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Towards Tree-level Evapotranspiration Estimation with Small UAVs in Precision Agriculture
YangQuan Chen, Haoyu Niu
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Springer International Publishing 
Naturwissenschaften, Medizin, Informatik, Technik / Landwirtschaft, Gartenbau
Description
Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. On the other hand, while remote sensing is able to provide spatially distributed measurements, the spatial resolution of multispectral satellite images is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery.
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Multispectral Imagery, Unmanned aerial vehicles, Evapotranspiration, Machine Learning, Pomegranate Orchard