Methane plays an important role in determining the atmosphere’s climate and chemistry. Fluxes of methane from an ecosystem are often measured using eddy covariance flux towers; however, there are disadvantages with this method. Flux towers are expensive to purchase and have high demands with respect to maintenance and cost of operation, especially in remote locations, making replication across the landscape a challenge. Using sensors mounted on a unmanned aerial vehicle (UAV), also known as a drone, would allow replication of flux measurements across a landscape as well as enable scientists to measure methane at locations where towers are not practical (i.e. sites that are ephemeral in nature, immediately after a disturbance, etc.). In this work, we test the ability of a UAV equipped with a highly accurate methane sensor to calculate ecosystem flux using the mass balance method. This method uses data collected with curtains (transects at various heights) flown both upwind and downwind of the area of interest. The concentration of methane within these curtains is then estimated using kriging techniques. The difference in calculated amounts of methane between the upwind and downwind curtains is processed to obtain an estimate of flux. Flights in wetlands that also have eddy covariance towers, providing corroborating flux values, have been flown in Alaska and California. We calculated UAV-based flux for the Alaskan flights using a bootstrap approach from multiple randomly subsampled data points within each full curtain of data. We compare these calculations to the traditional mass balance technique. We tested if these different approaches improve the accuracy of our results, as well as the uncertainty bounds for the small fluxes emitted from these ecosystems.

Gross Primary Productivity (GPP) is the largest flux in the global carbon cycle and satellite-based GPP estimates have long been used to study the trends and inter-annual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar-orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites - R series (GOES-R) to test the ability of sub-daily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We optimized parameters for three models to estimate GPP. A light response curve (LRC) achieved the lowest test mean absolute error for winter (1.82 µmol CO2 m-2 s-1), spring (2.51 µmol CO2 m-2 s-1), summer (1.45 µmol CO2 m-2 s-1), and fall (1.25 µmol CO2 m-2 s-1). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation towards the morning hours during the dry summers. The LRC and the light-use efficiency model were in agreement with these patterns of increasing shift of GPP towards the morning hours during the summer months. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.

To explain the inequality between aerodynamic and radiometric surface temperature, we used an analytical surface energy balance model where evaporation is directly estimated by constraining the state equations of aerodynamic temperature and biophysical conductances through radiometric temperature. While the derived aerodynamic temperature was comparable with a flux-inverted counterpart, evaporation and sensible heat fluxes also showed good correspondence with in-situ eddy covariance observations over contrasting aridity in Australia. Results showed aerodynamic temperature frequently exceeds the radiometric temperature in arid and semiarid ecosystems for two reasons: (i) declining canopy-surface conductance and evaporative fraction due to escalated water stress and vapor pressure deficit, and (ii) a simultaneous increase in aerodynamic conductance, air temperature and sensible heat flux. The analytical approach provides valuable insights into the long-lasting debate of aerodynamic versus radiometric temperature paradox by recognizing the feedback between biophysical conductances and the supply-demand limit of solar radiation, soil moisture, and vapor pressure deficit.

Agriculture is a significant source of carbon dioxide (CO2) and methane (CH4) and is the dominant source of anthropogenic nitrous oxide (N2O) emissions. Changes in agricultural land management practices that reduce overall greenhouse gas (GHG) emissions have been suggested to help mitigate climate change, but a better understanding of the timing, magnitude, and drivers of GHG fluxes is needed. Alfalfa agroecosystems may be significant sources of N2O given their ability to increase N inputs through symbiotic N2 fixation and frequent irrigation events that create conditions for hot moments of N2O production. However, few studies have explored long-term N2O emissions and their associated drivers in alfalfa ecosystems. We collected over 108,000 CO2, CH4 and N2O soil flux measurements over four years using cavity ring-down spectroscopy from a conventional flood-irrigated alfalfa field in California, USA. This ecosystem was a consistent source of N2O (annual mean: 624.4 ± 27.8 mg N2O m-2 yr-1, range: 263.6. ± 5.6 to 901.9 ± 74.5 mg N2O m-2 yr-1) and a small net sink of CH4 (annual mean: -53.5 ± 2.5 mg CH4 m-2 yr-1, range: -78.2 ± 8.8 to -31.6 ± 2.5 mg CH4 m-2 yr-1). Soil CO2 fluxes averaged 4925.9 ± 13.5 g CO2 m-2 yr-1 and were greater than other alfalfa ecosystem estimates, likely driven by elevated temperatures and plant productivity throughout the growing season. Hot moments of N2O emissions represented only 0.2% to 1.1% of annual measurements but were 31.6% to 56.8% of the annual flux. We found that both the magnitude and the contribution of N2O hot moments to annual emissions decreased over time. Normalized difference vegetation index (NDVI), soil temperature, moisture, and O2 were all significantly correlated with soil CO2, N2O, and CH4 fluxes, although associations varied across both soil depth and timescales. Our results suggest that flood-irrigated alfalfa is a significant source of agricultural N2O emissions, and that plant productivity and soil moisture effects on O2 availability may modulate the net GHG budget of alfalfa agroecosystems.