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<p>The use of thermal-based crop water stress index (CWSI) has been studied in many crops in semi-arid regions and found as an effective method in detecting real-time crop water status of commercial fields remotely and non-destructively. However, to our knowledge, no previous studies have validated the usefulness of CWSI in a temperate crop like wild blueberries. Additionally, the temporal changes of the water status estimation model has not been well-studied. In this multi-year study, Unoccupied Aerial Vehicle (UAV)-borne thermal imageries were collected in 2019, 2020, and 2021 to test the temporal effects and the impact of different approach-based reference temperatures (T<sub><i>wet</i></sub>, wet reference temperature; T<sub><i>dry</i></sub>, dry reference temperature) on leaf water potential (LWP) estimation models using CWSI in two large adjacent wild blueberry fields in Maine, United States. We found that different sampling dates have a significant impact on LWP estimation models using CWSI<sub>SE</sub> (statistical T<sub><i>wet</i></sub> and empirical T<sub><i>dry</i></sub> reference) and CWSI<sub>SS</sub> (statistical T<sub><i>wet</i></sub> and statistical T<sub><i>dry</i></sub> reference). Further, CWSI<sub>BB</sub> calculated with bio-indicator-based T<sub><i>wet</i></sub> and T<sub><i>dry</i></sub> reference was found more effective (<i>r</i>² = 0.79<i>)</i> in estimating LWP in 2021, compared to the CWSI<sub>SE</sub> and CWSI<sub>SS</sub> approaches in 2019 (<i>r</i>² = 0.34 &amp; <i>r</i>² = 0.36), 2020 (<i>r</i>² = 0.38 &amp; <i>r</i>² = 0.44) and 2021 (<i>r</i>² = 0.43 &amp; <i>r</i>² = 0.46). CWSI<sub>BB</sub> -LWP model-based crop water status maps show high variation in the crop water status of wild blueberries, even in an evenly irrigated field, suggesting the potential of UAV-borne thermal cameras to detect real-time crop water status within the field, with the CWSI<sub>BB</sub> calculated from bio-indicator-based references being more reliable. Our results could be used for precision irrigation to increase the overall water use efficiency and profitability of wild blueberry production.</p>

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Detecting spatial variation in wild blueberry water stress using UAV-borne thermal imagery: distinct temporal and reference temperature effects

  • 期刊论文

  • 发表日期：

    2025年1月28日

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  • Kallol Barai,
  • Matthew Wallhead,
  • Bruce Hall,
  • Parinaz Rahimzadeh-Bajgiran,
  • Jose Meireles,
  • Ittai Herrmann,
  • Yong-Jiang Zhang

摘要

The use of thermal-based crop water stress index (CWSI) has been studied in many crops in semi-arid regions and found as an effective method in detecting real-time crop water status of commercial fields remotely and non-destructively. However, to our knowledge, no previous studies have validated the usefulness of CWSI in a temperate crop like wild blueberries. Additionally, the temporal changes of the water status estimation model has not been well-studied. In this multi-year study, Unoccupied Aerial Vehicle (UAV)-borne thermal imageries were collected in 2019, 2020, and 2021 to test the temporal effects and the impact of different approach-based reference temperatures (Twet, wet reference temperature; Tdry, dry reference temperature) on leaf water potential (LWP) estimation models using CWSI in two large adjacent wild blueberry fields in Maine, United States. We found that different sampling dates have a significant impact on LWP estimation models using CWSISE (statistical Twet and empirical Tdry reference) and CWSISS (statistical Twet and statistical Tdry reference). Further, CWSIBB calculated with bio-indicator-based Twet and Tdry reference was found more effective (r² = 0.79) in estimating LWP in 2021, compared to the CWSISE and CWSISS approaches in 2019 (r² = 0.34 & r² = 0.36), 2020 (r² = 0.38 & r² = 0.44) and 2021 (r² = 0.43 & r² = 0.46). CWSIBB -LWP model-based crop water status maps show high variation in the crop water status of wild blueberries, even in an evenly irrigated field, suggesting the potential of UAV-borne thermal cameras to detect real-time crop water status within the field, with the CWSIBB calculated from bio-indicator-based references being more reliable. Our results could be used for precision irrigation to increase the overall water use efficiency and profitability of wild blueberry production.