AI Summary & Key Takeaways
AI InsightThe approach offers significant potential for real-time irrigation scheduling and water resource optimization, with applications extending to diverse agricultural systems and climate conditions.
📌 3 Key Takeaways
Abstract
Precise knowledge of plot-scale surface energy partitioning is fundamental for agricultural water management, yet conventional ground or satellite techniques rarely resolve the heterogeneity for the plot scale areas. This study couples multispectral and thermal imagery acquired weekly–bi-weekly by a DJI Matrice-300 RTK/MicaSense-Altum platform with the physically based Two-Source Energy Balance (TSEB) model to quantify net radiation (Rn), soil heat flux (G), sensible heat flux (H) and latent heat flux (LE) in a randomized sorghum experiment comprising four irrigation levels (100, 70, 40 % ETc and rain-fed) in the Bafra Plain, Türkiye (May–October 2021). The model produced consistent seasonal trends: midday Rn peaked at 797 W m⁻², while G/Rn declined from ≈0.20 at emergence to <0.10 under full canopy. Strong irrigation-induced contrasts were detected; fully irrigated plots reached maximum LE of 692 W m⁻² and H of 10 W m⁻², whereas rain-fed plots dropped to LE of 215 W m⁻² and exceeded H of 450 W m⁻² during peak stress. Flux magnitudes and partitioning agreed with published eddy covariance and lysimeter studies, indicating that UAV-driven TSEB reliably bridges the scale gap between point sensors and satellites. The approach offers significant potential for real-time irrigation scheduling and water resource optimization, with applications extending to diverse agricultural systems and climate conditions.
Özet
Precise knowledge of plot-scale surface energy partitioning is fundamental for agricultural water management, yet conventional ground or satellite techniques rarely resolve the heterogeneity for the plot scale areas. This study couples multispectral and thermal imagery acquired weekly–bi-weekly by a DJI Matrice-300 RTK/MicaSense-Altum platform with the physically based Two-Source Energy Balance (TSEB) model to quantify net radiation (Rn), soil heat flux (G), sensible heat flux (H) and latent heat flux (LE) in a randomized sorghum experiment comprising four irrigation levels (100, 70, 40 % ETc and rain-fed) in the Bafra Plain, Türkiye (May–October 2021). The model produced consistent seasonal trends: midday Rn peaked at 797 W m⁻², while G/Rn declined from ≈0.20 at emergence to <0.10 under full canopy. Strong irrigation-induced contrasts were detected; fully irrigated plots reached maximum LE of 692 W m⁻² and H of 10 W m⁻², whereas rain-fed plots dropped to LE of 215 W m⁻² and exceeded H of 450 W m⁻² during peak stress. Flux magnitudes and partitioning agreed with published eddy covariance and lysimeter studies, indicating that UAV-driven TSEB reliably bridges the scale gap between point sensors and satellites. The approach offers significant potential for real-time irrigation scheduling and water resource optimization, with applications extending to diverse agricultural systems and climate conditions.