Hyperspectral Analysis of Grapevine Water Stress

Öz

Viticulture is very sensitive to water stress, which is critical and influenced by all environmental factors, relating to the crop quality and productivity of vineyards. In this study, water stress was examined in veraison and harvest stages for nine different species with spectroradiometric measurements. Leaf water potential (LWP) values from field measurements and original spectra-based (OSB) and continuum removed spectra-based (CRSB) curves were analyzed with correlation and regression analysis to find the highest related wavelengths. The analysis was done for both specific dates of field measurements (i.e. 08.08.2012 and 06.09.2012) and also in aggregate i.e. all measured data. The specific date wavelength-based analysis revealed the “red edge region” as a major water stress indicator. The highest correlated wavelength was found to be 684 nm of CRSB curves with R=0.988. For the aggregate wavelength-based water stress analysis, the “violet and green regions” were identified as the best indicators. The highest correlated wavelength was found to be 410 nm of OSB curves with R=0.820. Furthermore, the Analysis of Variance (ANOVA) testing indicates that the results are significant at relatively high confidence levels. The spectral-based method performed in this study provides fast, flexible, and non-destructive water stress measurements of grapevines when compared to classical methods.

Anahtar Kelimeler

• Grapevine
• Water Stress
• Hyperspectral Remote Sensing

Asma Su Stresinin Hiperspektral Analizi

Öz

Bağcılık, ürün kalitesi ve üzüm bağlarının verimliliği ile ilgili tüm çevresel faktörlerden etkilenen ve hayati bir etken olan su stresine son derece duyarlıdır. Bu çalışmada, dokuz farklı asma türü için ben düşme ve hasat dönemlerindeki asma su stresi spektroradyometrik ölçümlerle incelenmiştir. Arazi ölçümleri ile elde edilen yaprak su potansiyeli (LWP) değerleri ile en ilişkili dalga boylarını bulmak için orijinal spektrum temelli (OSB) ve sürekliliği kaldırılmış spektrum temelli (CRSB) eğriler korelasyon ve regresyon analizi ile analiz edilmiştir. Analizler hem ölçüm tarihleri için ayrı ayrı değerlendirmeler ile (yani 08.08.2012 ve 06.09.2012) hem de tüm ölçüm verileri için toplam tek bir veri seti olacak şekilde iki farklı yaklaşım ile gerçekleştirilmiştir. Ölçümlerin ayrı ayrı analizi, “kırmızı kenar bölgesini” önemli bir su stres göstergesi olarak ortaya çıkarmıştır. En yüksek korelasyonun R = 0.988 değeri ile CRSB eğrisinin 684 nm dalga boyu olduğu belirlenmiştir. Tüm ölçümlerin bir arada değerlendirildiği su stresi analizi için “mor ve yeşil bölgeleri” en iyi göstergeler olarak tespit edilmiştir. En yüksek korelasyonun R = 0.820 değeri ile OSB eğrisinin 410 nm dalga boyu olduğu belirlenmiştir. Ayrıca, Analysis of Variance (ANOVA) testinin sonuçları bu çalışmada elde edilen bulguların yüksek güven seviyelerinde anlamlı olduğunu göstermektedir. Bu çalışmada gerçekleştirilen spektral tabanlı yöntem, üzüm bağlarının / asma su stresi ölçmelerini klasik yöntemlere kıyasla hızlı, esnek ve tahribatsız bir şekilde sağlamaktadır.

Anahtar Kelimeler

• Asma
• Su stresi
• Hiperspektal uzaktan algılama

Kaynakça

1. Australian Radiation Protection and Nuclear Safety Agency (ARPANSA)., 2013. Radiation Protection - Solar UV radiation and the UV Index. http://www.arpansa.gov.au/radiationprotection/factsheets/is_UVindex.cfm
2. Bertamini, M. and Nedunchezhian, N., 2003. Photosynthetic functioning of individual grapevine leaves (Vitis vinifera L. cv. Pinot noir) during ontogeny in the field. Vitis 42 (1), 13–17.
3. Blackburn, G.A., 2007. Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany, 58(4), 855-867, doi: 10.1093/jxb/erl123
4. Broge, N.H. and 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. Remote Sensing of Environment, 76(2), 156-172, http://dx.doi.org/10.1016/S0034-4257(00)00197-8
5. Borengasser, M., Hungate, W.S. and Watkins, R., 2004. Hyperspetral Remote Sensing. CRC Press, 1st ed., Florida. Canadian Centre for Occupational Health and Safety (CCOHS)., 2013. Ultraviolet Radiation. http://www.ccohs.ca/oshanswers/phys_agents/ultravioletradiation.html
6. Carter, G.A. and Miller, R.L., 1994. Early Detection of Plant Stress by Digital Imaging within Narrow Stress-Sensitive Wavebands. Remote Sensing of Environment, 50(3), 295-302, ttp://dx.doi.org/10.1016/0034-4257(94)90079-5
7. Carbonneau, A., 1998. Aspects Qualitatifs. 258-276. In: Tiercelin, JR (Ed.), Traite d’irrigation. Tec&Doc. Lavosier Ed., Paris, 1011 p.
8. Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S. and Gregoire, J.M., 2001. Detection vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 77(1), 22-33, http://dx.doi.org/10.1016/S0034-4257(01)00191-2

9. Chappelle, E.W., Kim, M.S. and McMurtrey, J.E., 1992. Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sensing of Environment, 39(3), 239-247, http://dx.doi.org/10.1016/0034-4257(92)90089-3
10. Cifre, J., Bota, J., Escalona, J.M., Medrano, H. and Flexas, J., 2005. Physiological tools for irrigation scheduling in grapevine (Vitis vinifera L.): an open gate to improve water-use efficiency? Agriculture Ecosystems and Environment, 106(2005), 159–170, doi:10.1016/j.agee.2004.10.005
11. Clark, R. N., 1999. Chapter 1: Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy, in Manual of Remote Sensing, Remote Sensing for the Earth Sciences, (A.N. Rencz, ed.) John Wiley and Sons, New York, 3, 3- 58
12. Çamoğlu, G., Demirel, K., Genc, L., 2018. Use of Infrared Thermography and Hyperspectral Data to Detect Effects of Water Stress on Pepper. Quantitative InfraRed Thermography Journal. 15(1):81-94.
13. Çamoğlu G., Akçal A., Demirel K., Genç L., 2019a. Su Stresinin Sofralık Domatesin Verimi ve Fizyolojik Özellikleri Üzerine Etkileri. Bursa Uludağ Üniversitesi Ziraat Fakültesi Dergisi, vol.33, pp.15-30,
14. Çamoğlu G., Demirel K., Genç L., 2019b. Termal Kamera ve NDVI Sensörü kullanılarak Domatesin Fizyolojik Özelliklerinin tahminlenmesi. Harran Tarım ve Gıda Bilimleri Dergisi, cilt.23, ss.78-89,
15. De Bei, R., Cozzolino, D., Sullivan, W., Cynkar, W., Fuentes, S., Dambergs, R., Pech, J. and Tyerman, S., 2011. Non-destructive measurement of grapevine water potential using near infrared spectroscopy. Australian Journal of Grape and Wine Research, 17(1), 62–71, doi: 10.1111/j.1755-0238.2010.00117.x
16. Deloire, A. and Carbonneau, A., Wang, Z. and Ojeda, H., 2004. Vine and water, a short review. Journal International des Sciences de la Vigne et du Vin, 38(1), 1-13.
17. Demirel K., Çamoğlu G., Genç L., Kizil Ü., 2014. The Variation of Plant Stress Indicators and Some Traits Under Different Irrigation and Nitrogen Levels In The Rocket. Fresenius Environmental Bulletin, vol.23, pp.1238-1248.
18. Demirel K., Çamoğlu G., Akçal A. (2018). Effect of Water Stress on Four Varieties of Gladiolus. Fresenius Environmental Bulletin, vol.27, no.12A/2018, pp.9300-9307.
19. Durgut, M.R. and Arın, S., 2005. Level and Problems of Trakya Region Vineyard Mechanization, Journal of Tekirdag Agricultural Faculty, 2(3), 287-297
20. Eamus, D. and Shanahan, S.T., 2002. A rate equation model of stomatal responses to vapour pressure deficit and drought. BMC Ecology. 2(8), 1-14, doi: 10.1186/1472-6785-2-8
21. Eitel, J.U.H., Gessler, P.E., Smith, A.M.S. and Robberecht, R., 2006. Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp.. Forest Ecology and Management, 229(1-3), 170–182, http://dx.doi.org/10.1016/j.foreco.2006.03.027
22. Ferrini, F., Mattii, G.B. and Nicese, F.P., 1995. Effect of temperature on key physiological responses of grapevine leaf. American Journal of Enology and Viticulture, 46(3): 375-379.
23. Filella, I. and Penuelas, J., 1994. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15(7), doi: 10.1080/0143116940895417
24. Fitzgerald, G.J., Rodriguez, D., Christensen, L.K., Belford, R., Sadras, V.O. and Clarke, T. R., 2006. Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments. Precision Agriculture, 7(4), 233-248, doi:10.1007/s11119-006-9011-z
25. Gao, B., 1996. NDWI a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266, http://dx.doi.org/10.1016/S0034-4257(96)00067-3
26. Govender, M., Dye, P., Weiersbye, I., Witkowski, E. and Ahmed, F., 2009. Review of commonly used remote sensing and ground-based technologies to measure plant water stress. Water SA, 35(5), 741-752, http://dx.doi.org/10.4314/wsa.v35i5.49201
27. Greenspan, M. D., Schultz, H. R. and Matthews, M. A., 1996. Field evaluation of water transport in grape berries during water deficits. Physiologia Plantarum, 97(1), 55–62, doi: 10.1111/j.1399-3054.1996.tb00478.x
28. Greer, D.H. and Weedon, M.M., 2012. Interactions between light and growing season temperatures on, growth and development and gas exchange of Semillon (Vitis vinifera L.) vines grown in an irrigated vineyard. Plant Physiology and Biochemistry, 54, 59-69. http://dx.doi.org/10.1016/j.plaphy.2012.02.010
29. Gutierrez, M., Reynolds, M.P., and Klatt, A.R., 2010. Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes. Journal of Experimental Botany, 61(12), 3291–3303, doi: 10.1093/jxb/erq156
30. Hunt, E.R. and Rock, B.N., 1989. Detection of changes in leaf water content using near- and middle infrared reflectances. Remote Sensing of Environment, 30(1), 43-54, http://dx.doi.org/10.1016/0034-4257(89)90046-1
31. İnce C., Özelkan E., Kaya Ş., 2014. Assessment of Thyme Reduction Using Multitemporal Satellite Data and In-Situ Spectroradiometric Measurement: Altioluk Plateau. Kocaeli-Turkey", FRESENIUS ENVIRONMENTAL BULLETIN, vol.23, pp.3007-3012.
32. Jayaraman, V. and Srivastava, S.K., 2002. The invariance of red-edge inflection wavelengths derived from ground based spectro-radiometer and space-borne IRS-P3: MOS-B data. International Journal of Remote Sensing, 23(14), 2741-2765, DOI:10.1080/014311602760128125
33. Kakani, V.G., Reddy, K.R. and Zhao, D., 2007 Deriving a simple spectral reflectance ratio to determine cotton leaf water potential. Journal of New Seeds, 8(3), 11-27, doi:10.1300/J153v08n03_02
34. Kennedy, J.A., Matthews, M.A., and Waterhouse A.L., 2002. Effect of maturity and vine water status on grape skin and wine flavonoids. American Journal of Enology and Viticulture, 53(4), 268-274.
35. Kokaly, R.F. and Clark, R.N., 1999. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3), 267-287, http://dx.doi.org/10.1016/S0034-4257(98)00084-4
36. Kokaly, R.F., 2001. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment, 75(2), 153-161, http://dx.doi.org/10.1016/S0034-4257(00)00163-2
37. Lambers, H.F., Chapin, F.S. and Pons, T.L., 2008. Plant physiological ecology. 2nd Edition. Springer Sciences Business Media, LLC, doi: 10.1007/978-0-387-78341-3
38. Liu, L., Zhao, J. and Guan, L., 2013. Tracking photosynthetic injury of Paraquat-treated crop using chlorophyll fluorescence from hyperspectral data. European Journal Of Remote Sensing, 46, 459-473, doi: 10.5721/EuJRS20134627
39. Mohan, B.K., 2008. Hyperspectral Image Processing. ISRS Pre-Symposium Tutorial on “Hyperspectral Data Analysis and Applicaitons”, December 16-17, 2008, SAC, Ahmedabad.
40. Mutanga, O. and Skidmore, A.K., 2003. Continuum-removed absorption features estimate tropical savanna grass quality in situ. 3rd EARSEL Workshop on Imaging Spectroscopy, Herrsching, 13-16 May 2003, 542-558. http://www.itc.nl/library/papers_2003/peer_ref_conf/mutanga.pdf
41. Naor, A., Bravado, B. and Gelobter, J., 1994. Gas exchange and water relations in field- grown ‘Sauvignon blanc’ grapevines. American Journal of Enology and Viticulture, 45(4), 423-428.
42. Ozelkan E., Karaman M., Candar S., Coskun Z., Ormeci C., 2015. Investigation of grapevine photosynthesis using hyperspectral techniques and development of hyperspectral band ratio indices sensitive to photosynthesis. Journal of Environmental Biology, vol.36, pp.91-100.
43. Ozelkan, E., Chen, G., Ustundag, B.B., 2016. Multiscale_object-based drought monitoring and comparison in rainfed and irrigated agriculture from Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation, 44: 159-170. https://doi.org/10.1016/j.jag.2015.08.003
44. Peñuelas, J., Filella, I., Biel, C., Serrano, L. and Savé, R., 1993. The Reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 14(10), doi:10.1080/01431169308954010
45. Rodríguez-Pérez J.R., Riaño D., Carlisle E., Ustin S., and Smart D.R., 2007. Evaluation of hyperspectral reflectance indexes to detect grapevine water status in vineyards. American Journal of Enology and Viticulture, 58(3), 302–317.
46. Ritchie, G.A., Hinckley, T.M., 1975. The pressure chamber as an instrument for ecological research. Advances in Ecological Research, 9, 165–254.
47. Scholander, P.F., Hammel, H.T., Bradstreet, E.A. and Hemmingsen, E.A., 1965. Sap pressure in vascular plants. Science, 148(3668), 339-346, doi:10.1126/science.148.3668.339
48. Schultz, H.R. and M.A. Matthews., 1993. Growth, osmotic adjustment, and cell-wall mechanics of expanding grape leaves during water deficits. Crop Science, 33(2), 287-294, doi:10.2135/cropsci1993.0011183X003300020015x
49. Sims, D.A. and Gamon, J.A., 2002. - Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2-3), 337– 354, http://dx.doi.org/10.1016/S0034-4257(02)00010-X
50. Sims, D.A. and Gamon, J.A., 2003. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment, 84(4), 526–537, http://dx.doi.org/10.1016/S0034-4257(02)00151-7
51. Schmidt, K.S. and Skidmore, A.K., 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment, 85(1), 92 – 108, http://dx.doi.org/10.1016/S0034-4257(02)00196-7
52. Sensoy, S., Demircan, M., Ulupinar, Y. and Balta, Z., 2008. Climate of Turkey. http://www.mgm.gov.tr/FILES/iklim/turkiye_iklimi.pdf
53. Smith, R. and Prichard, T., 2003. Using a Pressure Chamber in Winegrapes. UC Cooperative Extension, http://cesonoma.ucdavis.edu/files/27409.pdf
54. Stimson, H.C., Breshears, D.D., Ustin, S.L. and Kefauver, C., 2005. Spectral sensing of foliar water conditions in two co-occur- ring conifer species: Pinus edulis and Juniperus monosperma. Remote Sensing of Environment, 96(1), 108-118, http://dx.doi.org/10.1016/j.rse.2004.12.007
55. Tucker, C.J., 1980. Remote sensing of leaf water content in the near infrared. Remote Sensing of Environment, 10(1), 23-32, http://dx.doi.org/10.1016/0034-4257(80)90096-6
56. Turner, N.C., 1981. Techniques and experimental approaches for the measurement of plant water status. Plant and Soil, 58(1-3), 339-366, doi:10.1007/BF02180062
57. Turner, N.C., 1988. Measurement of plant water status by the pres- sure chamber technique. Irrigation Science, 9(4), 289-308, doi: 10.1007/BF00296704
58. Turkish State Meteorological Service (TSMS), 2013. Ozone and UV, http://www.mgm.gov.tr/arastirma/ozon-ve-uv.aspx?s=uv Uzun, I., 2004. Viticulture Handbook. Hasad Publications, 1st edition.
59. Williams, L.E. and Araujo, F.J., 2002. Correlations among Predawn Leaf, Midday Leaf, and Midday Stem Water Potential and their Correlations with other Measures of Soil and Plant Water Status in Vitis vinifera. Journal of American Society for Horticulture Science, 127(3), 448-454.
60. Zarco-Tejada, P.J., Miller, J.R., Mohammed, G.H. and Noland, T.L., 2000. Chlorophyll fluorescence effects on vegetation appar- ent reflectance. I. Leaf-level measurements and model simulation. Remote Sensing of Environment, 74(3), 582-595, http://dx.doi.org/10.1016/S0034-4257(00)00148-6