Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level

Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R-2 > 0.6) than the single spectral index (R-2 > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R-2 = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.

Automated summary

This study tested the ability of Hyperspectral LiDAR (HSL) in estimating maize nitrogen concentration and biomass, showing that Partial Least Squares Regression (PLSR) performed better with HSL assistance. Future research should utilize larger datasets to test the viability of using HSL for monitoring crop nitrogen concentration.