Integration of sUAS imagery and atmospheric data collection for improved agricultural greenhouse gas emissions monitoring

We present on a full season of low-cost sUAS agricultural monitoring for improved GHG emissions accounting and mitigation. Agriculture contributes 10-12% of global anthropogenic GHG emissions, and roughly half are from agricultural soils. A variety of land management strategies can be implemented to reduce GHG emissions, but agricultural lands are complex and heterogenous. Nutrient cycling processes that ultimately regulate GHG emission rates are affected by environmental and management dynamics that vary spatially and temporally (e.g. soil properties, manure spreading). Thus, GHG mitigation potential is also variable, and determining best practices for mitigation is challenging, especially considering potential conflicting pressure to manage agricultural lands for other objectives (e.g. decrease agricultural runoff). Monitoring complexity from agricultural lands is critical for regional GHG accounting and decision making, but current methods (e.g., static chambers) are time intensive, expensive, and use in-situ equipment. These methods lack the spatio-temporal flexibility necessary to reduce the high uncertainty in regional emissions estimates, while traditional remote sensing methods often do not provide adequate spatio-temporal resolution for robust field-level monitoring. Small Unmanned Aerial Systems (sUAS) provide the range and the rapid response data collection needed to monitor key variables on the landscape (imagery) and from the atmosphere (CO2 concentrations), and can provide ways to bridge between in-situ and remote sensing data. Initial results show good agreement between sUAS CO2 sensors with more traditional equipment, and at a fraction of the cost. We present results from test flights over managed agricultural landscapes in Vermont, showcasing capabilities from both sUAS imagery and atmospheric data collected from on-board sensors (CO2, PTH). We then compare results from two different in-flight data collection methods: Vertical Profile and Horizontal Surveys. We conclude with results from the integration of these sUAS data with concurrently collected in-field measurements from static chambers and Landsat imagery, demonstrating enhanced understanding of agricultural landscapes and improved GHG emissions monitoring with the addition of sUAS collected data.