Evaluation of the Capability of Spectral Water Indices for Assessing Water stress in Olive Tree

Document Type : Research Paper

Authors

1 Ph. D. student, Irrigation and drainage department, Water science faculty, Shahid Chamran University, Ahwaz, Iran

2 Associate Professor, Irrigation and drainage department, Water science faculty, Shahid Chamran University, Ahwaz, Iran

3 Full Professor, irrigation and drainage department, Water science faculty, Shahid Chamran University, Ahwaz, Iran

4 Assistant professor, Horticulture science department, Agriculture College, Shahid Chamran University, Ahwaz, Iran

Abstract

Spectrometric measurements have the potential for fast and non-destructive measurements of plant water stress. The aim of this work was to investigate the ability of several spectral water indices, including water index (WI), normalized spectral water indices 1-5 (NWI 1-5), and normalized water index based on wavelengths in 960 and 940 nm (NWI 960-940) for detection of water stress in olive trees. The experimental treatments involved two olive cultivars (Koroneiki and T2) and four water regimes (100%, 85%, 70%, and 55% of crop water requirement). Results showed that the olive trees in different water supplies 85%, 70%, and 55% of ETc were subjected to soil moisture deficit equal to 11, 15, and 20%, respectively, as compared to soil moisture of control treatment. Because of the high resistance of olive trees to water stress, water reduction at levels of 15, 30, and 45 percent did not have significant effects on spectral indices. However, spectral indices were closely and significantly linear associated with relative water content of the crop leaf (). Among all tested water spectral indices, NWI-2 showed the least consistent associations with relative water content of the leaf (ranging from 1–23% less than the ones in other tested indices). Based on the average amount of spectral indices and relative water content during the study period, NWI4, NWI5, NWI1, WI, NWI960-940, NWI3, and NWI2 showed a stronger relationship with the relative water content of olive leaves, respectively. In conclusion, spectral reflectance indices, WI, NWI 1-5, and NWI 960-940, could be useful for fast and non-destructive estimating of plant water stress.

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References

Agam, N., Cohen, Y., Berni, J.A.J., Alchanatis, V., Cool, D., Dag, A., Yermiyahu, U. and Ben-Gal, A. (2013). An insight to the performance of crop water stress index for olive trees. Agricultural Water Management, 118, 79-86.
Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop Evapotranspiration. Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage. Paper No. 56, FAO, Rome, Italy, pp. 300.
Babar, M. A., Reynolds, M. P, van Ginkel, M., Klatt, A. R., Raun, W. R. and Stone, M. L. (2006). Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Science. 46, 578-588.
Bacelar, E. A., Santos, D. L., Moutinho-Pereira, J. M., Goncalves, B. C., Ferreira, H. F. and Correia, C. M. (2006). Immediate responses and adaptative strategies of three olive cultivars under contrasting water availability regimes, Changes on structure and chemical composition of foliage and oxidative damage. Plant Science, 170, 596-605.
Becker, E. and Schmidhalter, U. (2017). Evaluation of yield and drought using active and passive spectral sensing systems at the reproductive stage in wheat. Frontiers in Plant Science, 8, 1–15.
Berni, J. A. J, Zarco-Tejada, P. J., Sepulcre-Canto, G., Fereres, E. and Villalobos, F. (2009). Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sensing of Environment, 113, 2380-2388.
Cohen, Y., Alchanatis, V., Meron, M., Saranga, Y. and Tsipris, J. (2005). Estimation of leaf water potential by thermal imagery and spatial analysis. Journal of Experimental Botany, 56 (417), 1843–1852.
DeJonge, K. C, Taghvaeian, S., Trout, T. J. and Comas, L. H. (2015). Comparison of canopy temperature-based water stress indices for maize. Agricultural Water Management, 156, 51–62.
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, 170–182.
Elsayed, S. and Darwish, W. (2017). Hyperspectral remote sensing to assess the water status, biomass, and yield of maize cultivars under salinity and water stress. Bragantia, 76, 62–72.
Elsayed, S., Elhoweity, M., Ibrahim, H.H., Dewir, Y.H., Migdadi, H.M. and Schmidhalter, U. (2017). Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes. Agricultural Water Management, 189, 98-110.
Elsayed, S., Elhoweity, M. and Schmidhalter, U. (2015). Normalized difference spectral indices and partial least squares regression to assess the yield and yield components of peanut. Australian Journal of Crop Science, 9, 976-986.
Elsayed. S., Mistele, B. and Schmidhalter, U. (2011). Can changes in leaf water potential be assessed spectrally? Functional Plant Biology, 38, 523–533.
Fereres, E., Orgaz, F. and Gonzalez-Dugo, V. (2011). Reflections on food security under water scarcity. Journal of Experimental Botany, 62, 4079–4086.
Garces-Restrepo, C., Vermillion, D. and Munoz, G. (2007). Irrigation management transfer. Worldwide efforts and results. FAO, Rome, pp. 62.
Garcia-Tejero, I. F., Hernandez , A., Padilla-Diaz, C. M., Diaz-Espejo, A. and Fernandez, J. . (2017). Assessing plant water status in a hedgerow olive orchard from thermography at plant level. Agricultural Water Management, 188, 50-60.
Geerts, S. and Raes, D. (2009). Deficit irrigation as an on farm strategy to maximize crop water productivity in dry areas. Agricultural Water Management, 96, 1275–1284.
Gutierrez, M., Reynolds, M. P., Raun, W. R., Stone, M. L. and Klatt, A. R. (2010). Spectral water indices for assessing yield in elite bread wheat genotypes in well irrigated, water stressed, and high temperature conditions. Crop Science, 50, 197–214.
Kim, Y., Glenn, D. M., Park, J., Ngugi, H. K. and Lehman, B. L. )2011(. Hyperspectral image analysis for water stress detection of apple trees. Computers and Electronics in Agriculture, 77, 155-160.
Maki, M., Ishiahra, M. and Tamura, M. (2004) Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment, 90, 441-450.
Mangus, D. L, Sharda, A. and Zhang, N. (2016). Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse. Computers and Electronics in Agriculture, 121, 149-159.
Marino, G., Pallozzi, E., Cocozza, C., Tognetti, R., Alessio, G., Cantini, C. and Centritto, M. (2014). Assessing gas exchange, sap flow and water relations using tree canopy spectral reflectance indices in irrigated and rainfed Olea europaea L. Environmental and Experimental Botany, 99, 43-52.
Penuelas, J., Pinol, J., Ogaya, R. and Filella, I. (1997). Estimation of plant water concentration by the reflectance water index (R900/R970). International Journal of Remote Sensing, 18, 2869–2875.
Penuelas, J., Filella, I., Biel, C., Serrano, L. and Save, R. (1993). The reflectance at the 950–970 mm region as an indicator of plant water status. International Journal of Remote Sensing, 14,1887-1905.
Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S, Daughtry, C. S and Upchurch, D. R. (2003). Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing, 69(6), 647-664.
Prasad, B., Carver, B. F., Stone, M. L., Babar, M. A., Raun, W.R. and Klatt, A. R. (2007). Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under Great Plains conditions. Crop Science, 47, 1426–1440.
Pu, R., Ge, S., Kelly, N. and Gong, P. (2003). Spectral absorption features as indicators of water status in coast live oak (Quercus Agrifolia) leaves. International Journal of Remote Sensing. 24, 1799-1810.
Richards, R. A., Rebetzke, G. J, Watt, M., Condon, A., Spielmeyer, W. and Dolferus, R. (2010). Breeding for improved water productivity in temperate cereals, phenotyping, quantitative trait loci, markers and the selection environment. Functional Plant Biology, 37, 85–97.
Sims, D. and Gamon, J. (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, 526–537.
Schlemmer, M. R., Francis, D. D., Shanahan, J. F. and Schepers, J. S (2005). Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agronomy Journal, 97,106–112.
Iranian Journal of Soil and Water Research
Volume 51, Issue 1
April 2020
Pages 179-190

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