylor, J.A.; Tisseyre, B.; Sartori, L.; Marinello, F. Evaluation of shadow effects in satellite images of vineyards with different row orientation. In Proceedings of the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Portici, Italy, 24–26 October 2019; pp. 350–354. [Google Scholar]
- Nazeer, M.; Nichol, J.E.; Yung, Y.K. Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment. Int. J. Remote Sens. 2014, 35, 6271–6291. [Google Scholar] [CrossRef]
- Frantz, D. FORCE—Landsat+ Sentinel-2 analysis ready data and beyond. Remote Sens. 2019, 11, 1124. [Google Scholar] [CrossRef]
- Guo, J.; Yang, X.; Niu, J.; Jin, Y.; Xu, B.; Shen, G.; Zhang, Y. Remote sensing monitoring of green-up dates in the Xilingol grasslands of northern China and their correlations with meteorological factors. Int. J. Remote Sens. 2019, 40, 2190–2211. [Google Scholar] [CrossRef]
- Pauletto, H.; Hoff, R.; Junges, A.H.; Valenti, E.D.S.; Alberti, R. Orbital images for spatial and temporal characterization of vineyard by vegetation index in the “campanha gaúcha” wine region, Brazil. Braz. J. Agric. 2022, 97, 86–116. [Google Scholar] [CrossRef]
- Brook, A.; De Micco, V.; Battipaglia, G.; Erbaggio, A.; Ludeno, G.; Catapano, I.; Bonfante, A. A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard. Remote Sens. Environ. 2020, 240, 111679. [Google Scholar] [CrossRef]
- Park, H.; Choi, J.; Park, N.; Choi, S. Sharpening the VNIR and SWIR Bands of Sentinel-2A Imagery through Modified Selected and Synthesized Band Schemes. Remote Sens. 2017, 9, 1080. [Google Scholar] [CrossRef]
- Main-Knorn, M.; Pflug, B.; Louis, J.; Debaecker, V.; Müller-Wilm, U.; Gascon, F. Sen2Cor for sentinel-2. Image Signal Process. Remote Sens. XXIII 2017, 10427, 37–48. [Google Scholar]
- Zhao, L.; Li, Q.; Zhang, Y.; Wang, H.; Du, X. Integrating the Continuous Wavelet Transform and a Convolutional Neural Network to Identify Vineyard Using Time Series Satellite Images. Remote Sens. 2019, 11, 2641. [Google Scholar] [CrossRef]
- Del Frate, F.; Latini, D.; Picchiani, M.; Schiavon, G.; Vittucci, C. A neural network architecture combining VHR SAR and multispectral data for precision farming in viticulture. In Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014; pp. 1508–1511. [Google Scholar]
- Meyers, J.M.; Dokoozlian, N.; Ryan, C.; Bioni, C.; Vanden Heuvel, J.E. A new, satellite NDVI-based sampling protocol for grape maturation monitoring. Remote Sens. 2020, 12, 1159. [Google Scholar] [CrossRef]
- Palazzi, F.; Biddoccu, M.; Borgogno Mondino, E.C.; Cavallo, E. Use of Remotely Sensed Data for the Evaluation of Inter-Row Cover Intensity in Vineyards. Remote Sens. 2023, 15, 41. [Google Scholar] [CrossRef]
- Cunha, M.; Marçal, A.R.; Rodrigues, A. A comparative study of satellite and ground-based vineyard phenology. In Proceedings of the 29th EARSeL Symposium, Chania, Greece, 15–18 June 2009; pp. 68–77. [Google Scholar]
- Campos, J.; Rigotti, J.I.R.; Baptista, E.A.; Monteiro, A.M.V.; Reis, I.A. Population estimates from orbital data of medium spatial resolution: Applications for a Brazilian municipality. Sustainability 2020, 12, 3565. [Google Scholar] [CrossRef]
- Borgogno-Mondino, E.; Novello, V.; Lessio, A.; de Palma, L. Describing the spatio-temporal variability of vines and soil by satellite-based spectral indices: A case study in Apulia (South Italy). Int. J. Appl. Earth Obs. Geoinf. 2018, 68, 42–50. [Google Scholar] [CrossRef]
- Campos, J.; García-Ruíz, F.; Gil, E. Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery. Sensors 2021, 21, 2363. [Google Scholar] [CrossRef] [PubMed]
- Bolognini, M.; Modina, D.; Bianchi, D.; Cavallaro, L.; Carnevali, P.; Brancadoro, L. Using a vegetation index to define homogeneous zones for variable rate irrigation in vineyard. In Precision agriculture’23; Wageningen Academic: Wageningen, The Netherlands, 2023; pp. 75–82. [Google Scholar]
- Campos, J.; Gallart, M.; Llop, J.; Ortega, P.; Salcedo, R.; Gil, E. On-Farm Evaluation of Prescription Map-Based Variable Rate Application of Pesticides in Vineyards. Agronomy 2020, 10, 102. [Google Scholar] [CrossRef]
- Llorens, J.; Gil, E.; Llop, J.; Escolà, A. Variable rate dosing in precision viticulture: Use of electronic devices to improve application efficiency. Crop Prot. 2010, 29, 239–248. [Google Scholar] [CrossRef]
- Gao, B.C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- Borgogno-Mondino, E.; Lessio, A.; Tarricone, L.; Novello, V.; de Palma, L. A comparison between multispectral aerial and satellite imagery in precision viticulture. Precis. Agric. 2018, 19, 195–217. [Google Scholar] [CrossRef]
- Gu, Y.; Brown, J.F.; Verdin, J.P.; Wardlow, B. A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophys. Res. Lett. 2007, 34, L06407. [Google Scholar] [CrossRef]
- Del Rio, M.S.; Cicuéndez, V.; Yagüe, C. Characterization of two vineyards in Mexico based on Sentinel-2 and meteorological data. Remote Sens. 2024, 16, 2538. [Google Scholar] [CrossRef]
- Grant, B.G. UAV imagery analysis: Challenges and opportunities. Long-Range Imaging II 2017, 10204, 28–33. [Google Scholar]
- Arab, S.T.; Noguchi, R.; Matsushita, S.; Ahamed, T. Prediction of grape yields from time-series vegetation indices using satellite remote sensing and a machine-learning approach. Remote Sens. Appl. Soc. Environ. 2021, 22, 100485. [Google Scholar] [CrossRef]
- Dobrowski, S.Z.; Ustin, S.L.; Wolpert, J.A. Remote estimation of vine canopy density in vertically shoot-positioned vineyards: Determining optimal vegetation indices. Aust. J. Grape Wine Res. 2002, 8, 117–125. [Google Scholar] [CrossRef]
- Abubakar, M.A.; Chanzy, A.; Flamain, F.; Courault, D. Characterisation of grapevine canopy leaf area and inter-row management using Sentinel-2 time series. OENO One 2023, 57, 1–13. [Google Scholar] [CrossRef]
- Tisseyre, B.; Ojeda, H.; Taylor, J. New technologies and methodologies for site-specific viticulture. J. Int. Sci. Vigne Vin 2007, 41, 63–76. [Google Scholar] [CrossRef]
- Sousa, J.J.; Toscano, P.; Matese, A.; Di Gennaro, S.F.; Berton, A.; Gatti, M.; Peres, E. UAV-Based Hyperspectral Monitoring Using Push-Broom and Snapshot Sensors: A Multisite Assessment for Precision Viticulture Applications. Sensors 2022, 22, 6574. [Google Scholar] [CrossRef] [PubMed]
- Sams, B.; Bramley, R.G.; Sanchez, L.; Dokoozlian, N.; Ford, C.; Pagay, V. Remote sensing, yield, physical characteristics, and fruit composition variability in Cabernet Sauvignon vineyards. Am. J. Enol. Vitic. 2022, 73, 93–105. [Google Scholar] [CrossRef]
- Picard, R.R.; Cook, R.D. Cross-Validation of Regression Models. J. Am. Stat. Assoc. 1984, 79, 575–583. [Google Scholar] [CrossRef]
- Knipper, K.R.; Kustas, W.P.; Anderson, M.C.; Nieto, H.; Alfieri, J.G.; Prueger, J.H.; Sanchez, L. Using high-spatiotemporal thermal satellite ET retrievals to monitor water use over California vineyards of different climate, vine variety and trellis design. Agric. Water Manag. 2020, 241, 106361. [Google Scholar] [CrossRef]
- Laroche-Pinel, E.; Duthoit, S.; Albughdadi, M.; Costard, A.D.; Rousseau, J.; Chéret, V.; Clenet, H. Towards vine water status monitoring on a large scale using Sentinel-2 images. Remote Sens. 2021, 13, 1837. [Google Scholar] [CrossRef]
- Lopez-Fornieles, E.; Brunel, G.; Devaux, N.; Roger, J.M.; Tisseyre, B. Is it possible to assess heatwave impact on grapevines at the regional level with time series of satellite images? Agronomy 2022, 12, 563. [Google Scholar] [CrossRef]
Index (Acronym) | Formula Equation | Application and Advantages | Limits | References | Spectral Region |
---|---|---|---|---|---|
Normalized Difference Vegetation Index (NDVI) | Simple, widely used, indicator of vegetation health, vigor, and stress | Saturation in high biomass | Tassopoulos et al. [23]; Stolarski et al. [24]; Di Gennaro et al. [35]; Comparetti et al. [38] | NIR, Red | |
Normalized Difference Water Index (NDWI) | Indicator of water content and changes in vines | Affected by soil moisture | Comparetti et al. [38]; Palazzi et al. [70]; Borgogno-Mondino et al. [73] | NIR, SWIR | |
Green Normalized Difference Vegetation Index (GNDVI) | Chlorophyll and nitrogen content | Non-vegetation factors | Tassopoulos et al. [23]; Cogato et al. [39]; Helman et al. [47]; Brook et al. [64] | NIR, Green | |
Enhanced Vegetation Index (EVI) | Reduces background noise (atmosphere, canopy), for vegetation health and phenology | More complex, requires additional parameters (Blue band) | Tassopoulos et al. [23]; Cogato et al. [39]; Cogato et al. [41]; Helman et al. [47] | NIR, Red, Blue | |
Soil-Adjusted Vegetation Index (SAVI) | × (1 + L) | Reduces the soil brightness; for heat stress, (semi)-arid conditions | Requires adjustment factor L | Cogato et al. [41]; Helman et al. [47] | NIR, Red |
Modified Soil-Adjusted Vegetation Index (MSAVI) | Reduces soil brightness; for (semi)-arid (sparse soil) | Requires adjustment factor L | Tassopoulos et al. [23]; Del-Rio et al. [81] | NIR, Red | |
Chlorophyll Absorption in Reflectance Index (CARI) | Chlorophyll content | Complex calculation | Cogato et al. [39] | Green, Red, Red-Edge |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ana, M.; Damir, M.; Antonio, M.-Š.; Marin, Č. Satellite Solutions for Precision Viticulture: Enhancing Sustainability and Efficiency in Vineyard Management. Agronomy 2024, 14, 1862. https://doi.org/10.3390/agronomy14081862
Ana M, Damir M, Antonio M-Š, Marin Č. Satellite Solutions for Precision Viticulture: Enhancing Sustainability and Efficiency in Vineyard Management. Agronomy. 2024; 14(8):1862. https://doi.org/10.3390/agronomy14081862
Chicago/Turabian StyleAna, Mucalo, Matić Damir, Morić-Španić Antonio, and Čagalj Marin. 2024. "Satellite Solutions for Precision Viticulture: Enhancing Sustainability and Efficiency in Vineyard Management" Agronomy 14, no. 8: 1862. https://doi.org/10.3390/agronomy14081862