Predicting evapotranspiration from drone-based thermography – a method comparison in a tropical oil palm plantation

Abstract

<jats:p>Abstract. For the assessment of evapotranspiration, near-surface airborne thermography offers new opportunities for studies with high numbers of spatial replicates and in a fine spatial resolution. We tested drone-based thermography and the subsequent application of three energy balance models (DATTUTDUT, TSEB-PT, DTD) using the widely accepted eddy covariance technique as a reference method. The study site was a mature oil palm plantation in lowland Sumatra, Indonesia. For the 61 flight missions, latent heat flux estimates of the DATTUTDUT model with measured net radiation agreed well with eddy covariance measurements (r² = 0.85; MAE = 47; RMSE = 60) across variable weather conditions and daytimes. Confidence intervals for slope and intercept of a model II Deming regression suggest no difference between drone-based and eddy covariance method, thus indicating interchangeability. TSEB-PT and DTD yielded agreeable results, but all three models are sensitive to the configuration of the net radiation assessment. Overall, we conclude that drone-based thermography with energy-balance modeling is a reliable method complementing available methods for evapotranspiration studies. It offers promising, additional opportunities for fine grain and spatially explicit studies. </jats:p>

Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach

“…We then applied the energy balance model DATTUTDUT [49] based on the canopy temperatures, again using the QGIS3-plugin QWaterModel, and extracted the same key metrics for the resulting fluxes. To calculate the fluxes using the DATTUTDUT model we applied both a fully modelled net radiation approach and a short-wave irradiance-based estimation approach [22]. We used the pandas library [50] in Python 3.6.9 to merge the datasets according to the individual plant and recording time.…”

“…To some extent, this might also be possible using other non-temperature-based approaches such as the normalized difference vegetation index (NDVI) that can capture the photosynthetic activity during the day [21]. For evapotranspiration, modelled results based on land surface temperature data often follow a linear relationship with ground-based measurements from eddy covariance systems [16,22]. For complex non-linear structures and relationships, e.g., between evapotranspiration and its controlling factors, statistical regression models can be supplemented with machine learning (ML) algorithms [23,24].…”

Remote sensing from unoccupied aerial systems: Opportunities to enhance Arctic plant ecology in a changing climate

“…moss, lichen, graminoid and low‐lying shrubs) and typically below T air , which leads to localized cooling during the growing season and exerts negative feedback to permafrost thaw (Blok et al, 2010; Frost et al, 2018) and plant diversity (Yang et al, 2021). Furthermore, using UAS‐borne TIR in the footprint of eddy covariance towers could improve the characterization and scaling of surface energy exchanges and water cycling from site to ecosystem or biome scale (Ellsäßer et al, 2020; Hoffmann et al, 2016).…”

Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives

“…Such high‐resolution UAV imagery could facilitate evapotranspiration partitioning between vine and interrow to understand the effects of canopy architecture (Nieto et al., 2019) and shadows (Aboutalebi et al., 2019) on surface‐atmosphere energy exchanges. In an oil palm plantation, an MK EASY Okto V3 octocopter equipped with a FLIR Tau 2 thermal camera and an Omnivision CMOS‐Sensor was used to compare latent heat flux and evapotranspiration between measurements from UAV imagery and eddy covariance methods (Ellsäßer et al., 2021). A good correlation between the DATTUTDUT model and eddy covariance existed for latent heat flux ( R 2 = 0.85) across all daytime and weather conditions, and the results underscored the utility of UAV‐based thermography for integrating miniaturized radiation sensors to estimate radiation budgets.…”