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Estimating cotton water consumption using a time series of Sentinel-2 imagery

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  • Rozenstein, Offer
  • Haymann, Nitai
  • Kaplan, Gregoriy
  • Tanny, Josef

Abstract

Crop coefficient (Kc)-based estimation of crop water consumption is one of the most commonly used methods for irrigation management. Spectral modeling of Kc is possible due to the high correlations between Kc and the crop phenologic development and spectral reflectance. In this study, cotton evapotranspiration was measured in the field using several methods, including eddy covariance, surface renewal, and heat pulse. Kc was estimated as the ratio between reference evapotranspiration and the measured cotton evapotranspiration. In addition, a time series of Sentinel-2 imagery was processed to produce 22 vegetation indices (VIs) based on the sensor’s unique spectral bands. Empirical Kc – VI models were derived and ranked according to their prediction error. In accordance with previous studies, we found a strong correlation between the normalized difference vegetation index (NDVI) and Kc (R2 = 0.94), and yet, we also identified other spectral indices that are more strongly correlated to Kc. The indices that were found to be the most suitable for Kc prediction were based on the red and red-edge bands (MTCI, REP, and S2REP). This progress in estimating cotton water consumption using satellite imagery that are available at no cost is a leap forward towards the development of crop irrigation requirements models. Consequently, this work sets the scene for near-real-time irrigation decision support systems.

Suggested Citation

  • Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2018. "Estimating cotton water consumption using a time series of Sentinel-2 imagery," Agricultural Water Management, Elsevier, vol. 207(C), pages 44-52.
    • Handle: RePEc:eee:agiwat:v:207:y:2018:i:c:p:44-52
    DOI: 10.1016/j.agwat.2018.05.017
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    Cited by:

    1. Kaplan, Gregoriy & Fine, Lior & Lukyanov, Victor & Malachy, Nitzan & Tanny, Josef & Rozenstein, Offer, 2023. "Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index," Agricultural Water Management, Elsevier, vol. 276(C).
    2. Eliav Shtull-Trauring & Asher Azenkot & Nirit Bernstein, 2022. "Translational Platform for Increasing Water Use Efficiency in Agriculture: Comparative Analysis of Plantation Crops," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 571-587, January.
    3. Gregoriy Kaplan & Offer Rozenstein, 2021. "Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2," Land, MDPI, vol. 10(5), pages 1-13, May.
    4. Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2019. "Validation of the cotton crop coefficient estimation model based on Sentinel-2 imagery and eddy covariance measurements," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    5. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    6. Jun Ma & Jianpeng Zhang & Jinliang Wang & Vadim Khromykh & Jie Li & Xuzheng Zhong, 2023. "Global Leaf Area Index Research over the Past 75 Years: A Comprehensive Review and Bibliometric Analysis," Sustainability, MDPI, vol. 15(4), pages 1-30, February.
    7. Munitz, Sarel & Schwartz, Amnon & Netzer, Yishai, 2019. "Water consumption, crop coefficient and leaf area relations of a Vitis vinifera cv. 'Cabernet Sauvignon' vineyard," Agricultural Water Management, Elsevier, vol. 219(C), pages 86-94.
    8. Pereira, L.S. & Paredes, P. & Hunsaker, D.J. & López-Urrea, R. & Mohammadi Shad, Z., 2021. "Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method," Agricultural Water Management, Elsevier, vol. 243(C).
    9. French, Andrew N. & Hunsaker, Douglas J. & Sanchez, Charles A. & Saber, Mazin & Gonzalez, Juan Roberto & Anderson, Ray, 2020. "Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest," Agricultural Water Management, Elsevier, vol. 239(C).
    10. Mahmoud, Shereif H. & Gan, Thian Yew, 2019. "Irrigation water management in arid regions of Middle East: Assessing spatio-temporal variation of actual evapotranspiration through remote sensing techniques and meteorological data," Agricultural Water Management, Elsevier, vol. 212(C), pages 35-47.
    11. Teixeira, Antônio & Leivas, Janice & Struiving, Tiago & Reis, João & Simão, Fúlvio, 2021. "Energy balance and irrigation performance assessments in lemon orchards by applying the SAFER algorithm to Landsat 8 images," Agricultural Water Management, Elsevier, vol. 247(C).
    12. Gregoriy Kaplan & Lior Fine & Victor Lukyanov & V. S. Manivasagam & Josef Tanny & Offer Rozenstein, 2021. "Normalizing the Local Incidence Angle in Sentinel-1 Imagery to Improve Leaf Area Index, Vegetation Height, and Crop Coefficient Estimations," Land, MDPI, vol. 10(7), pages 1-23, June.
    13. Pereira, L.S. & Paredes, P. & López-Urrea, R. & Hunsaker, D.J. & Mota, M. & Mohammadi Shad, Z., 2021. "Standard single and basal crop coefficients for vegetable crops, an update of FAO56 crop water requirements approach," Agricultural Water Management, Elsevier, vol. 243(C).

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