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Purpose <p>This study aimed to evaluate a methodology based on multispectral time series acquired by UAV for detecting irrigation anomalies in maize at the plot level across different crop development stages. The research addressed how irrigation blockage duration, spatial resolution (pixel size), and topographic conditions influence anomaly detectability.</p> Methods <p>Experiments were conducted during the 2024 summer growing season on two maize plots with contrasting topography: one flat and one sloped. In each plot, three sprinklers per irrigation event were intentionally blocked for periods ranging from 15 to 25 days, depending on crop phenological stage. Multispectral images were acquired every five days using a DJI Mavic 3&#xa0;M UAV equipped with RGB and multispectral sensors and a GNSS RTK module. Flights were performed during peak solar irradiance to ensure radiometric consistency and generate homogeneous time series for analysis.</p> Results <p>Detectability of irrigation-induced vegetation responses was influenced by both pixel size and irrigation blockage duration. Higher spatial resolution improved detection performance, particularly during prolonged water deficit periods. Precipitation events and topographic variability attenuated the effect of irrigation blockages by increasing water availability, which delayed their differentiation in the NDVI time series.</p> Conclusion <p>The proposed methodology integrates multispectral UAV time series with statistical testing based on p-values and mean differences between irrigated and non-irrigated zones to detect irrigation-induced vegetation responses at the plot scale. It demonstrates robustness under varying agronomic and topographic conditions and provides a transferable framework for irrigation monitoring, with potential application to other crops and integration with UAV and satellite-based precision agriculture systems.</p>

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Influence of spatial resolution on the detection of sprinkler irrigation non-uniformity using high-resolution remote sensing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月8日

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  • Milton José Campero-Taboada,
  • Javier Casalí,
  • María González-Audícana,
  • Miguel Á. Campo-Bescós

摘要

Purpose

This study aimed to evaluate a methodology based on multispectral time series acquired by UAV for detecting irrigation anomalies in maize at the plot level across different crop development stages. The research addressed how irrigation blockage duration, spatial resolution (pixel size), and topographic conditions influence anomaly detectability.

Methods

Experiments were conducted during the 2024 summer growing season on two maize plots with contrasting topography: one flat and one sloped. In each plot, three sprinklers per irrigation event were intentionally blocked for periods ranging from 15 to 25 days, depending on crop phenological stage. Multispectral images were acquired every five days using a DJI Mavic 3 M UAV equipped with RGB and multispectral sensors and a GNSS RTK module. Flights were performed during peak solar irradiance to ensure radiometric consistency and generate homogeneous time series for analysis.

Results

Detectability of irrigation-induced vegetation responses was influenced by both pixel size and irrigation blockage duration. Higher spatial resolution improved detection performance, particularly during prolonged water deficit periods. Precipitation events and topographic variability attenuated the effect of irrigation blockages by increasing water availability, which delayed their differentiation in the NDVI time series.

Conclusion

The proposed methodology integrates multispectral UAV time series with statistical testing based on p-values and mean differences between irrigated and non-irrigated zones to detect irrigation-induced vegetation responses at the plot scale. It demonstrates robustness under varying agronomic and topographic conditions and provides a transferable framework for irrigation monitoring, with potential application to other crops and integration with UAV and satellite-based precision agriculture systems.