Abstract
Management zones (MZs) are efficient for applying site-specific management in agricultural fields. This study proposes an approach for generating MZs using time-series clustering (TSC) to also enable time-specific management. TSC was applied to daily remote sensing retrievals in a California vineyard during four growing seasons (2015–2018) using three datasets: evapotranspiration (ET), leaf area index (LAI), and normalized difference vegetation index (NDVI). Distinct MZs were delineated based on similarities in pixel-level temporal dynamics for each dataset, using dissimilarity index to determine the optimal number of clusters and compare TSC results. The differences between the cluster centers were calculated, along with the ratio between the centers’ differences and the range of each dataset, denoting the degree of difference between MZs centers. Similarity between MZs from each factor was quantified using Cramer’s V and Fréchet distances. Finally, an aggregated (multi-factor) MZ map was generated using multivariate clustering. The resulting MZs were compared to a 2016 yield map to determine the significance of differences between means and distribution among MZs. The findings show that LAI TSC achieved the best cluster separation. The NDVI and LAI MZs maps were nearly identical (Cramer’s V of 0.97), while ET showed weaker similarities to NDVI and LAI (0.61 and 0.62, respectively). Similar findings were observed for the Fréchet distances. The yield values were found to be significantly different among MZs for all TSC maps. TSC may be further utilized for defining within-field spatial variability and temporal dynamics for precision irrigation practices that account for spatial and temporal variability.
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Acknowledgements
The authors wish to thank the staff of Viticulture, Chemistry and Enology Division of E&J Gallo Winery for logistical support and funding of GRAPEX field and research activities and insight to local irrigation practices. The authors appreciate the cooperation of Mr. Ernie Dosio of Pacific Agri Lands Management, along with the Sierra Loma vineyard staff, providing access to the vineyard and logistical support of GRAPEX field measurement activities. This research was supported in part by the U.S. Department of Agriculture, Agricultural Research Service. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.
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Funding provided by E. & J. Gallo Winery made possible the acquisition and processing of the vineyard field data and the financial support for the GRAPEX project by a grant from the NASA Applied Sciences–Water Resources Program (Grant Award NNH17AE39I) supported the development of the satellite-based ET product used in this analysis.
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NO-L: conceptualization, software, methods, formal analysis, interpretation, writing, and editing; FG: data collection, conceptualization, methods, software, interpretation, writing, and editing; KK: data collection, conceptualization, methods, software, interpretation, and editing; WPK: conceptualization, data collection, methods, interpretation, and funding acquisition; MCA: data collection and methods; MMA: data collection and interpretation; LAS: data collection; AK: conceptualization, interpretation, writing, and editing.
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Ohana-Levi, N., Gao, F., Knipper, K. et al. Time-series clustering of remote sensing retrievals for defining management zones in a vineyard. Irrig Sci 40, 801–815 (2022). https://doi.org/10.1007/s00271-021-00752-0
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DOI: https://doi.org/10.1007/s00271-021-00752-0