Determining nitrogen deficiencies for maize using various remote sensing indices

Abstract Determining a precise nitrogen fertilizer requirement for maize in particular field and year has proven to be challenge due the complexity of inputs, transformations outputs cycle. Remote sensing deficiency may one way move applications closer specific requirement. Six vegetation indices [normalized difference index (NDVI), green normalized (GNDVI), red-edge (RENDVI), triangle greenness (TGI), area (NAVI) chlorophyll index-green (CI )] were evaluated their ability detect predict grain yield. Strip trials established at two locations Arkansas, USA, with rate as primary treatment. data was collected weekly an unmanned aerial system (UAS) equipped multispectral thermal sensor. Relationships among value, growth stage evaluated. Green NDVI, RENDVI CI had strongest relationship Chlorophyll Index-green GNDVI best predictors yield early growing season when application additional still agronomically feasible. However, logistics late must considered.