Evaluating Unmanned Aerial Vehicle Remote Sensing Towards Improving Site-specific Management

Unmanned aerial vehicles (UAVs) offer exciting new potentials within the field of precision agriculture. However, the technology remains in an experimental setting because of questions concerning data quality and quantity. To address these concerns, field research was conducted within a cotton cropping system, with particular applications focused on management zone delineation and evapotranspiration (ET) mapping. The overall objective of this proposal is to evaluate the suitability of UAV imagery (i.e. thermal, near-infrared, visible) as decision-making tools for precisions agriculture or site-specific management. UAVs were analyzed in terms of their ability to: 1) define MZs at various points before, during, and at the end of, a growing season, and 2) estimate ET using energy balance models. Results from Chapter 2 indicate that multispectral, thermal, and RGB imagery were significant predictors of in-season yield indicators such as canopy height and yield itself. In addition, MZs showed significant separation during flowering and boll filling, respectively. Results from Chapter 3 indicate that non-contextual energy balance models outperformed those of contextual models using eddy covariance data. Furthermore, LE model performance varied by soil type. Results from Chapter 4 indicate that upscaling UAV data is an important component towards practical management operations. In particular, it was better to evaluate UAV imagery at initial resolutions (here ~1.3 m) before aggregating to coarser resolutions