Simultaneous multicopter-based air sampling and sensing of meteorological variables

Abstract

Abstract. The state and composition of the lowest part of the planetary boundary layer (PBL), i.e., the atmospheric surface layer (SL), reflects the interactions of external forcing, land surface, vegetation, human influence and the atmosphere. Vertical profiles of atmospheric variables in the SL at high spatial (meters) and temporal (1 Hz and better) resolution increase our understanding of these interactions but are still challenging to measure appropriately. Traditional ground-based observations include towers that often cover only a few measurement heights at a fixed location. At the same time, most remote sensing techniques and aircraft measurements have limitations to achieve sufficient detail close to the ground (up to 50 m). Vertical and horizontal transects of the PBL can be complemented by unmanned aerial vehicles (UAV). Our aim in this case study is to assess the use of a multicopter-type UAV for the spatial sampling of air and simultaneously the sensing of meteorological variables for the study of the surface exchange processes. To this end, a UAV was equipped with onboard air temperature and humidity sensors, while wind conditions were determined from the UAV's flight control sensors. Further, the UAV was used to systematically change the location of a sample inlet connected to a sample tube, allowing the observation of methane abundance using a ground-based analyzer. Vertical methane gradients of about 0.3 ppm were found during stable atmospheric conditions. Our results showed that both methane and meteorological conditions were in agreement with other observations at the site during the ScaleX-2015 campaign. The multicopter-type UAV was capable of simultaneous in situ sensing of meteorological state variables and sampling of air up to 50 m above the surface, which extended the vertical profile height of existing tower-based infrastructure by a factor of 5.

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

“…The station is located in a north–south-oriented valley in the foothills of the Bavarian Alps (47.833° N, 11.061° E; 595 m a.s.l.) with grassland being the prevailing land use with sporadic croplands (Brosy et al 2017; Zeeman et al 2017). In summer 2016, the grassland at the study site served as a meadow.…”

“…The advent of low-cost UAS platforms and concomitantly lightweight camera systems in the visible, near-infrared (NIR), and thermal spectral range has motivated their increased use in the remote-sensing community, e.g. precision agriculture applications (Berni et al 2009; Brosy et al 2017; Zhang and Kovacs 2012; Candiago et al 2015; Reineman et al 2013; Link, Senner, and Claupein 2013; Lelong et al 2008; Turner et al 2014; Stefano et al 2017; Vázquez-Tarrío et al 2017). However, studies using UAS-based TIR sensors to map LST and subsequently derive surface turbulent heat fluxes are still rare (Hoffmann et al 2016b; Ortega-Farías et al 2017; Ortega-Farías et al 2016; Brenner et al 2017).…”

Surface flux estimates derived from UAS-based mole fraction measurements by means of a nocturnal boundary layer budget approach

“…Fendt is part of the TERENO (Terrestrial Environmental Observatories) network and is extensively instrumented for the purpose of long-term monitoring of land-atmosphere exchange (Mauder et al, 2013;Kiese et al, 2018). Complementary observations were made during the ScaleX 2016 campaign (Wolf et al, 2017). In the following, we list only those instruments that produced the data presented in this publication.…”

“…Small and lightweight sensors for other tracers such as methane would open up even more possibilities. Alternatively, compact time-resolved sampling systems (Andersen et al, 2017) or long flexible tubing (Brosy et al, 2017) can be used in connection with conventional ground-based instrumentation to measure a whole range of species.…”

A Near-Field Gaussian Plume Inversion Flux Quantification Method, Applied to Unmanned Aerial Vehicle Sampling

“…They are significantly costlier to maintain and operate than small UAV platforms. Small UAVs have a recent and growing variety of scientific applications [44][45][46], especially in hazardous environments [47][48][49][50][51].…”