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Validation of the cotton crop coefficient estimation model based on Sentinel-2 imagery and eddy covariance measurements

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

Abstract

New technologies are needed in order to improve water use efficiency and aid irrigation management. To meet this challenge, crop coefficient (Kc) estimation models based on remotely sensed crop reflectance were developed using new public domain satellite imagery. 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 during two seasons using the eddy covariance method. 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 21 vegetation indices based on the sensor’s unique spectral bands. Empirical Kc – vegetation index models were derived and ranked according to their prediction error. This was performed for each season separately and in addition cross-validation between the seasons was performed. In accordance with previous findings, we found strong correlations between Kc and indices that are based on the red and red-edge bands (MTCI, REP, and S2REP). In addition, other spectral indices that are strongly correlated to Kc were identified. The Merris Terrestrial Chlorophyll Index (MTCI) had the closest relation to Kc (R2 > 0.91). It was also demonstrated that the model developed for the first season can be successfully applied to the second season data to predict Kc, and vice-versa with RMSE < 0.1. Therefore this study cross-validates the models for estimating cotton water consumption using satellite imagery that is available at no cost at a temporal resolution of five days and a spatial resolution of 10–20 m. The confidence inspired by this validation sets the scene for near-real-time irrigation decision support systems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agiwat:v:223:y:2019:i:c:63
    DOI: 10.1016/j.agwat.2019.105715
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    References listed on IDEAS

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    1. Duchemin, B. & Hadria, R. & Erraki, S. & Boulet, G. & Maisongrande, P. & Chehbouni, A. & Escadafal, R. & Ezzahar, J. & Hoedjes, J.C.B. & Kharrou, M.H. & Khabba, S. & Mougenot, B. & Olioso, A. & Rodrig, 2006. "Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices," Agricultural Water Management, Elsevier, vol. 79(1), pages 1-27, January.
    2. Christoph Schmitz & Hans van Meijl & Page Kyle & Gerald C. Nelson & Shinichiro Fujimori & Angelo Gurgel & Petr Havlik & Edwina Heyhoe & Daniel Mason d'Croz & Alexander Popp & Ron Sands & Andrzej Tabea, 2014. "Land-use change trajectories up to 2050: insights from a global agro-economic model comparison," Agricultural Economics, International Association of Agricultural Economists, vol. 45(1), pages 69-84, January.
    3. 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.
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    1. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
    2. 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.
    3. De Caro, Dario & Ippolito, Matteo & Cannarozzo, Marcella & Provenzano, Giuseppe & Ciraolo, Giuseppe, 2023. "Assessing the performance of the Gaussian Process Regression algorithm to fill gaps in the time-series of daily actual evapotranspiration of different crops in temperate and continental zones using gr," Agricultural Water Management, Elsevier, vol. 290(C).
    4. 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).
    5. 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.
    6. 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).
    7. Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).

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