Using a Fixed Wing Uav Remote Sensing System for Monitoring Sorghum Growth and Development.

Remote sensing is an effective method for estimating crop biophysical properties. High resolution multispectral unmanned aerial vehicle (UAV) remote sensing imagery was collected from a sorghum variety testing location at the Texas A&M Brazos Farm (30°32'26.02"N and 96°25'33.34"W) using the Sentek GEMS multispectral camera on the Anaconda fixed-wing UAV platform. Each plot was 15 m long and 3.5 m wide. Red and NIR images were collected at an altitude of 40 m above the ground at approximately 11:30 AM CST on 24 May, 10 June and 18 June in 2016. For radiometric calibration of the aerial imagery, calibration tarps were laid out in the field. Digital count (DC) values corresponding to the calibration tarps were extracted from the UAV imagery and correlated with the known average tarp reflectances. Linear calibration equations relating DC values to reflectance in the red, green and NIR wavebands were developed. These calibration equations were used to convert the UAV image DC to reflectance. Reflectance values for the pixels representing each plot were averaged to calculate NDVI. We collected overhead photographs of selected plots using a digital camera on both image acquisition dates to determine actual canopy cover. LAI was measured for selected plots using a LAI-2200C plant canopy analyzer (LI-COR, Lincoln, NE). Results of this study indicate a highly significant relationship between NDVI estimated from high-resolution UAV remote sensing data and crop biophysical variables. The coefficient of determination (R²) between NDVI and LAI was 0.88, while the R² between NDVI and percent canopy cover was 0.89.  Preliminary results from this study show that UAV-based remote sensing data could be used effectively for estimating plant biophysical parameters.