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<p>Application efficiency and distribution uniformity are the key indicators of irrigation performance. Field evaluation of irrigation uniformity with rain gauges is the standard practice, but it is time-consuming and limited in spatial coverage. Remote soil observation methodologies can be an interesting alternative or a complement to characterize irrigation variability. The aim of this research is to develop novel approaches to evaluate the spatial variability of water application in solid-set sprinkler irrigation across an entire field (with bare soil) using a combination of ground-sensors and Unmanned Aerial Vehicles (UAV) based remote sensing techniques. Experimental field trials were conducted to measure the variability of applied irrigation depth using standard methods (rain gauges), local soil temperature sensors and UAV-acquired thermal and visible (RGB) imagery. Additionally, a field trial on a proxy of a commercial field was performed to test the methodology. Local soil temperature measurements at a depth of 5 cm showed strong correlation with irrigation depth measured with rain gauges, particularly in sprinkler arrangements leading to low uniformity (average R<sup>2</sup> = 0.55 and RMSE = 1.21 mm). Thermal and visible UAV images effectively explained irrigation depth and variability, with thermal images achieving higher determination coefficients (average <i>R</i><sup>2</sup> = 0.83 and RMSE = 0.7 mm) than visible images (average <i>R</i><sup>2</sup> = 0.78 and RMSE = 0.9 mm). The best performance indicators were observed 3.5–5 h post-irrigation for soil luminance derived from RGB imagery and 5–7 h post-irrigation for thermal imagery. Remote soil temperature also performed better than local measured soil temperature. Linear regression models explaining irrigation depth variability based on soil temperature or luminance were influenced by environmental factors. To avoid this dependency, we propose to develop relational models using only 15 rain gauge measurements, which are used to train the UAV images. This method was validated through experimental trials and applied to estimate whole-field irrigation uniformity.</p>

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Soil luminance and thermography support the estimation of whole-field solid-set sprinkler irrigation uniformity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月30日

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  • N. Zapata,
  • A. Neji,
  • B. Latorre,
  • A. Serreta,
  • E. T. Medina,
  • P. Paniagua,
  • E. Playán

摘要

Application efficiency and distribution uniformity are the key indicators of irrigation performance. Field evaluation of irrigation uniformity with rain gauges is the standard practice, but it is time-consuming and limited in spatial coverage. Remote soil observation methodologies can be an interesting alternative or a complement to characterize irrigation variability. The aim of this research is to develop novel approaches to evaluate the spatial variability of water application in solid-set sprinkler irrigation across an entire field (with bare soil) using a combination of ground-sensors and Unmanned Aerial Vehicles (UAV) based remote sensing techniques. Experimental field trials were conducted to measure the variability of applied irrigation depth using standard methods (rain gauges), local soil temperature sensors and UAV-acquired thermal and visible (RGB) imagery. Additionally, a field trial on a proxy of a commercial field was performed to test the methodology. Local soil temperature measurements at a depth of 5 cm showed strong correlation with irrigation depth measured with rain gauges, particularly in sprinkler arrangements leading to low uniformity (average R2 = 0.55 and RMSE = 1.21 mm). Thermal and visible UAV images effectively explained irrigation depth and variability, with thermal images achieving higher determination coefficients (average R2 = 0.83 and RMSE = 0.7 mm) than visible images (average R2 = 0.78 and RMSE = 0.9 mm). The best performance indicators were observed 3.5–5 h post-irrigation for soil luminance derived from RGB imagery and 5–7 h post-irrigation for thermal imagery. Remote soil temperature also performed better than local measured soil temperature. Linear regression models explaining irrigation depth variability based on soil temperature or luminance were influenced by environmental factors. To avoid this dependency, we propose to develop relational models using only 15 rain gauge measurements, which are used to train the UAV images. This method was validated through experimental trials and applied to estimate whole-field irrigation uniformity.