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. This book examines the different
UAV-based approaches of ET estimation. Models and algorithms, such
as mapping evapotranspiration at high resolution with internalized
calibration (METRIC), the two-source energy balance (TSEB) model,
and machine learning (ML) are discussed. It also covers the
challenges and opportunities for UAVs in ET estimation, with the
final chapters devoted to new ET estimation methods and their
potential applications for future research.
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