Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

Abstract

Spatially distributed high-resolution data of land surface temperature (LST) and evapotranspiration (ET) are important information for crop water management and other applications in the agricultural sector. While satellite data can provide LST high-resolution data of 100 m, the current development of unmanned aerial systems (UAS) and affordable low-weight thermal cameras allows LST and subsequent ET to be derived at resolutions down to centimetre scale.In this study, UAS-based images in the thermal infrared (TIR) and visible spectral range were collected over a managed temperate grassland in July 2016 at the Terrestrial Environmental Observatories Networks TERENO preAlpine observatory site at Fendt, Germany. The UAS set-up included a lightweight thermal camera (Optris Pi Lightweight) and a regular digital camera (Sony α 6000) that allowed for the acquisition of thermal and optical images with a ground resolution of 5 cm and 1 cm, respectively. Three TIR-based ET models of different complexity were applied and the resulting ET estimates were compared to the Eddy covariance (EC) observations of turbulent energy fluxes and also to the evaporative fraction. While the Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature (DATTUTDUT) model and the Triangle Method belong to the group of simpler contextual models, the Two-Source Energy Balance (TSEB) model incorporates a more physically based formulation of the surface energy balance. In addition to the comparison of UAS-based estimates of latent heat fluxes to EC observations, the effect of the spatial resolution of the model imagery input on the modelled results was analysed by running the models with imagery from the native resolution of the acquired images to resolutions that were aggregated up to 30 m.The results show that both contextual models are sensitive to the input image resolution and that the agreement with the EC observations improves with increasing image resolution. The TSEB model assumes that LST pixels represent a mixed signal of the soil and canopy components, thus an image resolution coarse enough to ensure this assumption should be chosen. However, with the exception of the native image resolution of 5 cm, we found no effect of image resolution on the spatially weighted mean TSEB estimates.For the studied grassland, the comparison of model estimates with EC observations indicates that all three models are able to reproduce observed energy fluxes with comparable accuracy with mean absolute errors of ET between 20 and 40 W m−2. The TSEB model showed larger deviations from the reference observations under cloudy conditions with rapid fluctuations of LST within the 30 min averaging period for EC. The two contextual models yielded similar results for most of the flights. The good performance of the DATTUTDUT model, which had the lowest input requirements of the three models, is especially promising in view of the application of UAS for routine near-real-time ET monitoring.

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

“…LST is used in various research contexts such as urban ecology like monitoring plant and human health during heat waves [373], for the description of the hydrological cycle, in climate research, or in studies of vegetation dynamics [374,375]. Furthermore, LST is often used to estimate evapotranspiration [369,[376][377][378], which is a variable that is highly controlled by atmospheric conditions, but also by stomata conductance, the plant available, the soil moisture, and processes of the surface-subsurface interactions [379]. In this sense, the monitoring of LST with high spatial and temporal resolution can provide valuable information about the water and energy exchange between the soil-plant-atmosphere continuum as well as related photosynthetic activities of the vegetation.…”

The TERENO Pre‐Alpine Observatory: Integrating Meteorological, Hydrological, and Biogeochemical Measurements and Modeling

“…During the two past campaigns, researchers teamed up to evaluate the spatial variability of precipitation, surface energy exchange, the source strength of GHGs, the influences of local air‐flow patterns, and sensor design. The additional experiments included innovative systems for concerted airborne sampling and land‐surface sensing (Brenner et al, 2018; Brosy et al, 2017; Golston et al, 2017; Mauder and Zeeman, 2018), ground‐based remote sensing and mobile survey platforms, and enhanced sampling strategies to resolve properties of the atmospheric boundary layer, turbulent exchange, precipitation, the soil, and vegetation. The ScaleX activities therefore contribute to the evaluation of methodologies that are fundamental to the observatory and to the development of model chains that aim to resolve submesoscale interactions.…”

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

“…This is most probably due to higher solar intensity and higher mean atmospheric temperature in June (21.70 • C measured on 22/06/18) compared to those in October (15.08 • C measured on 20/10/18), and the lower wind speed encountered during the surveys (2 -3 ms −1 in survey 1 compared to 5 -8 ms −1 in survey 2) yielding a lower cooling effect. The range of temperature is also likely to be influenced by the presence of a crop and associated evapotranspiration and stress effects [106,107]. In order to understand why the thermal imagery from survey 1 (June 2018) revealed considerably greater archaeological information than the thermal imagery from survey 2 (October 2018), the two sets of thermal data were converted to 1D column vectors for statistical analysis and plotted together on the same scale ( Figure 10).…”

“…This is most probably due to higher solar intensity and higher mean atmospheric temperature in June (21.70°C measured on 22/06/18) compared to those in October (15.08°C measured on 20/10/18), and the lower wind speed encountered during the surveys (2 -3 ms -1 in survey 1 compared to 5 -8 ms -1 in survey 2) yielding a lower cooling effect. The range of temperature is also likely to be influenced by the presence of a crop and associated evapotranspiration and stress effects [106][107].…”