Assessment of Lower Boundary Layer Characteristics during Pre- and Post-Harvest Conditions Using Unmanned Aerial Systems

Proper characterization of land cover characteristics is critical in short-term weather forecasting due to the strong influence of surface heat and moisture fluxes on small-scale atmospheric circulations.  However, such a characterization is difficult in areas where land cover varies over short spatial and temporal scales, such as over agricultural areas during a harvest cycle.  To properly forecast near-surface atmospheric features in such circumstances, near-real-time assessment of the lower atmospheric boundary layer is necessary to describe the rapidly evolving meteorological conditions at any given time.  For this study, a small unmanned aerial system (UAS) is used to measure temperature, humidity, and pressure in the lower atmospheric boundary layer (< 300 m) before, during, and after the harvest cycle over an agricultural research area in east-central Mississippi.  By utilizing a consistent flight plan (path, altitude, and direction) and taking measurements on days with similar synoptic conditions, external factors related to boundary layer variability (i.e., turbulent mixing) can be controlled for as much as possible.  Since temperature differences between bare soil and mature crop leaf surfaces can exceed 15°C, surface and near-surface temperature and humidity observations will be taken over the various land cover types to quantify horizontal surface energy gradients.  The study area contains a variety of crops with various harvest periods and growth cycles; therefore, it serves as an excellent opportunity to identify and assess small-scale variations in atmospheric characteristics during periods of rapid changes in land cover.  The information gained from this research will help define the relative magnitude of changes in the lower atmosphere as a result of changes in surface conditions, which will benefit short-term weather forecasts over larger agricultural areas and other regions with local-scale land cover variability.