Assessing Drone-Based Remote Sensing Indices for Monitoring Rice Nitrogen Plant Status Under Different Irrigation Techniques

The rice sector is facing the challenge of increasing rice yields while maintaining or improving input use efficiency. The purpose of this study was to determine the most effective vegetation indices for monitoring nitrogen uptake (N uptake) under different irrigation techniques. The study was conducted in Uruguay over two rice-growing seasons. A split plot experimental design featured two irrigation treatments (main plots): continuous flooding (C) and alternate wetting and drying (AWD). The nitrogen-rate (N-rate) treatments (split plots) included no nitrogen, the recommended N-rate based on soil analyses, and two additional doses (±50% of the recommendation). The plant N uptake relationships with selected drone-based vegetation indices (VIs) were assessed at panicle initiation. The presence or absence of standing water during image collection affected the VIs and their relationships with N uptake. The relationships estimated for traditional irrigation may not be applicable for AWD. The SCCCI was the top index with a significantly stronger relationship with N uptake under the C (R2 = 0.84) and AWD (R2 = 0.71) irrigation techniques in relation to all evaluated vegetation indices. The Clre, NDRE2, NDRE, and CLg also had a significant relationship with N uptake under both irrigation treatments in both seasons, though their average R2 values of 0.75, 0.74, 0.73, and 0.71, respectively, were lower than the SCCCI (average R2 = 0.78). The findings would assist rice growers for selecting effective VIs for remote crop monitoring.