Assessing VIs Calculated From UAS-Acquired Multispectral Imaging to Detect Iron Chlorosis in Grain Sorghum

This study uses a small Unmanned Aircraft System (sUAS) equipped with a multispectral sensor to assess various Vegetation Indices (VIs) for their potential to monitor iron chlorosis levels in a grain sorghum crop. Iron chlorosis is a nutritional disorder that affects various crops grown in high- pH, calcareous soils. Weekly flights were completed over the growing season and processed using Structure-from- Motion (SfM) photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red-edge, and near-infrared wavelengths. Ground data collection was used to analyze stress and chlorophyll levels, correlating them to the imagery. 25 VIs were calculated using reflectance maps and soil-removed reflectance maps. The separability for each VI was calculated using a two-class distance measure. The field-acquired data was used to conclude which VIs achieved the best results. In conclusion, the soil-removed MERIS Terrestrial Chlorophyll (MTCI), Normalized Difference Red-Edge (NDRE), and Normalized Green (NG) indices achieved the highest amount of separation.