Field heterogeneity of soil texture controls leaf water potential spatial distribution predicted from UAS-based vegetation indices in non-irrigated vineyards
[en] Grapevine water status exhibits substantial variability even within a single vineyard. Understanding how edaphic, topographic, and climatic conditions impact grapevine water status heterogeneity at the field scale, in nonirrigated vineyards, is essential for winemakers as it significantly influences wine quality. This study aimed to quantify the spatial distribution of grapevine leaf water potential (leaf) within vineyards and to assess the influence of soil property heterogeneity, topography, and climatic conditions on intra-field variability in two non-irrigated vineyards during two viticultural seasons. By combining multilinear vegetation indices from very-high-spatial-resolution multispectral, thermal, and lidar imageries collected with uncrewed aerial systems (UASs), we efficiently and robustly captured the spatial distribution of leaf across both vineyards on different dates. Our results demonstrated that in non-irrigated vineyards, the spatial distribution of leaf was mainly governed by the within-vineyard soil hydraulic conductivity heterogeneity (R 2 up to 0.81) and was particularly marked when the evaporative demand and the soil water deficit increased, since the range of leaf was greater, up to 0.73 MPa, in these conditions. However, topographic attributes (elevation and slope) were less related to grapevine leaf variability. These findings show that the soil property within-field spatial distribution and climatic conditions are the primary factors governing leaf heterogeneity observed in non-irrigated vineyards, and their effects are concomitant.
Delval, Louis; Earth and Life Institute, Environmental Sciences, UCLouvain, Louvain-la-Neuve, Belgium
Bates, Jordan ; Université de Liège - ULiège > Département de géographie > Earth Observation and Ecosystem Modelling (EOSystM Lab) ; Agrosphere IBG-3, Forschungszentrum Jülich GmbH, Jülich, Germany
Jonard, François ; Université de Liège - ULiège > Département de géographie ; Université de Liège - ULiège > Sphères ; Université de Liège - ULiège > Département de géographie > Earth Observation and Ecosystem Modelling (EOSystM Lab)
Javaux, Mathieu; Earth and Life Institute, Environmental Sciences, UCLouvain, Louvain-la-Neuve, Belgium ; Agrosphere IBG-3, Forschungszentrum Jülich GmbH, Jülich, Germany
Language :
English
Title :
Field heterogeneity of soil texture controls leaf water potential spatial distribution predicted from UAS-based vegetation indices in non-irrigated vineyards
Acevedo-Opazo, C., Tisseyre, B., Guillaume, S., and Ojeda, H.: The potential of high spatial resolution information to define withinvineyard zones related to vine water status, Precis. Agric., 9, 285-302, https://doi.org/10.1007/s11119-008-9073-1, 2008.
Acevedo-Opazo, C., Tisseyre, B., Ojeda, H., and Guillaume, S.: Spatial extrapolation of the vine (Vitis vinifera L.) water status: a first step towards a spatial prediction model, Irrigation Sci., 28, 143-155, https://doi.org/10.1007/s00271-009-0170-3, 2010.
Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716-723, https://doi.org/10.1109/TAC.1974.1100705, 1974.
Allen, R., Pereira, L., Smith, M., and Raes, D.: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, ISBN 92-5-104219-5, 1998.
Baluja, J., Diago, M. P., Balda, P., Zorer, R., Meggio, F., Morales, F., and Tardaguila, J.: Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV), Irrigation Sci., 30, 511-522, https://doi.org/10.1007/s00271-012-0382-9, 2012.
Bates, J. S., Montzka, C., Schmidt, M., and Jonard, F.: Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR, Remote Sens., 13, 710, https://doi.org/10.3390/rs13040710, 2021.
Becker, T., Nelsen, T. S., Leinfelder-Miles, M., and Lundy, M. E.: Differentiating between Nitrogen and Water Deficiency in Irrigated Maize Using a UAV-Based Multi-Spectral Camera, Agronomy, 10, 1671, https://doi.org/10.3390/agronomy10111671, 2020.
Bellvert, J., Marsal, J., Mata, M., and Girona, J.: Identifying irrigation zones across a 7.5-ha 'Pinot noir' vineyard based on the variability of vine water status and multispectral images, Irrigation Sci., 30, 499-509, https://doi.org/10.1007/s00271-012-0380-y, 2012.
Bellvert, J., Zarco-Tejada, P. J., Girona, J., and Fereres, E.: Mapping crop water stress index in a "Pinot-noir" vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle, Precis. Agric., 15, 361-376, https://doi.org/10.1007/s11119-013-9334-5, 2014.
Bellvert, J., Marsal, J., Girona, J., and Zarco-Tejada, P. J.: Seasonal evolution of crop water stress index in grapevine varieties determined with high-resolution remote sensing thermal imagery, Irrigation Sci., 33, 81-93, https://doi.org/10.1007/s00271-014-0456-y, 2015.
Berni, J. A. J., Zarco-Tejada, P. J., Suarez, L., and Fereres, E.: Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle, IEEE T. Geosci. Remote, 47, 722-738, https://doi.org/10.1109/TGRS.2008.2010457, 2009.
Berry, A., Vivier, M. A., and Poblete-Echeverría, C.: Evaluation of canopy fraction-based vegetation indices, derived from multispectral UAV imagery, to map water status variability in a commercial vineyard, Irrigation Sci., https://doi.org/10.1007/s00271-023-00907-1, 2024.
Bezerra-Coelho, C. R., Zhuang, L., Barbosa, M. C., Soto, M. A., and van Genuchten, M. T.: Further tests of the HYPROP evaporation method for estimating the unsaturated soil hydraulic properties, J. Hydrol. Hydromech., 66, 161-169, https://doi.org/10.1515/johh-2017-0046, 2018.
Binte Mostafiz, R., Noguchi, R., and Ahamed, T.: Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices, Land, 10, 223, https://doi.org/10.3390/land10020223, 2021.
Bramley, R. G. V., Ouzman, J., and Boss, P. K.: 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: Vineyard variation in wines, grapes and soils, Aust. J. Grape Wine R., 17, 217-229, https://doi.org/10.1111/j.1755-0238.2011.00136.x, 2011.
Brillante, L., Martínez-Luscher, J., Yu, R., Plank, C. M., Sanchez, L., Bates, T. L., Brenneman, C., Oberholster, A., and Kurtural, S. K.: Assessing Spatial Variability of Grape Skin Flavonoids at the Vineyard Scale Based on Plant Water Status Mapping, J. Agr. Food Chem., 65, 5255-5265, https://doi.org/10.1021/acs.jafc.7b01749, 2017a.
Brillante, L., Mathieu, O., Lévêque, J., van Leeuwen, C., and Bois, B.: Water status and must composition in grapevine cv.Chardonnay with different soils and topography and a mini meta-analysis of the _13C/water potentials correlation, J. Sci. Food Agr., 98, 691-697, https://doi.org/10.1002/jsfa.8516, 2017b.
Cai, G., König, M., Carminati, A., Abdalla, M., Javaux, M., Wankmüller, F., and Ahmed, M. A.: Transpiration response to soil drying and vapor pressure deficit is soil texture specific, Plant Soil, 500, 129-145, https://doi.org/10.1007/s11104-022-05818-2, 2022.
Calvario, G., Sierra, B., Alarcón, T. E., Hernandez, C., and Dalmau, O.: A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs, Sensors, 17, 1411, https://doi.org/10.3390/s17061411, 2017.
Carminati, A. and Javaux, M.: Soil Rather Than Xylem Vulnerability Controls Stomatal Response to Drought, Trends Plant Sci., 25, 868-880, https://doi.org/10.1016/j.tplants.2020.04.003, 2020.
Carter, G. A. and Knapp, A. K.: Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration, Am. J. Bot., 88, 677-684, 2001.
Caruso, G. and Palai, G.: Assessing grapevine water status using Sentinel-2 images, Italus Hortus, 30, 70-79, https://doi.org/10.26353/j.itahort/2023.3.7079, 2023.
Choné, X., Van Leeuwen, C., Dubourdieu, D., and Gaudillère, J. P.: Stem Water Potential is a Sensitive Indicator of Grapevine Water Status, Ann. Bot., 87, 477-483, https://doi.org/10.1006/anbo.2000.1361, 2001.
Cinat, P., Di Gennaro, S. F., Berton, A., and Matese, A.: Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images, Remote Sens., 11, 1023, https://doi.org/10.3390/rs11091023, 2019.
Clevers, J. G. P. W. and Gitelson, A. A.: Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3, Int. J. Appl. Earth Obs., 23, 344-351, https://doi.org/10.1016/j.jag.2012.10.008, 2013.
Comba, L., Biglia, A., Ricauda Aimonino, D., and Gay, P.: Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture, Comput. Electron. Agr., 155, 84-95, https://doi.org/10.1016/j.compag.2018.10.005, 2018.
Costa, J. M., Grant, O. M., and Chaves, M. M.: Use of Thermal Imaging in Viticulture: Current Application and Future Prospects, in: Methodologies and Results in Grapevine Research, edited by: Delrot, S., Medrano, H., Or, E., Bavaresco, L., and Grando, S., Springer Netherlands, Dordrecht, 135-150, https://doi.org/10.1007/978-90-481-9283-0_10, 2010.
De Bei, R., Cozzolino, D., Sullivan, W., Cynkar, W., Fuentes, S., Dambergs, R., Pech, J., and Tyerman, S.: Non-destructive measurement of grapevine water potential using near infrared spectroscopy, Aust. J. Grape Wine R., 17, 62-71, https://doi.org/10.1111/j.1755-0238.2010.00117.x, 2011.
Delval, L., Vanderborght, J., and Javaux, M.: Combination of plant and soil water potential monitoring and modelling demonstrates soil-root hydraulic disconnection during drought, Plant Soil, https://doi.org/10.1007/s11104-024-07062-2, 2024a.
Delval, L., Jonard, F., and Javaux, M.: Simultaneous in situ monitoring of belowground, trunk and relative canopy hydraulic conductance of grapevine demonstrates a soil texture-specific transpiration control, OENO One, 58, 1-17, https://doi.org/10.20870/oeno-one.2024.58.4.8257, 2024b.
Draye, X., Kim, Y., Lobet, G., and Javaux, M.: Model-assisted integration of physiological and environmental constraints affecting the dynamic and spatial patterns of root water uptake from soils, J. Exp. Bot., 61, 2145-2155, https://doi.org/10.1093/jxb/erq077, 2010.
Dry, P. R. and Loveys, B. R.: Factors influencing grapevine vigour and the potential for control with partial rootzone drying, Aust. J. Grape Wine R., 4, 140-148, https://doi.org/10.1111/j.1755-0238.1998.tb00143.x, 1998.
Dry, P. R. and Loveys, B. R.: Grapevine shoot growth and stomatal conductance are reduced when part of the root system is dried, VITIS - J. Grapevine Res., 38, 151-151, https://doi.org/10.5073/vitis.1999.38.151-156, 1999.
Espinoza, C. Z., Khot, L. R., Sankaran, S., and Jacoby, P. W.: High Resolution Multispectral and Thermal Remote Sensing-Based Water Stress Assessment in Subsurface Irrigated Grapevines, Remote Sens., 9, 961, https://doi.org/10.3390/rs9090961, 2017.
Ferro, M. V. and Catania, P.: Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review, Horticulturae, 9, 399, https://doi.org/10.3390/horticulturae9030399, 2023.
Fraga, H., Malheiro, A. C., Moutinho-Pereira, J., Cardoso, R. M., Soares, P. M. M., Cancela, J. J., Pinto, J. G., and Santos, J. A.: Integrated Analysis of Climate, Soil, Topography and Vegetative Growth in Iberian Viticultural Regions, PLOS ONE, 9, e108078, https://doi.org/10.1371/journal.pone.0108078, 2014.
Gambetta, G. A., Herrera, J. C., Dayer, S., Feng, Q., Hochberg, U., and Castellarin, S. D.: The physiology of drought stress in grapevine: towards an integrative definition of drought tolerance, J. Exp. Bot., 71, 4658-4676, https://doi.org/10.1093/jxb/eraa245, 2020.
Gamon, J. A. and Surfus, J. S.: Assessing leaf pigment content and activity with a reflectometer, New Phytol., 143, 105-117, https://doi.org/10.1046/j.1469-8137.1999.00424.x, 1999.
Gamon, J. A., Peñuelas, J., and Field, C. B.: A narrowwaveband spectral index that tracks diurnal changes in photosynthetic efficiency, Remote Sens. Environ., 41, 35-44, https://doi.org/10.1016/0034-4257(92)90059-S, 1992.
Gao, B.: NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 58, 257-266, https://doi.org/10.1016/S0034-4257(96)00067-3, 1996.
García-Tejera, O., López-Bernal, Á., Orgaz, F., Testi, L., and Villalobos, F. J.: The pitfalls of water potential for irrigation scheduling, Agr. Water Manage., 243, 106522, https://doi.org/10.1016/j.agwat.2020.106522, 2021.
Gavrilović, M., Jovanović, D., Božović, P., Benka, P., and Govedarica, M.: Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery, Remote Sens., 16, 584, https://doi.org/10.3390/rs16030584, 2024.
Giovos, R., Tassopoulos, D., Kalivas, D., Lougkos, N., and Priovolou, A.: Remote Sensing Vegetation Indices in Viticulture: A Critical Review, Agriculture, 11, 457, https://doi.org/10.3390/agriculture11050457, 2021.
Gitelson, A. A., Kaufman, Y. J., Stark, R., and Rundquist, D.: Novel algorithms for remote estimation of vegetation fraction, Remote Sens. Environ., 80, 76-87, https://doi.org/10.1016/S0034-4257(01)00289-9, 2002.
Gitelson, A. A., Keydan, G. P., and Merzlyak, M. N.: Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves, Geophys. Res. Lett., 33, L11402, https://doi.org/10.1029/2006GL026457, 2006.
Helman, D., Bahat, I., Netzer, Y., Ben-Gal, A., Alchanatis, V., Peeters, A., and Cohen, Y.: Using Time Series of High-Resolution Planet Satellite Images to Monitor Grapevine Stem Water Potential in Commercial Vineyards, Remote Sens., 10, 1615, https://doi.org/10.3390/rs10101615, 2018.
Hochberg, U., Degu, A., Gendler, T., Fait, A., and Rachmilevitch, S.: The variability in the xylem architecture of grapevine petiole and its contribution to hydraulic differences, Funct. Plant Biol., 42, 357-365, https://doi.org/10.1071/FP14167, 2015.
Idso, S. B., Jackson, R. D., Pinter, P. J., Reginato, R. J., and Hatfield, J. L.: Normalizing the stress-degree-day parameter for environmental variability, Agr. Meteorol., 24, 45-55, https://doi.org/10.1016/0002-1571(81)90032-7, 1981.
James, G., Witten, D., Hastie, T., and Tibshirani, R.: An Introduction to Statistical Learning: with Applications in R, Springer US, New York, NY, https://doi.org/10.1007/978-1-0716-1418-1, 2021.
Jones, H.: Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology, Cambridge University Press, Cambridge, 423 pp., https://doi.org/10.1017/CBO9780511845727, 2013.
Jones, H. G., Stoll, M., Santos, T., de Sousa, C., Chaves, M. M., and Grant, O. M.: Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine, J. Exp. Bot., 53, 2249-2260, https://doi.org/10.1093/jxb/erf083, 2002.
Karn, R., Hillin, D., Helwi, P., Scheiner, J., and Guo, W.: Assessing grapevine vigor as affected by soil physicochemical properties and topographic attributes for precision vineyard management, Sci. Hortic., 328, 112857, https://doi.org/10.1016/j.scienta.2024.112857, 2024.
Laroche-Pinel, E., Duthoit, S., Albughdadi, M., Costard, A., Rousseau, J., Chéret, V., and Clenet, H.: Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images, Remote Sens., 13, 1-21, https://doi.org/10.3390/rs13091837, 2021a.
Laroche-Pinel, E., Albughdadi, M., Duthoit, S., Chéret, V., Rousseau, J., and Clenet, H.: Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status, Remote Sens., 13, 536, https://doi.org/10.3390/rs13030536, 2021b.
Laroche-Pinel, E., Vasquez, K. R., and Brillante, L.: Assessing grapevine water status in a variably irrigated vineyard with NIR/SWIR hyperspectral imaging from UAV, Precis. Agric., 25, 2356-2374, https://doi.org/10.1007/s11119-024-10170-9, 2024.
Lavoie-Lamoureux, A., Sacco, D., Risse, P.-A., and Lovisolo, C.: Factors influencing stomatal conductance in response to water availability in grapevine: a meta-analysis, Physiol. Plant., 159, 468-482, https://doi.org/10.1111/ppl.12530, 2017.
MacQueen, J.: Some methods for classification and analysis of multivariate observations, Proc. Fifth Berkeley Symp. Math. Stat. Probab. Vol. 1 Stat., 5.1, 281-298, 1967.
Marañón, M., Fernández-Novales, J., Tardaguila, J., Gutiérrez, S., and Diago, M. P.: NIR attribute selection for the development of vineyard water status predictive models, Biosyst. Eng., 229, 167-178, https://doi.org/10.1016/j.biosystemseng.2023.04.001, 2023.
Möller, M., Alchanatis, V., Cohen, Y., Meron, M., Tsipris, J., Naor, A., Ostrovsky, V., Sprintsin, M., and Cohen, S.: Use of thermal and visible imagery for estimating crop water status of irrigated grapevine, J. Exp. Bot., 58, 827-838, https://doi.org/10.1093/jxb/erl115, 2007.
Moya, I., Camenen, L., Evain, S., Goulas, Y., Cerovic, Z. G., Latouche, G., Flexas, J., and Ounis, A.: A new instrument for passive remote sensing: 1. Measurements of sunlight-induced chlorophyll fluorescence, Remote Sens. Environ., 91, 186-197, https://doi.org/10.1016/j.rse.2004.02.012, 2004.
Moya-Laraño, J. and Corcobado, G.: Plotting partial correlation and regression in ecological studies, Web Ecol., 8, 35-46, https://doi.org/10.5194/we-8-35-2008, 2008.
Nolan, A., Park, S., O'Connell, M., Fuentes, S., Ryu, D., and Chung, H.: Automated detection and segmentation of vine rows using high resolution UAS imagery in a commercial vineyard, in: Proceedings - 21st International Congress on Modelling and Simulation, MODSIM 2015, Gold Coast, Australia, 29 November-4 December 2015, 1406-1412, https://doi.org/10.36334/modsim.2015.f12.nolan, 2015.
Ojeda, H., Carrillo, N., Deis, L., Tisseyre, B., Heywang, M., and Carbonneau, A.: Precision viticulture and water status II: Quantitative and qualitative performance of different within field zones, defined from water potential mapping, Proc. 14th GESCO Congr., 741-748, 2005.
Ollat, N., Peccoux, A., Papura, D., Esmenjaud, D., Marguerit, E., Tandonnet, J.-P., Bordenave, L., Cookson, S. j., Barrieu, F., Rossdeutsch, L., Lecourt, J., Lauvergeat, V., Vivin, P., Bert, P.-F., and Delrot, S.: Rootstocks as a component of adaptation to environment, in: Grapevine in a Changing Environment, John Wiley & Sons, Ltd, 68-108, https://doi.org/10.1002/9781118735985.ch4, 2015.
Pagay, V. and Kidman, C. M.: Evaluating Remotely-Sensed Grapevine (Vitis vinifera L.) Water Stress Responses Across a Viticultural Region, Agronomy, 9, 682, https://doi.org/10.3390/agronomy9110682, 2019.
Passioura, J.: The Transport of Water from Soil to Shoot in Wheat Seedlings, J. Exp. Bot., 31, 333-345, https://doi.org/10.1093/jxb/31.1.333, 1980.
Penuelas, J., Pinol, J., Ogaya, R., and Filella, I.: Estimation of plant water concentration by the reflectance Water Index WI (R900/R970), Int. J. Remote Sens., 18, 2869-2875, https://doi.org/10.1080/014311697217396, 1997.
Pereyra, G., Pellegrino, A., Ferrer, M., and Gaudin, R.: How soil and climate variability within a vineyard can affect the heterogeneity of grapevine vigour and production, OENO One, 57, 297-313, https://doi.org/10.20870/oeno-one.2023.57.3.7498, 2023.
Poblete, T., Ortega-Farías, S., Moreno, M. A., and Bardeen, M.: Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV), Sensors, 17, 2488, https://doi.org/10.3390/s17112488, 2017.
Poblete-Echeverría, C., Olmedo, G. F., Ingram, B., and Bardeen, M.: Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard, Remote Sens., 9, 268, https://doi.org/10.3390/rs9030268, 2017.
Pôças, I., Rodrigues, A., Gonçalves, S., Costa, P. M., Gonçalves, I., Pereira, L. S., and Cunha, M.: Predicting GrapevineWater Status Based on Hyperspectral Reflectance Vegetation Indices, Remote Sens., 7, 16460-16479, https://doi.org/10.3390/rs71215835, 2015.
Pou, A., Diago, M. P., Medrano, H., Baluja, J., and Tardaguila, J.: Validation of thermal indices for water status identification in grapevine, Agr. Water Manage., 134, 60-72, https://doi.org/10.1016/j.agwat.2013.11.010, 2014.
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., and Sorooshian, S.: A modified soil adjusted vegetation index, Remote Sens. Environ., 48, 119-126, https://doi.org/10.1016/0034-4257(94)90134-1, 1994.
Rabia, A. H., Neupane, J., Lin, Z., Lewis, K., Cao, G., and Guo, W.: Chapter Four - Principles and applications of topography in precision agriculture, in: Advances in Agronomy, vol. 171, edited by: Sparks, D. L., Academic Press, 143-189, https://doi.org/10.1016/bs.agron.2021.08.005, 2022.
Rallo, G., Minacapilli, M., Ciraolo, G., and Provenzano, G.: Detecting crop water status in mature olive groves using vegetation spectral measurements, Biosyst. Eng., 128, 52-68, https://doi.org/10.1016/j.biosystemseng.2014.08.012, 2014.
Rapaport, T., Hochberg, U., Shoshany, M., Karnieli, A., and Rachmilevitch, S.: Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment, ISPRS J. Photogramm., 109, 88-97, https://doi.org/10.1016/j.isprsjprs.2015.09.003, 2015.
Rebekić, A., Lončarić, Z., Petrović, S., and Marić, S.: Pearson's or Spearman's correlation coefficient - Which one to use?, Poljoprivreda, 21, 47-54, https://doi.org/10.18047/poljo.21.2.8, 2015.
Ridley, A. M. and Burland, J. B.: A new instrument for the measurement of soil moisture suction, Géotechnique, 43, 321-324, https://doi.org/10.1680/geot.1993.43.2.321, 1993.
Rodríguez-Pérez, J. R., Riaño, D., Carlisle, E., Ustin, S., and Smart, D. R.: Evaluation of Hyperspectral Reflectance Indexes to Detect Grapevine Water Status in Vineyards, Am. J. Enol. Viticult., 58, 302-317, https://doi.org/10.5344/ajev.2007.58.3.302, 2007.
Romero, M., Luo, Y., Su, B., and Fuentes, S.: Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management, Comput. Electron. Agr., 147, 109-117, https://doi.org/10.1016/j.compag.2018.02.013, 2018.
RStudio Team: RStudio: Integrated Development for R. RStudio, PBC, Boston, 2022.
Santesteban, L. G., Di Gennaro, S. F., Herrero-Langreo, A., Miranda, C., Royo, J. B., and Matese, A.: Highresolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard, Agr. Water Manage., 183, 49-59, https://doi.org/10.1016/j.agwat.2016.08.026, 2017.
Scholander, P. F., Bradstreet, E. D., Hemmingsen, E. A., and Hammel, H. T.: Sap Pressure in Vascular Plants, Science, 148, 339-346, https://doi.org/10.1126/science.148.3668.339, 1965.
Serrano, L., González-Flor, C., and Gorchs, G.: Assessing vineyard water status using the reflectance based Water Index, Agr. Ecosyst. Environ., 139, 490-499, https://doi.org/10.1016/j.agee.2010.09.007, 2010.
Soar, C. J., Speirs, J., Maffei, S. M., Penrose, A. B., Mccarthy, M. G., and Loveys, B. R.: Grape vine varieties Shiraz and Grenache differ in their stomatal response to VPD: apparent links with ABA physiology and gene expression in leaf tissue, Aust. J. GrapeWine R., 12, 2-12, https://doi.org/10.1111/j.1755-0238.2006.tb00038.x, 2006.
Soubry, I., Patias, P., and Tsioukas, V.: Monitoring vineyards with UAV and multi-sensors for the assessment of water stress and grape maturity, J. Unmanned Veh. Syst., 5, 37-50, https://doi.org/10.1139/juvs-2016-0024, 2017.
Stagakis, S., González-Dugo, V., Cid, P., Guillén-Climent, M. L., and Zarco-Tejada, P. J.: Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices, ISPRS J. Photogramm., 71, 47-61, https://doi.org/10.1016/j.isprsjprs.2012.05.003, 2012.
Suárez, L., Zarco-Tejada, P. J., Sepulcre-Cantó, G., Pérez-Priego, O., Miller, J. R., Jiménez-Muñoz, J. C., and Sobrino, J.: Assessing canopy PRI for water stress detection with diurnal airborne imagery, Remote Sens. Environ., 112, 560-575, https://doi.org/10.1016/j.rse.2007.05.009, 2008.
Tang, Z., Jin, Y., Alsina, M. M., McElrone, A. J., Bambach, N., and Kustas, W. P.: Vine water status mapping with multispectral UAV imagery and machine learning, Irrigation Sci., 40, 715-730, https://doi.org/10.1007/s00271-022-00788-w, 2022.
Tardaguila, J., Fernández-Novales, J., Gutiérrez, S., and Diago, M. P.: Non-destructive assessment of grapevine water status in the field using a portable NIR spectrophotometer, J. Sci. Food Agr., 97, 3772-3780, https://doi.org/10.1002/jsfa.8241, 2017.
Taylor, J. A., Acevedo-Opazo, C., Ojeda, H., and Tisseyre, B.: Identification and significance of sources of spatial variation in grapevine water status, Aust. J. Grape Wine R., 16, 218-226, https://doi.org/10.1111/j.1755-0238.2009.00066.x, 2010.
Tisseyre, B., Ojeda, H., Carillo, N., Deis, L., and Heywang, M.: Precision Viticulture and Water Status: Mapping the PredawnWater Potential to Define within Vineyard Zones, Proc. 14th GESCO Congr., Montpellier, France, September 2005, 337-346, https://hal.inrae.fr/hal-02763242v1 (last access: 28 January 2025), 2005.
Tramontini, S., van Leeuwen, C., Domec, J.-C., Destrac-Irvine, A., Basteau, C., Vitali, M., Mosbach-Schulz, O., and Lovisolo, C.: Impact of soil texture and water availability on the hydraulic control of plant and grape-berry development, Plant Soil, 368, 215-230, https://doi.org/10.1007/s11104-012-1507-x, 2013.
Tyree, M. T. and Zimmermann, M. H.: The Cohesion-Tension Theory of Sap Ascent, in: Xylem Structure and the Ascent of Sap, edited by: Tyree, M. T. and Zimmermann, M. H., Springer, Berlin, Heidelberg, 49-88, https://doi.org/10.1007/978-3-662-04931-0_3, 2002.
van Genuchten, M. T.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils, Soil Sci. Soc. Am. J., 44, 892-898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.
Van Leeuwen, C., Friant, P., Choné, X., Tregoat, O., Koundouras, S., and Dubourdieu, D.: Influence of Climate, Soil, and Cultivar on Terroir, Am. J. Enol. Vitic., 55, 207-217, https://doi.org/10.5344/ajev.2004.55.3.207, 2004.
van Leeuwen, C., Trégoat, O., Choné, X., Bois, B., Pernet, D., and Gaudillere, J.-P.: 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?, J. Int. Sci. Vigne Vin, 43, 121-134, https://doi.org/10.20870/oenoone. 2009.43.3.798, 2009.
Van Leeuwen, C., Roby, J.-P., and Rességuier, L. de: Soilrelated terroir factors: a review, OENO One, 52, 173-188, https://doi.org/10.20870/oeno-one.2018.52.2.2208, 2018.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, J. Climate, 23, 1696-1718, https://doi.org/10.1175/2009JCLI2909.1, 2010.
Wilkinson, L.: Tests of significance in stepwise regression, Psychol. Bull., 86, 168-174, https://doi.org/10.1037/0033-2909.86.1.168, 1979.
Xue, J. and Su, B.: Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications, J. Sensors, 2017, e1353691, https://doi.org/10.1155/2017/1353691, 2017.
Yang, W., Zhu, J., van Leeuwen, C., Dai, Z., and Gambetta, G. A.: GrapevineXL reliably predicts multi-annual dynamics of vine water status, berry growth, and sugar accumulation in vineyards, Hortic. Res., 10, uhad071, https://doi.org/10.1093/hr/uhad071, 2023.
Zarco-Tejada, P. J., González-Dugo, V., Williams, L. E., Suárez, L., Berni, J. A. J., Goldhamer, D., and Fereres, E.: A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index, Remote Sens. Environ., 138, 38-50, https://doi.org/10.1016/j.rse.2013.07.024, 2013.
Zhou, Q., Chan, C. W., and Tontiwachiwuthikul, P.: Regression Analysis Study on the Carbon Dioxide Capture Process, Ind. Eng. Chem. Res., 47, 4937-4943, https://doi.org/10.1021/ie701747f, 2008.
Zygielbaum, A. I., Gitelson, A. A., Arkebauer, T. J., and Rundquist, D. C.: Non-destructive detection of water stress and estimation of relative water content in maize, Geophys. Res. Lett., 36, L12403, https://doi.org/10.1029/2009GL038906, 2009.
