Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices

• CCCI consistently performed best for estimating summer maize N status;.
• MTCI was the second best performing vegetation index;.
• Red edge vegetation indices generally performed better than red radiation-based vegetation indices;.
• NDRE and CIred edge performed similarly as CCCI and MTCI at the V10–V12 stage.

In recent decades, many spectral indices have been proposed to estimate crop nitrogen (N) status parameters. However, most of the indices based on red radiation lose their sensitivity under high aboveground biomass conditions. The objectives of this study were to (i) evaluate red-edge based spectral indices for estimating plant N concentration and uptake of summer maize (Zea mays L.) and (ii) study the influence of bandwidth and crop growth stage changes on the performance of various vegetation indices. Nitrogen rate experiments for maize were conducted in 2009 and 2010 at Quzhou Experimental Station of China Agricultural University in the North China Plain. The spectral indices were calculated from hyperspectral narrow bands, simulated Crop Circle ACS-470 active crop canopy sensor bands and simulated WorldView-2 satellite broad bands. The results indicated that red edge-based canopy chlorophyll content index (CCCI) performed the best across different bandwidths for estimating summer maize plant N concentration and uptake at the V6 and V7 and V10–V12 stages. The second best index was MERIS terrestrial chlorophyll index (MTCI). The four red edge-based indices, CCCI, MTCI, normalized difference red edge (NDRE) and red edge chlorophyll index (CIred edge), performed similarly better across bandwidths for estimating plant N uptake (R2 = 0.76–0.91) than normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) (R2 = 0.54–0.80) at the V10–V12 and V6–V12 stages. More studies are needed to further evaluate these red edge-based vegetation indices using real Crop Circle ACS 470 sensor and satellite remote sensing images for maize as well as other crops under on-farm conditions.