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Sensitivity of METRIC-based tree crop evapotranspiration estimation to meteorology, land surface parameters and domain size

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  • He, Ruyan
  • Jin, Yufang
  • Jiang, Jinbao
  • Xu, Meng
  • Jia, Sen

Abstract

Evapotranspiration (ET), a key component of crop water balance, is essential for growers to manage agricultural irrigation. METRIC model is increasingly used to estimate and map ET with satellite observations, especially at daily and field scales. However, the analysis of its uncertainties on tree crop ET estimation is very limited. We used METRIC model to estimate daily ET over the almond and pistachio orchards throughout the entire growing season in the San Joaquin Valley, California, and evaluated the impacts of variations in meteorological variables, land surface parameters and different study sizes on ET estimation. The results showed that the METRIC-based daily ET estimates of the almond and pistachio trees were most sensitive to net radiation (Rn), followed by instantaneous reference ET (ETo), land surface temperature (LST), and near-surface temperature difference (dT). When a 5% error was added to Rn, the uncertainty in daily ET estimates was up to 8.44 ± 1.24%, corresponding to a mean increase of 0.45 ± 0.02 mm/day throughout the entire growing season. A 5% or 0.5 K error in instantaneous ETo, LST, and dT also caused changes in daily ET estimates by 4.15 ± 3.75%, 2.97 ± 0.14%, and 2.31 ± 1.20%, respectively. ET estimates with METRIC were relatively much less sensitive to other variables. The results from different study sizes indicated that daily ET estimates in Size-2 were 0.33 ± 0.08 mm/day overestimation compared with those from Size-1 in the almond site. Similarly, a mean overestimation of 0.30 ± 0.14 mm/day was found between Size-1 and Size-3 in the pistachio sites throughout the entire growing season. This study demonstrates the potential to further improve the accuracy of remote sensing based ET estimation for complex tree crops with METRIC model, by optimizing key parameters and choosing a proper domain size for study area.

Suggested Citation

  • He, Ruyan & Jin, Yufang & Jiang, Jinbao & Xu, Meng & Jia, Sen, 2022. "Sensitivity of METRIC-based tree crop evapotranspiration estimation to meteorology, land surface parameters and domain size," Agricultural Water Management, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:agiwat:v:271:y:2022:i:c:s0378377422003365
    DOI: 10.1016/j.agwat.2022.107789
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    References listed on IDEAS

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    1. Xue, Jingyuan & Bali, Khaled M. & Light, Sarah & Hessels, Tim & Kisekka, Isaya, 2020. "Evaluation of remote sensing-based evapotranspiration models against surface renewal in almonds, tomatoes and maize," Agricultural Water Management, Elsevier, vol. 238(C).
    2. Martin Jung & Markus Reichstein & Philippe Ciais & Sonia I. Seneviratne & Justin Sheffield & Michael L. Goulden & Gordon Bonan & Alessandro Cescatti & Jiquan Chen & Richard de Jeu & A. Johannes Dolman, 2010. "Recent decline in the global land evapotranspiration trend due to limited moisture supply," Nature, Nature, vol. 467(7318), pages 951-954, October.
    3. C. Santos & I. Lorite & R. Allen & M. Tasumi, 2012. "Aerodynamic Parameterization of the Satellite-Based Energy Balance (METRIC) Model for ET Estimation in Rainfed Olive Orchards of Andalusia, Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(11), pages 3267-3283, September.
    4. Wang, Tianxin & Melton, Forrest S. & Pôças, Isabel & Johnson, Lee F. & Thao, Touyee & Post, Kirk & Cassel-Sharma, Florence, 2021. "Evaluation of crop coefficient and evapotranspiration data for sugar beets from landsat surface reflectances using micrometeorological measurements and weighing lysimetry," Agricultural Water Management, Elsevier, vol. 244(C).
    5. Ortega-Salazar, Samuel & Ortega-Farías, Samuel & Kilic, Ayse & Allen, Richard, 2021. "Performance of the METRIC model for mapping energy balance components and actual evapotranspiration over a superintensive drip-irrigated olive orchard," Agricultural Water Management, Elsevier, vol. 251(C).
    6. Qiu, Rangjian & Liu, Chunwei & Cui, Ningbo & Wu, Youjie & Wang, Zhenchang & Li, Gen, 2019. "Evapotranspiration estimation using a modified Priestley-Taylor model in a rice-wheat rotation system," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
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