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Validating FAO AquaCrop using Landsat images and regional crop information

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  • Kim, Daeha
  • Kaluarachchi, Jagath

Abstract

Defining crop response to water is a crucial part of decision-making for agricultural water management. This study is proposing an efficient and a low-cost approach to validate FAO's AquaCrop model using remote sensing (RS) estimates instead of crop ground measurements. A radiance use efficiency (RUE) based RS model for estimating aboveground biomass (AGB) is proposed with Landsat images and regional crop information. The RS estimates are used to validate AquaCrop's built-in crops and calibrate it under salinity stress. As a result, RS estimates of canopy cover (CC) and AGB were produced from an existing CC model and the proposed AGB model, respectively. These estimates became good replacements of the ground measurements for validation and calibration. Built-in maize of AquaCrop showed good agreement between simulations and RS estimates under non-stress conditions whereas built-in barley underestimated AGB compared to the RS estimates. By comparing the RS estimates in salinity-affected farms to AquaCrop simulations without considering salinity stress, AGB reduction due to salinity stress and corresponding CC reduction were quantified for calibration of AquaCrop under salinity stress. The results of calibration predicted initial soil salinity of saline-stressed farms and the values are within the possible ranges. The proposed methodology shows that the readily available Landsat images and regional crop information could extend the validation of built-in crops of AquaCrop to regions without ground measurements.

Suggested Citation

  • Kim, Daeha & Kaluarachchi, Jagath, 2015. "Validating FAO AquaCrop using Landsat images and regional crop information," Agricultural Water Management, Elsevier, vol. 149(C), pages 143-155.
    • Handle: RePEc:eee:agiwat:v:149:y:2015:i:c:p:143-155
    DOI: 10.1016/j.agwat.2014.10.013
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    Cited by:

    1. Moursi, Hossam & Kim, Daeha & Kaluarachchi, Jagath J., 2017. "A probabilistic assessment of agricultural water scarcity in a semi-arid and snowmelt-dominated river basin under climate change," Agricultural Water Management, Elsevier, vol. 193(C), pages 142-152.
    2. Foster, T. & Brozović, N. & Butler, A.P. & Neale, C.M.U. & Raes, D. & Steduto, P. & Fereres, E. & Hsiao, T.C., 2017. "AquaCrop-OS: An open source version of FAO's crop water productivity model," Agricultural Water Management, Elsevier, vol. 181(C), pages 18-22.
    3. Kim, Daeha & Kaluarachchi, Jagath J., 2016. "A risk-based hydro-economic analysis for land and water management in water deficit and salinity affected farming regions," Agricultural Water Management, Elsevier, vol. 166(C), pages 111-122.
    4. Li, Jiang & Song, Jian & Li, Mo & Shang, Songhao & Mao, Xiaomin & Yang, Jian & Adeloye, Adebayo J., 2018. "Optimization of irrigation scheduling for spring wheat based on simulation-optimization model under uncertainty," Agricultural Water Management, Elsevier, vol. 208(C), pages 245-260.
    5. Shirazi, Sana Zeeshan & Mei, Xurong & Liu, Buchun & Liu, Yuan, 2021. "Assessment of the AquaCrop Model under different irrigation scenarios in the North China Plain," Agricultural Water Management, Elsevier, vol. 257(C).
    6. Kim, Daeha & Chun, Jong Ahn & Inthavong, Thavone, 2021. "Managing climate risks in a nutrient-deficient paddy rice field using seasonal climate forecasts and AquaCrop," Agricultural Water Management, Elsevier, vol. 256(C).

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