Utilizing In-Situ Static Chamber Measurements and UAV Imagery for Integrated Greenhouse Gas Emissions Estimations: Assessing Environmental and Management Impacts on Agricultural Emissions for Two Paired-Watershed Sites in Vermont

Abstract:

Agricultural greenhouse gas (GHG) emissions contribute to ~10-12% of global anthropogenic emissions. While agriculture is a major source of GHG emissions, there is also great potential for mitigation, as emissions can be reduced by utilizing specific field management and fertilization strategies. This study closely monitors hay and corn fields in Vermont in two paired-watershed sites. Carbon dioxide, nitrous oxide and methane emissions were measured weekly using static chambers and a Photoacoustic Gas Sensor (PAS) across both field management treatments: conventional and mitigation.

Accurately quantifying emissions from agricultural landscapes is crucial to develop and implement optimal mitigation strategies, but quantifying landscape-wide emissions is challenging. In this study, we show that both field management treatments and environmental conditions (such as field flooding from rain events) significantly affect GHG emissions, and both can be highly spatially variable even on the field-scale.

Monitoring this kind of complexity across a watershed is difficult, as most current emissions quantification techniques, such as static chambers, are localized, point specific and costly. Remote sensing provides an opportunity to monitor landscapes more efficiently and cost effectively. High resolution imagery from an Unmanned Aerial Vehicle (UAV) can also provide opportunities for more accurate watershed-wide estimates of GHG emission rates based on observable agricultural field conditions and management signals, such as field flooding, fertilizer application method, and cover cropping. Satellite imagery, and even the higher resolution aerial imagery used for agricultural monitoring, do not provide the spatial or temporal resolution needed to monitor the on-field complexities that affect GHG emissions.

This study combines and compares environmental and management observations from UAV imagery and in-situ field GHG emissions measurements to determine the effectiveness of UAV imagery in capturing data from environmental and management events. We then employ this UAV imagery to bridge the scale between field-monitoring of emissions to a landscape-wide monitoring of emissions, a technique which we hope can be utilized for more robust landscape-wide GHG emissions in the future.