Estimating leaf nitrogen content by coupling a nitrogen allocation model with canopy reflectance

The area-based leaf nitrogen content (LNCA) has gained increasing attention recently in ecology and plant physiology, and the mass-based nitrogen content (LNCM) has been extensively used by agronomists for making fertilization recommendations. Remote sensing has been emerging as an important tool to quantify LNCA and LNCM across multiple observation scales, such as leaf and canopy. Most models for estimating LNCA and LNCM are based on their empirical relationships with chlorophyll-related vegetation indices (VIs), which cannot cap-ture the dynamic allocation of nitrogen into different components throughout the growing season. This study proposed a simplified nitrogen allocation model by dividing the total nitrogen into photosynthetic nitrogen (PN) and non-photosynthetic nitrogen with the purpose of estimating LNCA and LNCM in rice and winter wheat. The nitrogen allocation model was calibrated with an independent leaf-scale dataset (R-2 = 0.82) and then applied to the canopy scale without the need to adjust the model coefficients. The estimated fractions of PN were 71% and 78% for rice and wheat leaves, respectively, which were close to the reported value (similar to 75%) for common C-3 plants. Overall, the nitrogen allocation-based models were applicable for estimating both LNCA and LNCM with relative root mean square error (RRMSE) values <15% for the jointing and booting stages. In addition, our novel estimation model was generic for rice and wheat, yielding the best estimations for LNCM (R-2 = 0.63) across the entire growing season compared with traditional empirical models based on VIs. This study represents a first attempt to couple nitrogen allocation theory with remote sensing data to improve the spectroscopic estimation of LNCA and LNCM.

Automated summary

This study proposes a nitrogen allocation-based model to estimate leaf nitrogen content and mass-based nitrogen content in rice and winter wheat. By dividing the total nitrogen into photosynthetic nitrogen and non-photosynthetic nitrogen, the model demonstrates good accuracy at leaf and canopy scales. Compared with traditional models based on vegetation indices, this novel estimation model shows better performance in estimating nitrogen content throughout the growing season.