Hyperspectral remote sensing of grapevine drought stress
- 19 Downloads
In karst landscapes stony soils have little water holding capacity; the rational use of water for irrigation therefore plays an important management role. Because the water holding capacity is not homogenous, precision agriculture approaches would enable better management decisions. This research was carried out in an experimental vineyard grown in an artificially transformed karst terrain in Dalmatia, Croatia. The experimental design included four water treatments in three replicates: (1) fully irrigated, based on 100% crop evapotranspiration (ETc) application (N100); (2 and (3) deficit irrigation, based on 75% and 50% ETc applications (N75 and N50, respectively); and (4) non-irrigated (N0). Hyperspectral images of grapevines were taken in the summer of 2016 using two spectral-radiance (W sr−1 m−2) calibrated cameras, covering wavelengths from 409 to 988 nm and 950 to 2509 nm. The four treatments were grouped into a new set consisting of: (1) drought (N0); and (2) irrigated (the remaining three treatments: N100, N75, and N50). The images were analyzed using Partial Least Squares-Discriminant Analysis (PLS-DA), and treatments were classified using PLS-Single Vector Machines (PLS-SVM). PLS-SVM demonstrated the capability to determine levels of grapevine drought or irrigated treatments with an accuracy of more than 97%. PLS-DA identified relevant wavelengths, which were linked to O–H, C–H, and N–H stretches in water, carbohydrates and proteins. The study presents the applicability of hyperspectral imaging for drought stress assessment in grapevines, even though temporal variability needs to be taken into account for early detection.
KeywordsVineyard Irrigation Water stress Hyperspectral imagery Soil Precision agriculture
This research has been partially supported in part by Croatian Science Foundation under the Project IP-2016-06-8379, SENSIRRIKA - Advanced sensor systems for precision irrigation in karst landscape, and partially by the Croatian Waters, and by the Slovenian Research Agency (ARRS), Grant P4-0072.
- Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration—guidelines for computing crop water requirements—FAO Irrigation and drainage paper 56. Rome: Food and Agriculture Organization.Google Scholar
- Matese, A., & Di Genaro, S. F. (2014). Technology in precision viticulture: a state of the art review. International Journal of Wine Research, 7, 69–81.Google Scholar
- R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2015. https://www.R-project.org/.
- Rodriguez-Perez, J. R., Riano, D., Carlisle, E., Ustin, S., & 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.Google Scholar
- Steele, M. R., Gitelson, A. A., Rundquist, D. C., & Merzlyak, M. N. (2009). Nondestructive estimation of anthocyanin content in grapevine leaves. American Journal of Enology and Viticulture, 60, 87–92.Google Scholar
- Susič, N., Žibrat, U., Širca, S., Strajnar, P., Razinger, J., Knapič, M., et al. (2018). Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging. Sensors and actuators B: Chemical, 273, 842–852. https://doi.org/10.1016/j.snb.2018.06.121.CrossRefGoogle Scholar
- Vadez, V., Kholova, J., Choudhary, S., Zindy, P., Terrier, M., Krishnamurth, L., et al. (2011). Responses to increased moisture stress and extremes: Whole plant response to drought under climate change. In S. S. Yadav, R. Redden, J. L. Hatfield, H. Lotze-Campen, & A. E. Hall (Eds.), Crop adaptation to climate change (pp. 186–197). Orford: Chichester-Wiley-Blackwell.CrossRefGoogle Scholar
- Zarco-Tejada, P. J., Berjon, A., Lopez-Lozano, R., Miller, J. R., Martın, P., Cachorro, V., et al. (2005). Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment, 99, 271–287.CrossRefGoogle Scholar