Spatially explicit regionalization of airborne flux measurements using environmental response functions

Abstract

Abstract. The goal of this study is to characterize the sensible (H) and latent (LE) heat exchange for different land covers in the heterogeneous steppe landscape of the Xilin River catchment, Inner Mongolia, China. Eddy-covariance flux measurements at 50–100 m above ground were conducted in July 2009 using a weight-shift microlight aircraft. Wavelet decomposition of the turbulence data enables a spatial discretization of 90 m of the flux measurements. For a total of 8446 flux observations during 12 flights, MODIS land surface temperature (LST) and enhanced vegetation index (EVI) in each flux footprint are determined. Boosted regression trees are then used to infer an environmental response function (ERF) between all flux observations (H, LE) and biophysical (LST, EVI) and meteorological drivers. Numerical tests show that ERF predictions covering the entire Xilin River catchment (≈3670 km2) are accurate to ≤18% (1 σ). The predictions are then summarized for each land cover type, providing individual estimates of source strength (36 W m−2 < H < 364 W m−2, 46 W m−2 < LE < 425 W m−2) and spatial variability (11 W m−2 < σH < 169 W m−2, 14 W m−2 < σLE < 152 W m−2) to a precision of ≤5%. Lastly, ERF predictions of land cover specific Bowen ratios are compared between subsequent flights at different locations in the Xilin River catchment. Agreement of the land cover specific Bowen ratios to within 12 ± 9% emphasizes the robustness of the presented approach. This study indicates the potential of ERFs for (i) extending airborne flux measurements to the catchment scale, (ii) assessing the spatial representativeness of long-term tower flux measurements, and (iii) designing, constraining and evaluating flux algorithms for remote sensing and numerical modelling applications.

The NASA Carbon Airborne Flux Experiment (CARAFE): instrumentation and methodology

“…The technique provides a time series of fluxes along a flight track, removing the need to block-average homogenous sub-sections of the flight and giving relatively fine surface resolution that is essential when surveying patchy terrain. Further descriptions of CWT applications to airborne fluxes can be found elsewhere (Desjardins et al, 2018;Karl et al, 2009;Kaser et al, 2015;Mauder et al, 2007;Metzger et al, 2013;Misztal et al, 2016Misztal et al, , 2014Vaughan et al, 2016;Wolfe et al, 2015;Yuan et al, 2015).…”

“…For example, Misztal et al (2016) define a series of circles with radii equal to dx 0.5 and used these areas to integrate model-derived surface fluxes (with equal weighting within the circle). Sayres et al (2017) utilize the 1-D parameterization of Kljun et al (2004), while others have augmented the latter with a cross-wind distribution function (Metzger et al, 2012(Metzger et al, , 2013Vaughan et al, 2016). The recent 2-D parameterization of Kljun et al (2015;hereafter K15) is an attractive next step, both because it is based on the same Lagrangian framework as its 1-D predecessor and because the MATLAB code is freely available.…”

eddy4R 0.2.0: a DevOps model for community-extensible processing and analysis of eddy-covariance data based on R, Git, Docker, and HDF5

“…In Lee et al, 2015;Metzger et al, , 2013Metzger et al, , 2016Salmon et al, 2015;Serafimovich et al, 2013;Starkenburg et al, 2016;Vaughan et al, 2016;Xu et al, 2017). eddy4R currently consists of four packages: eddy4R.base, eddy4R.qaqc, eddy4R.turb, and eddy4R.erf.…”

Flux measurements by the NRC Twin Otter atmospheric research aircraft: 1987–2011

“…Mauder et al (2008) used these data to demonstrate that flux measurements from a low-flying aircraft could be used to produce 2-dimensional maps of the sensible and latent heat flux for this complex ecosystem. Recent work has demonstrated how aircraft-based flux data can be used in combination with satellite-based input data to develop environmental response functions (Metzger et al, 2013). These permit an order-of-magnitude increase in spatio-temporal resolution and extend the resulting flux maps.…”