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Crop RS-Met: A biophysical evapotranspiration and root-zone soil water content model for crops based on proximal sensing and meteorological data

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  • Helman, David
  • Bonfil, David J.
  • Lensky, Itamar M.

Abstract

Assessing crops water use is essential for agricultural water management and planning, particularly in water-limited regions. Here, we present a biophysical model to estimate crop actual evapotranspiration and root-zone soil water content using proximal sensing and meteorological data (Crop RS-Met). The model, which is based on the dual FAO56 formulation, uses a water deficit factor calculated from rainfall and atmospheric demand information to constrain actual evapotranspiration and soil water content in crops growing under dry conditions. We tested the Crop RS-Met model in a dryland experimental field comprising a variety of wheat (Triticum aestivum L. and T. durum) cultivars with diverse phenology. Crop RS-Met was shown to accurately capture seasonal changes in wheat water use during the growing season. The average R2 of modeled vs. observed soil water content for all cultivars (N = 11) was 0.92 ± 0.02 with average relative RMSE and bias of 9.29 ± 1.30% and 0.13 ± 0.03%, respectively. We found that changing the integration time period of the water deficit factor in Crop RS-Met affects the accuracy of the model implying that this factor has a vital role in modeling crop water use under dry conditions. Currently, Crop RS-Met has a simple representation of surface runoff and does not take into consideration heterogeneity in the soil profile. Thus, efforts to combine numerical models that simulate soil water dynamics with a Crop RS-Met model driven by high-resolution remote sensing data may be needed for a spatially continuous assessment of crop water use in fields with more complex edaphic characteristics.

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  • Helman, David & Bonfil, David J. & Lensky, Itamar M., 2019. "Crop RS-Met: A biophysical evapotranspiration and root-zone soil water content model for crops based on proximal sensing and meteorological data," Agricultural Water Management, Elsevier, vol. 211(C), pages 210-219.
    • Handle: RePEc:eee:agiwat:v:211:y:2019:i:c:p:210-219
    DOI: 10.1016/j.agwat.2018.09.043
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    1. Pereira, L.S. & Paredes, P. & Jovanovic, N., 2020. "Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach," Agricultural Water Management, Elsevier, vol. 241(C).
    2. Víctor Manuel Fernández-Pacheco & Elena Antuña-Yudego & Juan Luis Carús-Candás & María José Suárez-López & Eduardo Álvarez-Álvarez, 2022. "An Evapotranspiration Evolution Model as a Function of Meteorological Variables: A CFD Model Approach," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    3. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).

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