Abstract
With the development of low-cost, lightweight, integrated thermal infrared-multispectral cameras, unmanned aerial systems (UAS) have recently become a flexible complement to eddy covariance (EC) station methods for mapping surface energy fluxes of vegetated areas. These sensors facilitate the measurement of several site characteristics in one flight (e.g., radiometric temperature, vegetation indices, vegetation structure), which can be used alongside in-situ meteorology data to provide spatially-distributed estimates of energy fluxes at very high resolution. Here we test one such system (MicaSense Altum) integrated into an off-the-shelf long-range vertical take-off and landing (VTOL) unmanned aerial vehicle, and apply and evaluate our method by comparing flux estimates with EC-derived data, with specific and novel focus on heterogeneous vegetation communities at three different sites in Germany. Firstly, we present an empirical method for calibrating airborne radiometric temperature in standard units (K) using the Altum multispectral and thermal infrared instrument. Then we provide detailed methods using the two-source energy balance model (TSEB) for mapping net radiation (Rn), sensible (H), latent (LE) and ground (G) heat fluxes at <0.82 m resolution, with root mean square errors (RMSE) less than 45, 37, 39, 52 W m−2 respectively. Converting to radiometric temperature using our empirical method resulted in a 19% reduction in RMSE across all fluxes compared to the standard conversion equation provided by the manufacturer. Our results show the potential of this UAS for mapping energy fluxes at high resolution over large areas in different conditions, but also highlight the need for further surveys of different vegetation types and land uses.
Highlights
Introduction2021, 13, 1286 transpiration/latent heat flux (LE) over sensible (H) and ground (G) heat effluxes [1,7,8]
When Altum digital number (DN) is converted to Kelvin using Equation (9) and compared to Apogee measurements, the root mean square error (RMSE)
This study has successfully demonstrated the application of an off-the-shelf unmanned aerial system (e.g., Trinity F90+ and Micasense Altum) for reproducing eddy covariance station-derived energy flux estimates using the Two-Source Energy Balance Model
Summary
2021, 13, 1286 transpiration/latent heat flux (LE) over sensible (H) and ground (G) heat effluxes [1,7,8]. This interplay between Rn, LE, H and G, as moderated by the type, physiology, structure and distribution of vegetation that are present, can influence climate at all scales; from the local scale where shade trees can reduce local temperatures ( mitigating climatic extremes in agroforestry settings [9]), to regional scale weather systems where the amount and distribution of tree cover can influence precipitation patterns [6,10]. The accurate measurement of these fluxes over different land use and land cover types is of great interest to food producers, climatologists and land/water resource managers alike
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