Detecting interactive effects of N fertilization and heat stress on maize productivity by remote sensing techniques

• Spectroradiometric (NDVI) and RGB (NGRDI) derived vegetation indices are compared.
• Predictive capacity of vegetation indices increases in absence of heat stress.
• NGRDI is more efficient than NDVI detecting biomass and yield changes in maize.

The objective of this study was to compare the performance of two different remotely sensed techniques in detecting the effects of terminal heat stress and N fertilization on final maize aerial biomass (AB) and grain yield (GY). The study was conducted under field conditions for two consecutive growing seasons. Six N treatments combining three doses [0, 100, 200 Kg N ha−1] and two timings [at V4 and at 15 days before silking] were applied. Within each N treatment three heat treatments were applied (pre-flowering, post-flowering and the control treatment at ambient air temperature). Remote sensing measurements were taken with a multispectral band camera to measure the normalized difference vegetation index (NDVI) and a digital Red/Green/Blue (RGB) camera to measure the normalized green red difference index (NGRDI). Both indices failed to predict the GY of pre-flowering heat-treated plants due to grain set establishment problems that could not be detected by vegetation indices which are designed to capture differences in green canopy area. In contrast, both the NGRDI and the NDVI correlated positively with GY and AB in the control heat treatment and to a lesser extent in the post-flowering heat treatment. Under the control heat treatment, the NGRDI exhibited higher correlations with AB and GY than the NDVI across the N fertilization treatments. Since the NGRDI is formulated based only on the reflectance in the visible regions (VIS) of the spectrum (Green and Red) without dependence on the near infrared regions (NIR), it performs better than the NDVI. This is because it overcame the reported saturation patterns at high leaf area index and was more efficient at capturing even small differences in leaf colour (chlorophyll content) due to the different applied N treatments. Also, the NGRDI seemed to be a more seasonally independent parameter than the NDVI, which is more affected by temporal variability within the field, and thus the NGRDI predicted AB and GY better than the NDVI when combining the data of the two growing seasons.