Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Abstract

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.

Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects

“…With an available orthomosaic approach that is effective at reducing inconsistencies currently experienced with standard blending modes, improved farm management can be realized through reduction of wind effects and enabling quantification of errors using our proposed STD approach between neighboring orthophotos. While the literature provides examples of many precision agricultural applications for UAV-based thermal infrared data, including surface flux retrievals for evapotranspiration studies [51], phenotyping [28], plant water stress assessment [52], and irrigation scheduling [53], they suffer from inconsistencies that would inevitably be introduced by wind effects, especially wind direction, and the existing orthomosaic approaches. Even when applying best-practice field data collections of temperature-controlled ground references [54], inconsistencies from wind effects and the generation of an orthomosaic still exist within the dataset.…”

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

“…Recently, remote sensing has been used for large scale ET estimation providing high spatial and temporal resolution. Several models and algorithms, such as Mapping evapotranspiration at high resolution with internalized calibration (METRIC), the Two-source energy balance (TSEB) model, and machine learning have been used and discussed [79]. Recently, DL learning has been introduced to improve the ET estimation based on remotely sensed data.…”

Web and MATLAB-Based Platform for UAV Flight Management and Multispectral Image Processing

“…Nowadays, UAVs are economically affordable and their ability to be flown at different altitudes and speeds make them helpful for photogrammetry and remote sensing (RS) techniques [3]. Their use is particularly extended in precision agriculture due to their capability to house onboard visible, thermal, and near-infrared sensors for acquiring aerial images of cultivated fields quickly in non-destructive and cost-effective ways [4][5][6][7][8][9][10][11]. In fact, UAVs have been widely used in the last 30 years in precision agriculture [12].…”

“…In fact, UAVs have been widely used in the last 30 years in precision agriculture [12]. A comprehensive review on UAV-based applications in precision agriculture can be found in [1,[4][5][6][7][8][9][10][11][12][13], and several UAV onboard sensors for remote sensing are reviewed in [14]. Deployment of any UAV application involves the integration of several issues.…”