Irrigation Science

, Volume 30, Issue 6, pp 511–522 | Cite as

Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV)

  • Javier Baluja
  • Maria P. Diago
  • Pedro Balda
  • Roberto Zorer
  • Franco Meggio
  • Fermin Morales
  • Javier Tardaguila
Original Paper

Abstract

The goal of this study was to assess the water status variability of a commercial rain-fed Tempranillo vineyard (Vitis vinifera L.) by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). The relationships between aerial temperatures or indices derived from the imagery and leaf stomatal conductance (g s) and stem water potential (Ψstem) were determined. Aerial temperature was significantly correlated with g s (R 2 = 0.68, p < 0.01) and Ψstem (R 2 = 0.50, p < 0.05). Furthermore, the thermal indices derived from aerial imagery were also strongly correlated with Ψstem and g s. Moreover, different spectral indices were related to vineyard water status, although NDVI (normalized difference vegetation index) and TCARI/OSAVI (ratio between transformed chlorophyll absorption in reflectance and optimized soil-adjusted vegetation index) showed the highest coefficient of determination with Ψstem (R 2 = 0.68, p < 0.05) and g s (R 2 = 0.84, p < 0.05), respectively. While the relationship with thermal imagery and water status parameters could be considered as a short-term response, NDVI and TCARI/OSAVI indices were probably reflecting the result of cumulative water deficits, hence a long-term response. In conclusion, thermal and multispectral imagery using an UAV allowed assessing and mapping spatial variability of water status within the vineyard.

Notes

Acknowledgments

This work was supported by the Agencia de Desarrollo Económico de La Rioja (ADER) with the Project TELEVITIS 2008-I-ID-00123. The authors want to thank Domecq Bodegas allowing the execution of this study in their commercial vineyard. Gratefulness also to Dr. Pablo Zarco-Tejada and Dr. Guadalupe Sepulcre-Cantó for their collaboration and advice on this study. Also, the authors wish to acknowledge the Quantalab (IAS, CSIC) participation in the UAV flights. Also, the authors want to thank Markus Metz for the support provided with r.watershed algorithm and the PGIS team (FEM, IASMA), particularly Markus Neteler, for the support with GRASS GIS. Fermín Morales thanks Gobierno de Aragón (A03 research group) for financial support.

References

  1. Acevedo-Opazo C, Tysseire B, Taylor J, Ojeda H, Guillaume S (2010) Spatial prediction model of the vine (Vitis vinifera L.) water status using high resolution ancillary information. Precis Agr 11:358–378CrossRefGoogle Scholar
  2. Acevedo-Opazo C, Tisseyre B, Ojeda H, Ortega-Farias S, Guillaume S (2008a) Is it possible to assess the spatial variability of vine water status? J Int Sci Vigne Vin 42:203–219Google Scholar
  3. Acevedo-Opazo C, Tisseyre B, Guillaume S, Ojeda H (2008b) The potential of high resolution information to define within-vineyard zones related to vine water status. Precis Agr 9:285–302CrossRefGoogle Scholar
  4. Alchanatis V, Cohen Y, Cohen S, Moller M, Sprinstin M, Meron M, Tsipris J, Saranga Y, Sela E (2010) Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging. Precis Agric 11:27–41CrossRefGoogle Scholar
  5. Allen RG, Pereira LS, Raes D, Smith M (1999) Crop evapotranspiration. FAO irrigation and drainage paper 56. Rome: FAOGoogle Scholar
  6. Arnó J, Martínez Casanovas J, Ribes-Dasi M, Rosell JR (2009) Review. Precision viticulture. Research topics, challenges and opportunities in site-specific vineyard management. Spanish J Agric Res 7:779–790Google Scholar
  7. Ben-Gal A, Agam N, Alchanatis V, Cohen Y, Yermiyahu U, Zipori I, Presnov E, Sprintsin M, Dag A (2009) Evaluating water stress in irrigated olives: correlation of soil water status, tree water status, and thermal imagery. Irrig Sci 27:367–376CrossRefGoogle Scholar
  8. Berni JAJ, Zarco-Tejada PJ, Suarez L, Fereres E (2009) Thermal and narrow-band multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Rem Sens 47:722–738CrossRefGoogle Scholar
  9. Bramley RGV (2010) Precision viticulture: Managing vineyard variability for improved quality outcomes. Chapter 12. In: Reynolds AG (ed) Understanding and managing wine quality and safety. Woodhead Publishing, Cambridge, pp 445–480Google Scholar
  10. Broge NH, Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Rem Sens Environ 76:156–172CrossRefGoogle Scholar
  11. Chapman DM, Roby G, Ebeler SE, Guinard JX, Matthews MA (2005) Sensory attributes of Cabernet Sauvignon wines made from vines with different water status. Aust J Grape Wine Res 11:339–347CrossRefGoogle Scholar
  12. Chaves MM, Santos TP, Souza CR, Ortuño MF, Rodrigues ML, Lopes CM, Maroco JP, Pereira JS (2007) Deficit irrigation in grapevine improves water-use efficiency while controlling vigour and production quality. Ann Appl Biol 150:237–252CrossRefGoogle Scholar
  13. Chen J (1996) Evaluation of vegetation indices and modified simple ratio for boreal applications. Can J Remote Sens 22:229–242Google Scholar
  14. Cifre J, Bota J, Escalona JM, Medrano H, Flexas J (2005) Physiological tools for irrigation scheduling in grapevine (Vitis vinifera L.) An open gate to improve water-use efficiency? Agr Ecosyst Environ 106:159–170CrossRefGoogle Scholar
  15. Cohen Y, Alchanatis V, Meron M, Saranga S, Tsipris J (2005) Estimation of leaf water potential by thermal imagery and spatial analysis. J Exp Bot 56:1843–1852PubMedCrossRefGoogle Scholar
  16. Cohen Y, Alchanatis V, Prigojin A, Levi A, Soroker V, Cohen Y (2011) Use of aerial thermal imaging to estimate water status on palm trees. Precis Agric. doi: 10.1007/s11119-011-9232-7 Google Scholar
  17. Costa JM, Grant OM, Chaves MM (2010) Use of Thermal Imaging in Viticulture: Current Application and Future Prospects. In: Delrot S, Medrano H, Or E, Bavaresco L, Grando S (eds) Methodologies and results in grapevine research. Springer, New York, pp 135–150CrossRefGoogle Scholar
  18. Daughtry CST, Walthall CL, Kim MS, Brown de Colstoun E, McMurtrey JE III (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Rem Sens Environ 74:229–239CrossRefGoogle Scholar
  19. De Bei R, Cozzolino D, Sullivan W, Cynkar W, Fuentes S, Dambergs R, Pech J, Tyerman S (2011) Non-destructive measurement of grapevine water potential using near infrared spectroscopy. Aust J Grape Wine Res 17:62–71CrossRefGoogle Scholar
  20. Escalona JM, Flexas J, Medrano H (2002) Drought effects on water flow, photosynthesis and growth of potted grapevines. Vitis 41:57–62Google Scholar
  21. Flexas J, Escalona JM, Evain S, Gulias J, Moya I, Osmond CB, Medrano H (2002) Steady-state chlorophyll fluorescence (Fs) measurements as a tool to follow variations of net CO2 assimilation and stomatal conductance during water-stress in C3 plants. Physiol Plant 114:231–240PubMedCrossRefGoogle Scholar
  22. Fuchs M (1990) Infrared measurement of canopy temperature and detection of plant water stress. Theor Appl Climatol 42:253–261CrossRefGoogle Scholar
  23. Fuentes DA, Gamon JA, Qiu HL, Sims DA, Roberts DA (2001) Mapping Canadian boreal forest vegetation using pigment and water absorption features derived from the AVIRIS sensor. J Geophys Res 106:33565–33577CrossRefGoogle Scholar
  24. Gamon JA, Surfus JS (1999) Assessing leaf pigment content and activity with a reflectometer. New Phytol 143:105–117CrossRefGoogle Scholar
  25. Girona J, Mata AM, Del Campo J, Arbone A, Bartra E, Marsal J (2006) The use of midday leaf water potential for scheduling deficit irrigation in vineyards. Irrig Sci 24:115–117CrossRefGoogle Scholar
  26. Gitelson AA, Merzlyak MN (1998) Remote sensing of chlorophyll concentration in higher plant leaves. Adv Space Res 22:689–692CrossRefGoogle Scholar
  27. Grant OM, Tronina L, Jones HG, Chaves MM (2007) Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. J Exp Bot 58:815–825PubMedCrossRefGoogle Scholar
  28. 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. Rem Sens Environ 84:416–426CrossRefGoogle Scholar
  29. 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. Rem Sens Environ 90:337–352CrossRefGoogle Scholar
  30. Hall A, Lamb DW, Holzapfel B, Louis J (2002) Optical remote sensing applications in viticulture: a review. Aust J Grape Wine Res 8:36–47CrossRefGoogle Scholar
  31. Hofierka J (1997) Direct solar radiation modelling within an open GIS environment. Conference, Vienna, AustryGoogle Scholar
  32. Hofierka J, Súri M (2002) The solar radiation model for open source GIS:implementation and applications. Open source GIS-GRASS users conference. Trento, ItalyGoogle Scholar
  33. Hofierka J, Mitasova H, Neteler M (2009) Chapter 17: geomorphometry in GRASS GIS. Dev Soil Sci 33:387–410CrossRefGoogle Scholar
  34. Holben BN, Tanré D, Smirnov A, Eck TF, Slutsker I, Abuhassan N, Newcomb WW, Schafer JS, Chatenet B, Lavenu F, Kaufman YJ, Vande-Castle J, Setzer A, Markham B, Clark D, Frouin R, Halthore R, Karneli A, O’Neill NT, Pietras C, Pinker RT, Voss K, Zibordi G (2001) An emerging ground-based aerosol climatology. Aerosol optical depth from AERONET. J Geophys Res 106:12067–12097CrossRefGoogle Scholar
  35. Idso SB, Jackson RD, Pinter PJ, Reginato RJ, Hatfield JL (1981) Normalizing the stress degree- day parameter for environmental variability. Agr Forest Meteorol 24:45–55Google Scholar
  36. Intrigliolo DS, Castel JR (2007) Evaluation of grapevine water status from trunk diameter variations. Irrig Sci 26:49–59CrossRefGoogle Scholar
  37. Jones HG (1992) Plants and microclimate. Cambridge University Press, CambridgeGoogle Scholar
  38. Jones HG (1999) Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agr Forest Meteorol 95:139–149CrossRefGoogle Scholar
  39. Jones HG (2004a) Irrigation scheduling: advantages and pitfalls of plant based methods. J Exp Bot 55:2427–2436PubMedCrossRefGoogle Scholar
  40. Jones HG (2004b) Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. Adv Bot Res 41:107–163CrossRefGoogle Scholar
  41. Jones HG, Stoll M, Santos T, De Sousa C, Chaves MM, Grant OM (2002) Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. J Exp Bot 53:2249–2260PubMedCrossRefGoogle Scholar
  42. Jones HG, Serraj R, Loveys BR, Xiong L, Wheaton A, Price AH (2009) Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct Plant Biol 36:978–989CrossRefGoogle Scholar
  43. Jordan CF (1969) Derivation of leaf area index from quality of light on the forest floor. Ecology 50:663–666CrossRefGoogle Scholar
  44. Karantzalos K, Argialas D (2006) Improving edge detection and watershed segmentation with anisotropic diffusion and morphological levelings. Int J Rem Sens 27:5427–5434CrossRefGoogle Scholar
  45. Kasten F (1996) The linke turbidity factor based on improved values of the integral Rayleigh optical thickness. Sol Energy 56:239–244CrossRefGoogle Scholar
  46. Kazmierski M, Glemas P, Rousseau J, Tisseyre B (2011) Temporal stability of within-field patterns of NDVI in non irrigated Mediterranean vineyards. J Int Sci Vigne Vin 45:61–73Google Scholar
  47. Kim MS, Daughtry CST, Chappelle EW, McMurtrey JE (1994) The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (APAR) In Proceedings for ISPRS’94 299–306.Val d’Isere, FranceGoogle Scholar
  48. Leinonen I, Grant OM, Tagliavia CPP, Chaves MM, Jones HG (2006) Estimating stomatal conductance with thermal imagery. Plant Cell Environ 29:1508–1518PubMedCrossRefGoogle Scholar
  49. Meggio F, Zarco-Tejada PJ, Miller JR, Martín P, González MR, Berjón A (2008) Row orientation and viewing geometry effects on row-structured vine crops for chlorophyll content estimation. Can J Rem Sens 34(3):220–234Google Scholar
  50. Meggio F, Zarco-Tejada PJ, Núñez LC, Sepulcre-Cantó G, González MR, Martín P (2010) Grape quality assessment in vineyards affected by iron deficiency chlorosis using narrow-band physiological remote sensing indices. Rem Sens Environ 114:1968–1986CrossRefGoogle Scholar
  51. Metz M, Mitasova H, Harmon RS (2011) Accurate stream extraction from large, radar-based elevation models. Hydrol Earth Syst Sci 15:667–678. doi: 10.5194/hess-15-667-2011 CrossRefGoogle Scholar
  52. Mitasova H, Mitas L, Harmon RS (2005) Simultaneous spline approximation and topographic analysis for lidar elevation data in open-source GIS. Geosci Rem Sens Lett IEEE 2:375–379CrossRefGoogle Scholar
  53. Möller M, Alchanatis V, Cohen Y, Meron M, Tsipris J, Naor A, Ostrovsky V, Sprintsin M, Cohen S (2007) Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J Exp Bot 58:827–838PubMedCrossRefGoogle Scholar
  54. Neteler M, Mitasova H (2007) Open GIS: a grass GIS approach. Springer, New YorkGoogle Scholar
  55. Ochagavía H, Grant OM, Baluja J, Diago MP, Tardáguila J (2011) Exploring zenithal and lateral thermography for the assessment of vineyard water status. Proceedings of 17th International Symposium of GiESCO. Asti–Alba (Italia)Google Scholar
  56. Ojeda H, Andary C, Kraeva E, Carbonneau A, Deloire A (2002) Influence of pre and postveraison water deficit on synthesis and concentration of skin phenolic compounds during berry growth of Vitis vinifera cv Shiraz. Am J Enol Vitic 53:261–267Google Scholar
  57. Otsu N (1979) Threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66CrossRefGoogle Scholar
  58. Pieri P, Gaudillére JP (2003) Sensitivity to training system parameters and soil surface albedo of radiation intercepter by vine rows. Vitis 42:77–82Google Scholar
  59. Qi J, Chehbouni A, Huete AR, Kerr Y (1994) A modified soil adjusted vegetation index (MSAVI). Rem Sens Environ 48:119–126CrossRefGoogle Scholar
  60. Reujean J, Breon F (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Rem Sens Environ 51:375–380CrossRefGoogle Scholar
  61. Rigollier C, Bauer O, Wald L (2000) On the clear sky model of the ESRA with respect to the heliosat method. Sol Energy 68:33–48CrossRefGoogle Scholar
  62. Rodríguez-Pérez JR, Riaño D, Carlisle E, Ustin S, Smart DR (2007) Evaluation of hyperspectral reflectance indexes to detect grapevine water status in vineyards. Am J Enol Vitic 58:302–317Google Scholar
  63. Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Rem Sens Environ 55:95–107CrossRefGoogle Scholar
  64. Rouse J, Haas RH, Schell JA, Deering DW, Harlan JC (1974) Monitoring the vernal advancement and retrogradation (Greenwave effect) of natural vegetation. RS Center, A Texas, GSF Center—1974—Texas A & M University, Remote Sensing CenterGoogle Scholar
  65. Safren O, Alchanatis V, Ostrovsky V, Levi O (2007) Detection of green apples in hyperspectralimages of apple-tree foliage using machine vision. Trans ASABE 50:2303–2313Google Scholar
  66. Sepulcre-Cantó G, Diago MP, Balda P, Martínez de Toda F, Morales F, Tardaguila J (2009) Monitoring vineyard spatial variability of vegetative growth and physiological status using an unmanned aerial vehicle (UAV). In: Proceedings of GIESCO symposium. Davis, USAGoogle Scholar
  67. Tardaguila J, Baluja J, Arpon L, Balda P, Oliveira M (2011) Variations of soil properties affect the vegetative growth and yield components of “Tempranillo” grapevines. Precis Agr 12:762–773CrossRefGoogle Scholar
  68. Zarco-Tejada PJ, Berjón A, López-Lozano R, Miller JR, Martín P, Cachorro V, González MR, de Frutos A (2005a) Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Rem Sens Environ 99:271–287CrossRefGoogle Scholar
  69. Zarco-Tejada PJ, Ustin SL, Whiting ML (2005b) Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agron J 97:641–653CrossRefGoogle Scholar
  70. Zarco-Tejada PJ, Berni JAJ, Suárez L, Fereres E (2008) A new era in remote sensing of crops with unmanned robots. SPIE Newsroom. doi: 10.1117/2.1200812.1438

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Javier Baluja
    • 1
  • Maria P. Diago
    • 1
  • Pedro Balda
    • 1
  • Roberto Zorer
    • 2
  • Franco Meggio
    • 3
  • Fermin Morales
    • 4
  • Javier Tardaguila
    • 1
  1. 1.Instituto de Ciencias de la Vid y del VinoUniversity of La Rioja, CSIC, Gobierno de La RiojaLogroñoSpain
  2. 2.GIS and Remote Sensing Unit, Biodiversity and Molecular Ecology Department—DBEMIASMA Research and Innovation CentreSan Michele all’AdigeItaly
  3. 3.Department of Environmental Agronomy and Crop ScienceUniversity of PadovaLegnaroItaly
  4. 4.Department of Plant Nutrition, Experimental Station of Aula DeiCSICZaragozaSpain

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