Tyler J. Nigon1, Aicam Laacouri2, Ce Yang3 and David Mulla1, (1)Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN (2)Department of Soil, Water and Climate, University of Minnesota, St Paul, MN (3)Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN
Leaf area index [LAI] and above ground biomass are strong indicators of overall crop status and provide insight into how soil spatial variability affects crop growth and yield. They are closely linked to crop and soil variables that have a direct impact on the economic optimum nitrogen [N] rate, such as available water, soil nutrients, crop N uptake, or yield potential. Remote sensing algorithms and crop models are used as decision support tools for determining variable rate and timing of N fertilizer, but they are much more reliable if accurate estimates of early season above-ground biomass and LAI are used for calibration. Measuring LAI and biomass via destructive methods is both time consuming and costly, especially as the growth stage progresses and there is more plant material to handle. Therefore, there is strong interest in developing models for estimating these biophysical parameters and understanding how much variability is expected using a more streamlined, remote sensing approach. A study was performed in southern Minnesota to observe the relationships among maize height, LAI, above-ground biomass, and spectral reflectance collected via an unmanned aerial vehicle platform. Four N rates were applied to ensure differences in growth among treatments, and measurements were taken at the V5, V8, and V10 growth stages. Utilization of spectral reflectance for estimating LAI and biomass is a viable approach for informing remote sensing algorithms and crop models, but variability of such estimations should be considered when interpreting the final precision N fertilizer recommendations.