Decoupling canopy structure effects from vegetation indices for robust assessment of potato plant nitrogen content across growth stages

Accurate monitoring of plant nitrogen concentration (PNC) is essential for precision nitrogen (N) fertilization management at the farm scale. While numerous vegetation indices (VIs) have been proposed for PNC monitoring, their applicability and accuracy often vary across growth stages. The impact of canopy structural variations across phenological stages on the performance of VIs in estimating crop N status has been confirmed, but the underlying mechanisms and effective solutions remain insufficiently understood. This study systematically analyzed how growth stage-induced canopy structural variations affect potato PNC estimation using VIs. Furthermore, a novel index named the canopy structure adjustment vegetation index (CSAI) was developed to mitigate the interference of growth stages in PNC estimation. During the 2022 and 2023 growing seasons, a total of 432 PNC measurements and corresponding unmanned aerial vehicle (UAV) multispectral images were collected from potato experimental fields in Northeast China. Experimental plots with N and potassium treatments were laid out to rigorously evaluate the performance of CSAIs in estimating the PNC of potato. Our findings revealed that fractional vegetation cover (FVC) was the primary driver of VIs variations across different potato growth stages, thus rendering PNC estimation by VIs strongly dependent on the relationship between FVC and PNC. The newly developed CSAI (CSAI = VI/FVC) can better adapt to the dynamic growth patterns of potatoes, substantially mitigating growth stage effects on PNC estimation. Validation using an independent seasonal dataset indicated that CSAIs derived from the optimized soil-adjusted vegetation index (OSAVI) and normalized difference red edge index (NDRE) yielded higher R 2 values of 0.88 and 0.80, with lower RRMSE values of 13.88% and 16.65%, respectively. Using potato as an example, this study investigated the mechanisms underlying how growth stages influence the performance of UAV-based VIs in estimating PNC and introduced effective solutions. Future research should further validate the CSAI framework across broader environmental conditions and crop species. These findings provide a promising foundation for informed decision-making in crop N management.