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Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach

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  • Zhang, Yu
  • Han, Wenting
  • Zhang, Huihui
  • Niu, Xiaotao
  • Shao, Guomin

Abstract

Timely and accurate estimation of crop evapotranspiration (ETc) is essential for efficient irrigation management at the farmland scale. However, effective decision-making for irrigation scheduling requires high spatiotemporal-resolution data to provide within-field heterogeneity information. The objective of this study was to evaluate the use of optical and thermal information obtained from an unmanned aerial vehicle (UAV) to quantify maize (Zea mays L.) ETc within the framework of the FAO-56 dual crop coefficient approach. Canopy temperature-based crop water stress index (CWSI) and number of degrees above canopy threshold (DACT) were used to determine the stress coefficient (Ks). Three types of normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) were adopted to determine basal crop coefficient (Kcb). Then the two forms of stress coefficient in combination with three vegetation indices (VIs) were evaluated to estimate daily crop ETc under different irrigation treatments over two years in the southwest region of Inner Mongolia, China. The results demonstrated that the combination of NDVI and CWSI produced the best estimates of maize ETc, with R2 of 0.84 and root mean square error (RMSE) of 0.50 mm/day. The DACT-based model also performed well, with R2 of 0.77–0.80 and RMSE of 0.53 mm/day. Although the results varied with irrigation levels, the daily mean bias error of ETc predictions over different years indicated acceptable accuracy, with a mean bias error (MBE) of 0.48 mm/day. Sensitivity analyses indicated that ETc models were most sensitive to the slope of the linear regression between Kcb and VIs, followed by soil evaporation constant and influential factor in Ks_DACT. From this study, the combinations of vegetation index and CWSI, as well as DACT, were recommended as alternative approaches for estimating ETc due to intrinsic simplicity and easy interpretation. These ETc models relying on UAV-based multi-sensor data thus show promising potential in farmland-scale applications.

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  • 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).
    • Handle: RePEc:eee:agiwat:v:275:y:2023:i:c:s0378377422005510
    DOI: 10.1016/j.agwat.2022.108004
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    1. González-Dugo, M.P. & Escuin, S. & Cano, F. & Cifuentes, V. & Padilla, F.L.M. & Tirado, J.L. & Oyonarte, N. & Fernández, P. & Mateos, L., 2013. "Monitoring evapotranspiration of irrigated crops using crop coefficients derived from time series of satellite images. II. Application on basin scale," Agricultural Water Management, Elsevier, vol. 125(C), pages 92-104.
    2. Pereira, L.S. & Paredes, P. & Melton, F. & Johnson, L. & Wang, T. & López-Urrea, R. & Cancela, J.J. & Allen, R.G., 2020. "Prediction of crop coefficients from fraction of ground cover and height. Background and validation using ground and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).
    3. 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).
    4. Ali, M.H. & Hoque, M.R. & Hassan, A.A. & Khair, A., 2007. "Effects of deficit irrigation on yield, water productivity, and economic returns of wheat," Agricultural Water Management, Elsevier, vol. 92(3), pages 151-161, September.
    5. Jin, Xiuliang & Yang, Guijun & Xue, Xuzhang & Xu, Xingang & Li, Zhenhai & Feng, Haikuan, 2017. "Validation of two Huanjing-1A/B satellite-based FAO-56 models for estimating winter wheat crop evapotranspiration during mid-season," Agricultural Water Management, Elsevier, vol. 189(C), pages 27-38.
    6. 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).
    7. Molden, David, 2007. "Water for food, water for life: a comprehensive assessment of water management in agriculture: summary. In Russian," IWMI Books, Reports H041260, International Water Management Institute.
    8. Alexandratos, Nikos & Bruinsma, Jelle, 2012. "World agriculture towards 2030/2050: the 2012 revision," ESA Working Papers 288998, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
    9. 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.
    10. Veysi, Shadman & Naseri, Abd Ali & Hamzeh, Saeid & Bartholomeus, Harm, 2017. "A satellite based crop water stress index for irrigation scheduling in sugarcane fields," Agricultural Water Management, Elsevier, vol. 189(C), pages 70-86.
    11. Farré, I. & Faci, J.-M., 2009. "Deficit irrigation in maize for reducing agricultural water use in a Mediterranean environment," Agricultural Water Management, Elsevier, vol. 96(3), pages 383-394, March.
    12. Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2022. "Effects of image spatial resolution and statistical scale on water stress estimation performance of MGDEXG: A new crop water stress indicator derived from RGB images," Agricultural Water Management, Elsevier, vol. 264(C).
    13. Hemakumara, H. M. & Chandrapala, Lalith & Moene, Arnold F., 2003. "Evapotranspiration fluxes over mixed vegetation areas measured from large aperture scintillometer," Agricultural Water Management, Elsevier, vol. 58(2), pages 109-122, February.
    14. DeJonge, Kendall C. & Taghvaeian, Saleh & Trout, Thomas J. & Comas, Louise H., 2015. "Comparison of canopy temperature-based water stress indices for maize," Agricultural Water Management, Elsevier, vol. 156(C), pages 51-62.
    15. 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.
    16. Han, Ming & Zhang, Huihui & DeJonge, Kendall C. & Comas, Louise H. & Trout, Thomas J., 2016. "Estimating maize water stress by standard deviation of canopy temperature in thermal imagery," Agricultural Water Management, Elsevier, vol. 177(C), pages 400-409.
    17. Mokhtari, Ali & Noory, Hamideh & Vazifedoust, Majid & Bahrami, Mahdi, 2018. "Estimating net irrigation requirement of winter wheat using model- and satellite-based single and basal crop coefficients," Agricultural Water Management, Elsevier, vol. 208(C), pages 95-106.
    18. Molden, David, 2007. "Water for food, water for life: a comprehensive assessment of water management in agriculture," IWMI Books, Reports H040193, International Water Management Institute.
    19. Ding, Risheng & Kang, Shaozhong & Zhang, Yanqun & Hao, Xinmei & Tong, Ling & Du, Taisheng, 2013. "Partitioning evapotranspiration into soil evaporation and transpiration using a modified dual crop coefficient model in irrigated maize field with ground-mulching," Agricultural Water Management, Elsevier, vol. 127(C), pages 85-96.
    20. Bausch, Walter C., 1995. "Remote sensing of crop coefficients for improving the irrigation scheduling of corn," Agricultural Water Management, Elsevier, vol. 27(1), pages 55-68, April.
    21. Mateos, L. & González-Dugo, M.P. & Testi, L. & Villalobos, F.J., 2013. "Monitoring evapotranspiration of irrigated crops using crop coefficients derived from time series of satellite images. I. Method validation," Agricultural Water Management, Elsevier, vol. 125(C), pages 81-91.
    22. Er-Raki, S. & Chehbouni, A. & Guemouria, N. & Duchemin, B. & Ezzahar, J. & Hadria, R., 2007. "Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region," Agricultural Water Management, Elsevier, vol. 87(1), pages 41-54, January.
    23. Tari, Ali Fuat, 2016. "The effects of different deficit irrigation strategies on yield, quality, and water-use efficiencies of wheat under semi-arid conditions," Agricultural Water Management, Elsevier, vol. 167(C), pages 1-10.
    24. Anderson, Ray G. & Alfieri, Joseph G. & Tirado-Corbalá, Rebecca & Gartung, Jim & McKee, Lynn G. & Prueger, John H. & Wang, Dong & Ayars, James E. & Kustas, William P., 2017. "Assessing FAO-56 dual crop coefficients using eddy covariance flux partitioning," Agricultural Water Management, Elsevier, vol. 179(C), pages 92-102.
    25. Santesteban, L.G. & Di Gennaro, S.F. & Herrero-Langreo, A. & Miranda, C. & Royo, J.B. & Matese, A., 2017. "High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard," Agricultural Water Management, Elsevier, vol. 183(C), pages 49-59.
    26. Campos, Isidro & Neale, Christopher M.U. & Calera, Alfonso & Balbontín, Claudio & González-Piqueras, Jose, 2010. "Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.)," Agricultural Water Management, Elsevier, vol. 98(1), pages 45-54, December.
    27. 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.
    28. Kullberg, Emily G. & DeJonge, Kendall C. & Chávez, José L., 2017. "Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients," Agricultural Water Management, Elsevier, vol. 179(C), pages 64-73.
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