Evapotranspiration Estimation with Small UAVs in Precision Agriculture

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 22; p. 6427
• Main Authors: Niu, Haoyu, Hollenbeck, Derek, Zhao, Tiebiao, Wang, Dong, Chen, YangQuan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 10.11.2020; MDPI AG
• Subjects: Agriculture; Aircraft; clumped canopy; Crops, Agricultural - physiology; evapotranspiration; METRIC; Plant Transpiration; remote sensing; Remote Sensing Technology; Review; Soil; unmanned aerial vehicles; METRIC; remote sensing; evapotranspiration; unmanned aerial vehicles; clumped canopy
• DOI: 10.3390/s20226427
• ISSN: 1424-8220

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. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which 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. In this study, the authors examined different UAV-based approaches of ET estimation at first. 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 analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.