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A fully coupled crop-water-energy balance model based on satellite data for maize and tomato crops yield estimates: The FEST-EWB-SAFY model

Author

Listed:
  • Corbari, C.
  • Ben Charfi, I.
  • Al Bitar, A.
  • Skokovic, D.
  • Sobrino, J.A.
  • Perelli, C.
  • Branca, G.
  • Mancini, M.

Abstract

Agricultural crop management requires extensive and comprehensive tools that allow for a full knowledge of the crops’ status and growth dynamic. This study aims at estimating crop yield for maize and tomato crops over large areas at field scale. For this purpose, we developed a fully coupled model based on a parameter-saving crop growth model (Simple Algorithm For Yield estimates (SAFY)) with a water-energy balance model (Flash–flood Event–based Spatially–distributed rainfall–runoff Transformation- Energy Water Balance model (FEST-EWB)) with a double exchange of leaf area index (LAI) and soil moisture (SM) information. Both models are driven by remote sensing data and are calibrated independently from in situ measurements. Satellite LAI data are used to calibrate the crop growth model parameters, while the energy-water balance parameters are calibrated against satellite land surface temperature (LST) data. Multiple satellite data are used either at high spatial resolution (Sentinel 2 and LANDSAT 7 and 8) and at low-resolution (MODIS). Two Italian case studies are selected to test the model accuracy: the Chiese Irrigation Consortium (Northern Italy), mainly devoted to maize crop cultivation, and the Capitanata Irrigation Consortium (Southern Italy), where tomatoes are largely diffused.

Suggested Citation

  • Corbari, C. & Ben Charfi, I. & Al Bitar, A. & Skokovic, D. & Sobrino, J.A. & Perelli, C. & Branca, G. & Mancini, M., 2022. "A fully coupled crop-water-energy balance model based on satellite data for maize and tomato crops yield estimates: The FEST-EWB-SAFY model," Agricultural Water Management, Elsevier, vol. 272(C).
    • Handle: RePEc:eee:agiwat:v:272:y:2022:i:c:s0378377422003973
    DOI: 10.1016/j.agwat.2022.107850
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    References listed on IDEAS

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    1. Battude, Marjorie & Al Bitar, Ahmad & Brut, Aurore & Tallec, Tiphaine & Huc, Mireille & Cros, Jérôme & Weber, Jean-Jacques & Lhuissier, Ludovic & Simonneaux, Vincent & Demarez, Valérie, 2017. "Modeling water needs and total irrigation depths of maize crop in the south west of France using high spatial and temporal resolution satellite imagery," Agricultural Water Management, Elsevier, vol. 189(C), pages 123-136.
    2. Corbari, Chiara & Salerno, Raffaele & Ceppi, Alessandro & Telesca, Vito & Mancini, Marco, 2019. "Smart irrigation forecast using satellite LANDSAT data and meteo-hydrological modeling," Agricultural Water Management, Elsevier, vol. 212(C), pages 283-294.
    3. Li, Yan & Zhou, Qingguo & Zhou, Jian & Zhang, Gaofeng & Chen, Chong & Wang, Jing, 2014. "Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions," Ecological Modelling, Elsevier, vol. 291(C), pages 15-27.
    4. Lu, Yang & Chibarabada, Tendai P. & Ziliani, Matteo G. & Onema, Jean-Marie Kileshye & McCabe, Matthew F. & Sheffield, Justin, 2021. "Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model," Agricultural Water Management, Elsevier, vol. 252(C).
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    Cited by:

    1. Chiara Perelli & Giacomo Branca & Chiara Corbari & Marco Mancini, 2024. "Physical and Economic Water Productivity in Agriculture between Traditional and Water-Saving Irrigation Systems: A Case Study in Southern Italy," Sustainability, MDPI, vol. 16(12), pages 1-12, June.

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