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Determination of maize water requirement using remote sensing data and SEBAL algorithm

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  • Kamali, Mohammad Ismaeil
  • Nazari, Rouzbeh

Abstract

Efficient agricultural water demand management in arid and semi-arid regions is key in continuing crop production in a changing climate. As such, there’s a need for further investigation on various crops to identify the optimum water requirements to avoid water wasting in regions that are already facing water shortage. The focus of this work is to determine water requirement maize farming Mazandaran Province in Northern Iran, located on the southern side of the Caspian Sea, using Landsat satellite data. In order to use SEBAL algorithm, the images were atmospheric calibrated. Evapotranspiration maps with RMSE values equals to 0.73, 1.38 and 0.74 mm/day were produced and compared to Reference Book (RB), National Water Document (NWD) and FAO56 values. Furthermore, by computing reference evapotranspiration, crop coefficient curve was evaluated. In order to prepare maize water requirement maps, ET0 in 58 weather stations throughout the province were computed and interpolated. By using the daily ET0 maps and resulted Kc values, maize water requirement maps for the cultivated area of the province were 345.16–383.99 mm. The maize water requirement in the observation station of Sari compared to RB and NWD values showed −%20 and +%41 differences and RMSE was 76 and 156 mm, respectively.

Suggested Citation

  • Kamali, Mohammad Ismaeil & Nazari, Rouzbeh, 2018. "Determination of maize water requirement using remote sensing data and SEBAL algorithm," Agricultural Water Management, Elsevier, vol. 209(C), pages 197-205.
    • Handle: RePEc:eee:agiwat:v:209:y:2018:i:c:p:197-205
    DOI: 10.1016/j.agwat.2018.07.035
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    References listed on IDEAS

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    1. Ramos, J.G. & Cratchley, C.R. & Kay, J.A. & Casterad, M.A. & Martnez-Cob, A. & Domnguez, R., 2009. "Evaluation of satellite evapotranspiration estimates using ground-meteorological data available for the Flumen District into the Ebro Valley of N.E. Spain," Agricultural Water Management, Elsevier, vol. 96(4), pages 638-652, April.
    2. Rawat, Kishan Singh & Bala, Anju & Singh, Sudhir Kumar & Pal, Raj Kumar, 2017. "Quantification of wheat crop evapotranspiration and mapping: A case study from Bhiwani District of Haryana, India," Agricultural Water Management, Elsevier, vol. 187(C), pages 200-209.
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    Cited by:

    1. Mohammad Ismaeil Kamali & Hossein Ansari & Rouzbeh Nazari, 2022. "Optimization of Applied Water Depth Under Water Limiting Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4081-4098, September.
    2. de Oliveira Costa, Jéfferson & José, Jefferson Vieira & Wolff, Wagner & de Oliveira, Niclene Ponce Rodrigues & Oliveira, Rafaella Conceição & Ribeiro, Nathália Lopes & Coelho, Rubens Duarte & da Silva, 2020. "Spatial variability quantification of maize water consumption based on Google EEflux tool," Agricultural Water Management, Elsevier, vol. 232(C).
    3. Sabzchi-Dehkharghani, Hamed & Nazemi, Amir Hossein & Sadraddini, Ali Ashraf & Majnooni-Heris, Abolfazl & Biswas, Asim, 2021. "Recognition of different yield potentials among rain-fed wheat fields before harvest using remote sensing," Agricultural Water Management, Elsevier, vol. 245(C).
    4. Changchun Xu & Xicheng Zhang & Jinxia Zhang & Yapeng Chen & Teshome L. Yami & Yang Hong, 2021. "Estimation of Crop Water Requirement Based on Planting Structure Extraction from Multi-Temporal MODIS EVI," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2231-2247, May.

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