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Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties

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  • Campos, Isidro
  • Neale, Christopher M.U.
  • Suyker, Andrew E.
  • Arkebauer, Timothy J.
  • Gonçalves, Ivo Z.

Abstract

Methodologies based on earth observation remote sensing allow for a precise estimation of actual water requirements for irrigated crops across large areas. In spite of the many number of experiments using or analyzing the relationship between the basal crop coefficient (Kcb) and the soil adjusted vegetation index (SAVI) for maize, the development of new maize hybrid varieties with modifications related to canopy architecture suggest a possible change of the leaf area index (LAI) for maximum Kcb and its relationship with the SAVI or other vegetation indices. In addition, we noted a lack of analysis of these relationships for cultivated soybean. The objective of this paper is to analyze the Kcb, SAVI and LAI relationships in maize and soybean based on the non-linear relationships proposed by Choudhury et al. (1994). In addition, we propose a modification of the Choudhury et al. (1994) approach based on field-based experimental evidence of a minimum Kcb greater than 0. For sites with limited field data, we also analyze the utility of a simple linear regression based on the Kcb and SAVI values for bare soil and maximum Kcb values. The resulting Kcb-SAVI relationships are assimilated into a remote sensing based soil water balance model. The results of the model are analyzed in terms of irrigation requirements and crop evapotranspiration (ETa) for 11 growing seasons in two fields cultivated with irrigated and rain-fed maize and soybean in eastern Nebraska. Comparisons of measured and modelled ETa values indicate a good agreement, with RMSE lower than 0.7mmd−1 for weekly averaged values. The comparison of actual irrigation applied and irrigation requirements indicate the central pivot systems could not supply adequate water in some growing seasons with higher demands.

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  • Campos, Isidro & Neale, Christopher M.U. & Suyker, Andrew E. & Arkebauer, Timothy J. & Gonçalves, Ivo Z., 2017. "Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties," Agricultural Water Management, Elsevier, vol. 187(C), pages 140-153.
    • Handle: RePEc:eee:agiwat:v:187:y:2017:i:c:p:140-153
    DOI: 10.1016/j.agwat.2017.03.022
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    Cited by:

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    6. Bispo, R.C. & Hernandez, F.B.T. & Gonçalves, I.Z. & Neale, C.M.U. & Teixeira, A.H.C., 2022. "Remote sensing based evapotranspiration modeling for sugarcane in Brazil using a hybrid approach," Agricultural Water Management, Elsevier, vol. 271(C).
    7. Jovanovic, N. & Pereira, L.S. & Paredes, P. & Pôças, I. & Cantore, V. & Todorovic, M., 2020. "A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods," Agricultural Water Management, Elsevier, vol. 239(C).
    8. Barker, J. Burdette & Heeren, Derek M. & Neale, Christopher M.U. & Rudnick, Daran R., 2018. "Evaluation of variable rate irrigation using a remote-sensing-based model," Agricultural Water Management, Elsevier, vol. 203(C), pages 63-74.
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    10. Ouaadi, Nadia & Jarlan, Lionel & Khabba, Saïd & Le Page, Michel & Chakir, Adnane & Er-Raki, Salah & Frison, Pierre-Louis, 2023. "Are the C-band backscattering coefficient and interferometric coherence suitable substitutes of NDVI for the monitoring of the FAO-56 crop coefficient?," Agricultural Water Management, Elsevier, vol. 282(C).
    11. Kelechi Igwe & Vaishali Sharda & Trevor Hefley, 2023. "Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach," Land, MDPI, vol. 12(8), pages 1-26, July.
    12. 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.
    13. Zhao, Wenzhi & Chang, Xuexiang & Chang, Xueli & Zhang, Dengrong & Liu, Bing & Du, Jun & Lin, Pengfei, 2018. "Estimating water consumption based on meta-analysis and MODIS data for an oasis region in northwestern China," Agricultural Water Management, Elsevier, vol. 208(C), pages 478-489.
    14. Pôças, I. & Calera, A. & Campos, I. & Cunha, M., 2020. "Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches," Agricultural Water Management, Elsevier, vol. 233(C).
    15. Maguire, Mitchell S. & Neale, Christopher M.U. & Woldt, Wayne E. & Heeren, Derek M., 2022. "Managing spatial irrigation using remote-sensing-based evapotranspiration and soil water adaptive control model," Agricultural Water Management, Elsevier, vol. 272(C).
    16. Venancio, Luan Peroni & Mantovani, Everardo Chartuni & do Amaral, Cibele Hummel & Usher Neale, Christopher Michael & Gonçalves, Ivo Zution & Filgueiras, Roberto & Campos, Isidro, 2019. "Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI)," Agricultural Water Management, Elsevier, vol. 225(C).
    17. Gonçalves, I.Z. & Ruhoff, A. & Laipelt, L. & Bispo, R.C. & Hernandez, F.B.T. & Neale, C.M.U. & Teixeira, A.H.C. & Marin, F.R., 2022. "Remote sensing-based evapotranspiration modeling using geeSEBAL for sugarcane irrigation management in Brazil," Agricultural Water Management, Elsevier, vol. 274(C).
    18. 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).
    19. 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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