Abstract. Estimating evaporation is important when managing water resources and cultivating crops. Evaporation can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST), which have recently become obtainable in very high resolution using lightweight thermal cameras and Unmanned Aerial Vehicles (UAVs). In this study a thermal camera was mounted on a UAV and applied into the field of heat fluxes and hydrology by concatenating thermal images into mosaics of LST and using these as input for the two-source energy balance (TSEB) modelling scheme. Thermal images are obtained with a fixed-wing UAV overflying a barley field in western Denmark during the growing season of 2014 and a spatial resolution of 0.20 m is obtained in final LST mosaics. Two models are used: the original TSEB model (TSEB-PT) and a dual-temperaturedifference (DTD) model. In contrast to the TSEB-PT model, the DTD model accounts for the bias that is likely present in remotely sensed LST. TSEB-PT and DTD have already been well tested, however only during sunny weather conditions and with satellite images serving as thermal input. The aim of this study is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to constitute as model input and thus attain accurate and high spatial and temporal resolution surface energy heat fluxes, with special focus on latent heat flux (evaporation). Furthermore, this study evaluates the performance of the TSEB scheme during cloudy and overcast weather conditions, which is feasible due to the low data retrieval altitude (due to low UAV flying altitude) compared to satellite thermal data that are only available during clear-sky conditions. TSEB-PT and DTD fluxes are compared and validated against eddy covariance measurements and the comparison shows that both TSEB-PT and DTD simulations are in good agreement with eddy covariance measurements, with DTD obtaining the best results. The DTD model provides results comparable to studies estimating evaporation with similar experimental setups, but with LST retrieved from satellites instead of a UAV. Further, systematic irrigation patterns on the barley field provide confidence in the veracity of the spatially distributed evaporation revealed by model output maps. Lastly, this study outlines and discusses the thermal UAV image processing that results in mosaics suited for model input. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high-resolution and consistent LST.

Abstract. This study investigates whether a water deficit index (WDI) based on imagery from unmanned aerial vehicles (UAVs) can provide accurate crop water stress maps at different growth stages of barley and in differing weather situations. Data from both the early and late growing season are included to investigate whether the WDI has the unique potential to be applicable both when the land surface is partly composed of bare soil and when crops on the land surface are senescing. The WDI differs from the more commonly applied crop water stress index (CWSI) in that it uses both a spectral vegetation index (VI), to determine the degree of surface greenness, and the composite land surface temperature (LST) (not solely canopy temperature).Lightweight thermal and RGB (red-green-blue) cameras were mounted on a UAV on three occasions during the growing season 2014, and provided composite LST and color images, respectively. From the LST, maps of surface-air temperature differences were computed. From the color images, the normalized green-red difference index (NGRDI), constituting the indicator of surface greenness, was computed. Advantages of the WDI as an irrigation map, as compared with simpler maps of the surface-air temperature difference, are discussed, and the suitability of the NGRDI is assessed. Final WDI maps had a spatial resolution of 0.25 m.It was found that the UAV-based WDI is in agreement with measured stress values from an eddy covariance system. Further, the WDI is especially valuable in the late growing season because at this stage the remote sensing data represent crop water availability to a greater extent than they do in the early growing season, and because the WDI accounts for areas of ripe crops that no longer have the same need for irrigation. WDI maps can potentially serve as water stress maps, showing the farmer where irrigation is needed to ensure healthy growing plants, during entire growing season.

Abstract. Estimating evapotranspiration is important when managing water resources and cultivating crops. Evapotranspiration can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST) which recently have become obtainable in very high resolution using Unmanned Aerial Vehicles (UAVs). Very high resolution LST can give insight into e.g. distributed crop conditions within cultivated fields. In this study evapotranspiration is estimated using LST retrieved with a UAV and the physically-based, two source energy balance models: the Priestley–Taylor TSEB (TSEB-PT) and the Dual-Temperature-Difference (DTD). A fixed-wing UAV was flown over a barley field in western Denmark during the spring and summer in 2014 and retrieved images of LST is successfully processed into thermal mosaics which serve as model input for both TSEB-PT and DTD. The aim is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to obtain high spatial and temporal resolution surface energy heat fluxes. Furthermore, this study evaluates the performance of the two source energy balance (TSEB) model scheme during cloudy and overcast weather conditions. This is feasible due to the low data retrieval altitude compared to satellite thermal data that are only available during clear skies and sunny conditions. Flux estimates from TSEB-PT and DTD are compared and validated against field data collected using an eddy covariance system located at same site at which the UAV flights were conducted. Furthermore, spatially distributed evapotranspiration patterns are evaluated using known irrigation patterns. Evapotranspiration is well estimated by both TSEB-PT and DTD with DTD as the best predictor. The DTD model provides results comparable to studies estimating evapotranspiration with satellite retrieved LST and physical land-surface models. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high resolution and consistent LST. Lastly, this study explicates thermal UAV data processing and the mosaicking of images into ortho-photos suited for model input.