Published February 2022 | Version v1
Journal article Open

Potential of functional analysis applied to Sentinel-2 time-series to assess relevant agronomic parameters at the within-field level in viticulture

Authors/Creators

  • 1. ROR icon Instituto Tecnológico Agrario de Castilla y León
  • 2. ROR icon Institut Agro Montpellier

Description

Sentinel-2 satellite imagery offers a wealth of spectral information combined with a weekly temporal resolution. It is seen as a promising tool to extract spatial information about vineyards and link them to agronomic parameters. Usually, only one or a few images are commonly employed at specific stages like veraison in viticulture. Extracting further information from time-series images may be of interest; however, this remains an issue due to the noisy and complex nature of extracted time-series. The functional analysis proposes a robust continuous representation of these time-series, which can then be used with adapted statistical tools. This paper focuses on extracting relevant information at the within-field level on two vineyards in Spain, which can be jointly interpreted with field observations and measurements. More precisely, it discusses the use of popular linear dimensionality reduction techniques, namely Principal Component Analysis (PCA) and Partial Least Square (PLS), adapted to functional data in order to decompose NDVI time-series into a weighted sum of several functional components. The unsupervised methods, like PCA, decomposed the spatial structure within the vineyards using a few components, resulting in a better and more manageable dataset than the one obtained using simple non-constrained methods. The results show significant correlations with ground-truth data showing the added value of considering the whole NDVI temporal series compared to a single NDVI map at veraison. The proposed approach provided helpful information about each component's yearly trend. Moreover, the results are linked to grapevines' seasonal phenology and management practices, highlighting phenomena affecting the vineyard's development. This method is particularly suited for interactions with field experts, who may derive relevant agronomic information from the decomposition maps.
 

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Additional details

Funding

Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
FPI predoctoral contract FPI-INIA2016-017

References

  • Anastasiou, E., Balafoutis, A., Darra, N., Psiroukis, V., Biniari, A., Xanthopoulos, G. & Fountas, S. (2018). Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes. Agriculture, 8, 94, doi:10.3390/agriculture8070094.
  • Borgogno-Mondino, E., Novello, V., Lessio, A., Tarricone, L. & de Palma, L. (2018). Intra-vineyard variability description through satellite-derived spectral indices as related to soil and vine water status. Acta Horticulturae, 1197, 59–68, doi:10.17660/ActaHortic.2018.1197.8.
  • Box G.E.P., Jenkins G.M., Reinsel G.C. & Ljung G.M. (2015). Time Series Analysis: Forecasting and Control, 5th Edition. Published by John Wiley and Sons Inc., Hoboken, New Jersey, pp. 712. ISBN: 978-1-118-67502-1
  • Cardot H., Ferraty F. & Sarda P. (1999). Functional Linear Model. Statistics & Probability Letters, 45(1), 11–22.
  • Carreño-Conde, F., Sipols, A. E., de Blas, C. S., & Mostaza-Colado, D. (2021). A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI). Applied Sciences, 11(4), 1859. https://doi.org/10.3390/app11041859
  • Devaux, N., Crestey, T., Leroux, C. & Tisseyre, B. (2019). Potential of Sentinel-2 satellite images to monitor vine fields grown at a territorial scale. OENO One 53. https://doi.org/10.20870/oeno-one.2019.53.1.2293
  • Di Gennaro, S.F., Dainelli, R., Palliotti, A., Toscano, P. & Matese, A. (2019). Sentinel-2 Validation for Spatial Variability Assessment in Overhead Trellis System Viticulture Versus UAV and Agronomic Data. Remote Sensing, 11, 2573. https://doi.org/10.3390/rs11212573
  • Emilien, A.V., Thomas, C., & Thomas, H. (2021). UAV & satellite synergies for optical remote sensing applications: A literature review. Science of Remote Sensing, 3, 100019. https://doi.org/10.1016/j.srs.2021.100019
  • European Space Agency (ESA) (2015). SENTINEL-2 User Handbook, ESA Standard Document. Available Online: https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook (accessed on March 30, 2021).
  • Febrero-Bande M., Galeano P. & González-Manteiga W. (2008). Outlier Detection in Functional Data by Depth Measures, with Application to Identify Abnormal NOx Levels. Environmetrics, 19(4), 331–345. https://doi.org/10.1002/env.878
  • Febrero-Bande M. & Oviedo de la Fuente M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1–28. https://doi.org/10.18637/jss.v051.i04
  • Ferraty, F. & Vieu, P. (2006). Non-parametric functional data analysis. Springer Series in Statistics, New York.
  • Fountas, S., Anastasiou, E., Balafoutis, A., Koundouras, S., Theoharis, S. & Theodorou, N. (2014). The influence of vine variety and vineyard management on the effectiveness of canopy sensors to predict winegrape yield and quality. In Proceedings of the International Conference of Agricultural Engineering, Zurich, Switzerland, 6–10 July 2014.
  • Goldsmith J., Bobb J., Crainiceanu C., Caffo B. & Reich D (2011). Penalized Functional Regression. Journal of Computational and Graphical Statistics. https://doi.org/10.1198/jcgs.2010.10007
  • Hall, A., Lamb, D. W., Holzapfel, B. P., & Louis, J. P. (2010). Within-season temporal variation in correlations between vineyard canopy and winegrape composition and yield. Precision Agriculture, 12(1), 103–117. https://doi.org/10.1007/s11119-010-9159-4
  • Hidalgo, J. (2006), La calidad del vino desde el viñedo, Mundi-Prensa, Spain.
  • Hyndman, R., Koehler, A., Ord, K., & Snyder, R. (2008). Forecasting with Exponential Smoothing. Springer Series in Statistics. https://doi.org/10.1007/978-3-540-71918-2
  • Jackson, R. (2020). Wine Science: Principles and Applications, 5th ed., Elsevier: Cambridge.
  • Jolliffe I.T. (2002). Principal component analysis, 2nd edn New York, NY: Springer-Verlag.
  • Keller, M. (2015). The Science of Grapevines: Anatomy and Physiology. Second edition. Academic Press, Elsevier, Amsterdam.
  • Knipper, K.R., Kustas, W.P., Anderson, M.C., Alsina, M.M., Hain, C.R., Alfieri, J.G., Prueger, J.H., Gao, F., McKee, L.G. & Sanchez, L.A. (2019). Using High-Spatiotemporal Thermal Satellite ET Retrievals for Operational Water Use and Stress Monitoring in a California Vineyard. Remote Sensing, 11, 2124. https://doi.org/10.3390/rs11182124
  • Leng, X., & Müller, H. G. (2005). Classification using functional data analysis for temporal gene expression data. Bioinformatics, 22(1), 68–76. https://doi.org/10.1093/bioinformatics/bti742
  • Preda C., Saporta G. & Lévéder C.L. (2007). PLS classification of functional data. Comput. Stat, 22(2), 223–235. https://doi.org/10.1007/s00180-007-0041-4
  • Ramsay, J. & Silverman, B. (2005). Functional Data Analysis, 2nd Edition, Springer, New York. https://doi.org/10.1007/b98888
  • Reynolds, A.G. (2010). Managing Wine Quality. Volume 1: Viticulture and wine quality. Woodhead Publishing.
  • Rouse, J.W., Jr., R.H. Haas, J.A. Schell, & D.W. Deering. (1973). Monitoring vegetation systems in the Great Plains with ERTS. ERTS Symp. 3rd, Washington, DC. 10–14 Dec. 1972. NASA SP-351. Vol. I:309–317. NASA, Washington, DC
  • Sozzi, M., Kayad, A., Marinello, F., Taylor, J., & Tisseyre, B. (2020). Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform. OENO One, 54(2), 189–197. https://doi.org/10.20870/oeno-one.2020.54.1.2557
  • Sun, L., Gao, F., Anderson, M.C., Kustas, W.P., Alsina, MM, Sanchez, L., Sams, B., McKee, L., Dulaney, W. & White, W.A. (2017). Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards. Remote Sensing, 9, 317, doi:10.3390/rs9040317.
  • Tisseyre, B., Mazzoni, C., & Fonta, H. (2008). Whithin-field temporal stability of some parameters in Viticulture: potential toward a site specific management. OENO One, 42(1), 27–39. https://doi.org/10.20870/oeno-one.2008.42.1.834
  • Urretavizcaya, I., Royo, J. B., Miranda, C., Tisseyre, B., Guillaume, S., & Santesteban, L. G. (2017). Relevance of sink-size estimation for within-field zone delineation in vineyards. Precision Agriculture, 18(2), 133-144. https://doi.org/10.1007/s11119-016-9450-0
  • Vélez, S., Rubio, J.A., Andrés, M.I. & Barajas, E. (2019). Agronomic classification between vineyards ('Verdejo') using NDVI and Sentinel-2 and evaluation of their wines. Vitis Journal of Grapevine Research, 58, 33–38, doi:10.5073/vitis.2019.58.special-issue.33-38.
  • Vélez, S., Barajas, E., Rubio, J.A., Vacas, R. & Poblete-Echeverría, C. (2020). Effect of Missing Vines on Total Leaf Area Determined by NDVI Calculated from Sentinel Satellite Data: Progressive Vine Removal Experiments. Applied Sciences, 10, 3612. https://doi.org/10.3390/app10103612
  • Vodyanitskii, Y. N., & Savichev, A. T. (2017). The influence of organic matter on soil color using the regression equations of optical parameters in the system CIE- L*a*b*. Annals of Agrarian Science, 15(3), 380–385. https://doi.org/10.1016/j.aasci.2017.05.023
  • White, R.E. (2015). Understanding Vineyard Soils, Second edition., Oxford University Press: Oxford.
  • Yang, X. D., Wang, J., Xu, M. S., Ali, A., Xu, Y., Lamb, D., Duan, L. C., Yan, K. H., & Yang, S. T. (2019). Effects of the ephemeral stream on plant species diversity and distribution in an alluvial fan of arid desert region: An application of a low altitude UAV. PLOS ONE, 14(2), e0212057. https://doi.org/10.1371/journal.pone.0212057