[1]

Ojeda H, Andary C, Kraeva E, Carbonneau A, Deloire A. 2002. Influence of pre-and post-veraison water deficit on synthesis and concentration of skin phenolic compounds during berry growth of Vitis vinifera cv. Shiraz. American Journal of Enology and Viticulture 53:261−7

[2]

van Leeuwen C, Trégoat O, Choné X, Bois B, Pernet D, Gaudillère J-P. 2009. Vine water status is a key factor in grape ripening and vintage quality for red Bordeaux wine. How can it be assessed for vineyard management purposes? OENO One 43:121−34

doi: 10.20870/oeno-one.2009.43.3.798
[3]

Martínez-Lüscher J, Sánchez-Díaz M, Delrot S, Aguirreolea J, Pascual I, et al. 2014. Ultraviolet-B radiation and water deficit interact to alter flavonol and anthocyanin profiles in grapevine berries through transcriptomic regulation. Plant and Cell Physiology 55:1925−36

doi: 10.1093/pcp/pcu121
[4]

Chaves M, Zarrouk O, Francisco R, Costa J, Santos T, et al. 2010. Grapevine under deficit irrigation: hints from physiological and molecular data. Annals of Botany 105:661−76

doi: 10.1093/aob/mcq030
[5]

Brillante L, Martínez-Luscher J, Yu R, Plank CM, Sanchez L, et al. 2017. Assessing spatial variability of grape skin flavonoids at the vineyard scale based on plant water status mapping. Journal of Agricultural and Food Chemistry 65:5255−65

doi: 10.1021/acs.jafc.7b01749
[6]

Bramley R, Ouzman J, Boss PK. 2011. Variation in vine vigour, grape yield and vineyard soils and topography as indicators of variation in the chemical composition of grapes, wine and wine sensory attributes. Australian Journal of Grape and Wine Research 17:217−29

doi: 10.1111/j.1755-0238.2011.00136.x
[7]

Arnó J, Martínez Casasnovas JA, Ribes Dasi M, Rosell JR. 2009. Precision viticulture. Research topics, challenges and opportunities in site-specific vineyard management. Spanish Journal of Agricultural Research 7(4):779−90

doi: 10.5424/sjar/2009074-1092
[8]

Monaghan JM, Daccache A, Vickers LH, Hess TM, Weatherhead EK, et al. 2013. More 'crop per drop': constraints and opportunities for precision irrigation in European agriculture. Journal of the Science of Food and Agriculture 93:977−80

doi: 10.1002/jsfa.6051
[9]

Matese A, Toscano P, Di Gennaro S, Genesio L, Vaccari F, et al. 2015. Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sensing 7:2971−90

doi: 10.3390/rs70302971
[10]

Borgogno-Mondino E, Lessio A, Tarricone L, Novello V, De Palma L. 2018. A comparison between multispectral aerial and satellite imagery in precision viticulture. Precision Agriculture 19:195−217

doi: 10.1007/s11119-017-9510-0
[11]

Khaliq A, Comba L, Biglia A, Ricauda Aimonino D, Chiaberge M, et al. 2019. Comparison of satellite and UAV-based multispectral imagery for vineyard variability assessment. Remote Sensing 11:436

doi: 10.3390/rs11040436
[12]

Helman D, Bahat I, Netzer Y, Ben-Gal A, Alchanatis V, et al. 2018. Using time series of high-resolution planet satellite images to monitor grapevine stem water potential in commercial vineyards. Remote Sensing 10:1615

doi: 10.3390/rs10101615
[13]

Pla M, Bota G, Duane A, Balagué J, Curcó A, et al. 2019. Calibrating Sentinel-2 imagery with multispectral UAV derived information to quantify damages in Mediterranean rice crops caused by Western Swamphen (Porphyrio porphyrio). Drones 3:45

doi: 10.3390/drones3020045
[14]

Revill A, Florence A, MacArthur A, Hoad S, Rees R, et al. 2020. Quantifying uncertainty and bridging the scaling gap in the retrieval of leaf area index by coupling Sentinel-2 and UAV observations. Remote Sensing 12:1843

doi: 10.3390/rs12111843
[15]

Bukowiecki J, Rose T, Kage H. 2021. Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment Sensors 21:2861

doi: 10.3390/s21082861
[16]

Mazzia V, Comba L, Khaliq A, Chiaberge M, Gay P. 2020. UAV and machine learning based refinement of a satellite-driven vegetation index for precision agriculture. Sensors 20:2530

doi: 10.3390/s20092530
[17]

Gautam D, Pagay V. 2020. A review of current and potential applications of remote sensing to study the water status of horticultural crops. Agronomy 10:140

doi: 10.3390/agronomy10010140
[18]

Brook A, De Micco V, Battipaglia G, Erbaggio A, Ludeno G, et al. 2020. A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard. Remote Sensing of Environment 240:111679

doi: 10.1016/j.rse.2020.111679
[19]

Devaux N, Crestey T, Leroux C, Tisseyre B. 2019. Potential of Sentinel-2 satellite images to monitor vine fields grown at a territorial scale. OENO One 53:52−59

doi: 10.20870/oeno-one.2019.53.1.2293
[20]

van Leeuwen C, Goutouly JP, Costa-Ferreira AM, Azaïs C, Marguerit E, et al. 2006. Intra-block variations of vine water status in time and space. VIth International Terroir Congress 2006 6 to 7 July 2006 at Mas Saporta in Montpellier. pp. 64−69

[21]

Jasse A, Berry A, Aleixandre-Tudo JL, Poblete-Echeverría C. 2021. Intra-block spatial and temporal variability of plant water status and its effect on grape and wine parameters. Agricultural Water Management 246:106696

doi: 10.1016/j.agwat.2020.106696
[22]

Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. Rome: FAO. 300: D05109

[23]

Bellvert J, Marsal J, Mata M, Girona J. 2012. Identifying irrigation zones across a 7.5-ha ‘Pinot noir’vineyard based on the variability of vine water status and multispectral images. Irrigation Science 30:499−509

doi: 10.1007/s00271-012-0380-y
[24]

Ohana-Levi N, Munitz S, Ben-Gal A, Netzer Y. 2020. Evaluation of within-season grapevine evapotranspiration patterns and drivers using generalized additive models. Agricultural Water Management 228:105808

doi: 10.1016/j.agwat.2019.105808
[25]

Suter B, Triolo R, Pernet D, Dai Z, Van Leeuwen C. 2019. Modelling stem water potential by separating the effects of soil water availability and climatic conditions on water status in grapevine (Vitis vinifera L.). Frontiers in Plant Science 10:1485

doi: 10.3389/fpls.2019.01485
[26]

Kuhn M, Johnson K. 2013. Applied predictive modeling. New York: Springer. https://doi.org/10.1007/978-1-4614-6849-3

[27]

Borgogno-Mondino E, Novello V, Lessio A, de Palma L. 2018. Describing the spatio-temporal variability of vines and soil by satellite-based spectral indices: A case study in Apulia (South Italy). International Journal of Applied Earth Observation and Geoinformation 68:42−50

doi: 10.1016/j.jag.2018.01.013
[28]

Matese A, Baraldi R, Berton A, Cesaraccio C, Di Gennaro SF, et al. 2018. Estimation of water stress in grapevines using proximal and remote sensing methods. Remote Sensing 10:114

doi: 10.3390/rs10010114
[29]

Baluja J, Diago MP, Balda P, Zorer R, Meggio F, et al. 2012. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrigation Science 30:511−22

doi: 10.1007/s00271-012-0382-9
[30]

Espinoza CZ, Khot LR, Sankaran S, Jacoby PW. 2017. High resolution multispectral and thermal remote sensing-based water stress assessment in subsurface irrigated grapevines. Remote Sensing 9:961

doi: 10.3390/rs9090961
[31]

Poblete T, Ortega-Farías S, Moreno MA, Bardeen M. 2017. Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV). Sensors 17:2488

doi: 10.3390/s17112488
[32]

Romero M, Luo Y, Su B, Fuentes S. 2018. Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management. Computers and Electronics in Agriculture 147:109−17

doi: 10.1016/j.compag.2018.02.013
[33]

Wei HE, Grafton M, Bretherton M, Irwin M, Sandoval E. 2021. Evaluation of point hyperspectral reflectance and multivariate regression models for grapevine water status estimation. Remote Sensing 13:3198

doi: 10.3390/rs13163198
[34]

Wei H-E, Grafton M, Bretherton M, Irwin M, Sandoval E. 2022. Evaluation of the use of UAV-derived vegetation indices and environmental variables for grapevine water status monitoring based on machine learning algorithms and SHAP analysis. Remote Sensing 14:5918

doi: 10.3390/rs14235918
[35]

Fernandes-Silva A, Oliveira M, Paço TA, Ferreira I. 2019. Deficit irrigation in Mediterranean fruit trees and grapevines: Water stress indicators and crop responses. In Irrigation in Agroecosystems, ed. Ondrašek G. London, UK: IntechOpen. httpgs://doi.org/10.5772/intechopen.80365

[36]

Schreiner RP, Lee J. 2014. Effects of post-véraison water deficit on 'Pinot noir' yield and nutrient status in leaves, clusters, and musts. HortScience 49:1335−40

doi: 10.21273/HORTSCI.49.10.1335
[37]

Patakas A, Noitsakis B, Chouzouri A. 2005. Optimization of irrigation water use in grapevines using the relationship between transpiration and plant water status. Agriculture, Ecosystems & Environment 106:253−59

doi: 10.1016/j.agee.2004.10.013
[38]

Giovos R, Tassopoulos D, Kalivas D, Lougkos N, Priovolou A. 2021. Remote sensing vegetation indices in viticulture: A critical review. Agriculture 11:457

doi: 10.3390/agriculture11050457
[39]

Gitelson AA, Viña A, Ciganda V, Rundquist DC, Arkebauer TJ. 2005. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32:L08403

doi: 10.1029/2005GL022688
[40]

Tucker CJ. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of environment 8:127−50

doi: 10.1016/0034-4257(79)90013-0
[41]

Huete A, Didan K, Miura T, Rodriguez EP, Gao X, et al. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83:195−213

doi: 10.1016/S0034-4257(02)00096-2
[42]

Woebbecke DM, Meyer GE, Von Bargen K, Mortensen DA. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE 38:259−69

doi: 10.13031/2013.27838
[43]

Gitelson AA, Merzlyak MN. 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research 22:689−92

doi: 10.1016/S0273-1177(97)01133-2
[44]

Daughtry CS, Walthall C, Kim M, de Colstoun EB, McMurtrey III JE. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote sensing of Environment 74:229−39

doi: 10.1016/S0034-4257(00)00113-9
[45]

Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S. 1994. A modified soil adjusted vegetation index. Remote sensing of environment 48:119−26

doi: 10.1016/0034-4257(94)90134-1
[46]

Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90:337−52

doi: 10.1016/j.rse.2003.12.013
[47]

Barnes EM, Clarke TR, Richards SE, Colaizzi PD, Haberland J, et al. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proc. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, 2000, 1619. Madison, WI, USA: ASA-CSSA-SSSA

[48]

Rouse J, Haas R, Schell J, Deering D, Harlan J. 1974. Monitoring the vernal advancements and retrogradation. Report. Texas Agricultural and Mechanical University, Texas

[49]

Rondeaux G, Steven M, Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 55:95−107

doi: 10.1016/0034-4257(95)00186-7
[50]

Gamon JA, Surfus JS. 1999. Assessing leaf pigment content and activity with a reflectometer. The New Phytologist 143:105−17

doi: 10.1046/j.1469-8137.1999.00424.x
[51]

Birth GS, McVey GR. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal 60:640−43

doi: 10.2134/agronj1968.00021962006000060016x
[52]

Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextraze L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81:416−26

doi: 10.1016/S0034-4257(02)00018-4
[53]

Gitelson AA, Kaufman YJ, Stark R, Rundquist D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 80:76−87

doi: 10.1016/S0034-4257(01)00289-9
[54]

Planet Team. 2017. Planet application program interface: In space for life on earth. San Francisco, CA

[55]

Leach N, Coops NC, Obrknezev N. 2019. Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies. Computers and Electronics in Agriculture 164:104893

doi: 10.1016/j.compag.2019.104893
[56]

Houborg R, McCabe MF. 2018. Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data. Remote Sensing 10:890

doi: 10.3390/rs10060890
[57]

Cook PG, Williams BG. 1998. Electromagnetic Induction Techniques - Part 8. Australia: CSIRO Publishing. https://doi.org/10.1071/9780643105409

[58]

Yu R, Brillante L, Martínez-Lüscher J, Kurtural SK. 2020. Spatial variability of soil and plant water status and their cascading effects on grapevine physiology are linked to berry and wine chemistry. Frontiers in Plant Science 11:790

doi: 10.3389/fpls.2020.00790
[59]

Penman HL. 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences 193:120−45 www.jstor.org/stable/98151

[60]

Reynolds AG, Willwerth JJ. 2020. Spatial variability in Ontario Riesling Vineyards: I. Soil, vine water status and vine performance. Oeno One 54:311−33

doi: 10.20870/oeno-one.2020.54.2.2401
[61]

Kazmierski M, Glémas P, Rousseau J, Tisseyre B. 2011. Temporal stability of within-field patterns of NDVI in non irrigated Mediterranean vineyards. OENO One 45:61−73

doi: 10.20870/oeno-one.2011.45.2.1488
[62]

Heil K, Schmidhalter U. 2017. The application of EM38: Determination of soil parameters, selection of soil sampling points and use in agriculture and archaeology. Sensors 17:2540

doi: 10.3390/s17112540
[63]

Bellvert J, Zarco-Tejada PJ, Marsal J, Girona J, González-Dugo V, et al. 2016. Vineyard irrigation scheduling based on airborne thermal imagery and water potential thresholds. Australian Journal of Grape and Wine Research 22:307−15

doi: 10.1111/ajgw.12173
[64]

Lever J, Krzywinski M, Altman N. 2016. Model selection and overfitting. Nature Methods 13:703−5

doi: 10.1038/nmeth.3968
[65]

Zhan Y, Luo Y, Deng X, Zhang K, Zhang M, et al. 2018. Satellite-based estimates of daily NO2 exposure in China using hybrid random forest and spatiotemporal kriging model. Environmental Science & Technology 52:4180−89

doi: 10.1021/acs.est.7b05669
[66]

Montoro A, López Urrea R, Mañas F, López Fuster P, Fereres E. 2006. Evapotranspiration of grapevines measured by a weighing lysimeter in La Mancha, Spain. Acta Horticulturae 792:459−66

doi: 10.17660/ActaHortic.2008.792.53
[67]

Acevedo-Opazo C, Tisseyre B, Guillaume S, Ojeda H. 2008. The potential of high spatial resolution information to define within-vineyard zones related to vine water status. Precision Agriculture 9:285−302

doi: 10.1007/s11119-008-9073-1