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Integrating forecast meteorological data into the ArcDualKc model for estimating spatially distributed evapotranspiration rates of a citrus orchard

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  • Longo-Minnolo, G.
  • Vanella, D.
  • Consoli, S.
  • Intrigliolo, D.S.
  • Ramírez-Cuesta, J.M.

Abstract

In the last years, several satellite-based models, using measured or forecast meteorological data, have been developed for determining spatially distributed crop evapotranspiration (ETc) estimates. The study herein presented aims at assessing the reliability of the ArcDualKc model, based on the FAO-56 dual crop coefficient (Kc), for deriving spatially distributed estimates of Kc and ETc in a deficit irrigated (DI) orange orchard. Daily ETc and dual-Kc values were obtained using Sentinel-2 satellite imagery in combination with measured and forecast meteorological data provided by the nearest weather station and by COSMO model, respectively. Overestimations on Kc and ETc resulted from the study when using forecast instead of measured meteorological data (average PBIAS of 7.62% and 26.24%, respectively); this might be caused by some inaccuracies in meteorological predictions. The evaporation coefficient (Ke) derived by the ArcDualKc model resulted similar between the DI and the fully irrigated treatments. Differences up to 6% in terms of Kc and ETc were identified among the irrigation strategies, mainly due to variations of the transpiration coefficient (Kcb). Despite the reliability of the ArcDualKc model, the obtained results might be influenced by the heterogeneity of Sentinel-2 pixel, containing vegetated and bare soil surfaces, highlighting the need to integrate the model with ground-based data or by using higher-resolution images.

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  • Longo-Minnolo, G. & Vanella, D. & Consoli, S. & Intrigliolo, D.S. & Ramírez-Cuesta, J.M., 2020. "Integrating forecast meteorological data into the ArcDualKc model for estimating spatially distributed evapotranspiration rates of a citrus orchard," Agricultural Water Management, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:agiwat:v:231:y:2020:i:c:s0378377419312491
    DOI: 10.1016/j.agwat.2019.105967
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    2. Abou Ali, Asma & Bouchaou, Lhoussaine & Er-Raki, Salah & Hssaissoune, Mohammed & Brouziyne, Youssef & Ezzahar, Jamal & Khabba, Saïd & Chakir, Adnane & Labbaci, Adnane & Chehbouni, Abdelghani, 2023. "Assessment of crop evapotranspiration and deep percolation in a commercial irrigated citrus orchard under semi-arid climate: Combined Eddy-Covariance measurement and soil water balance-based approach," Agricultural Water Management, Elsevier, vol. 275(C).
    3. Chen, Mengting & Cui, Yuanlai & Wang, Xiaonan & Xie, Hengwang & Liu, Fangping & Luo, Tongyuan & Zheng, Shizong & Luo, Yufeng, 2021. "A reinforcement learning approach to irrigation decision-making for rice using weather forecasts," Agricultural Water Management, Elsevier, vol. 250(C).
    4. Saitta, Daniela & Consoli, Simona & Ferlito, Filippo & Torrisi, Biagio & Allegra, Maria & Longo-Minnolo, Giuseppe & Ramírez-Cuesta, Juan Miguel & Vanella, Daniela, 2021. "Adaptation of citrus orchards to deficit irrigation strategies," Agricultural Water Management, Elsevier, vol. 247(C).
    5. 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).

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