Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model

Nitrogen is one of the most important macronutrients for plant growth and timely estimation of canopy nitrogen content (CNC) is crucial for agricultural applications. Remote sensing has emerged as an important tool to quantify CNC using either empirically or physically based methods. Most empirical methods use chlorophyll related spectral indices and are dependent on the relationship between nitrogen and chlorophyll, which varies with vegetation types and growth stages. In contrast, physically based methods use the full-range reflectance data and retrieve CNC from coupled leaf and canopy radiative transfer models (such as PROSPECT-PRO + 4SAIL, PROSAIL-PRO). However, the subtle absorption features of nitrogen and protein in fresh leaves hinder the accurate estimation of CNC. Therefore, this study proposed an efficient and mechanistic framework to estimate CNC (PROSAIL-NAM) by coupling PROSAIL-PRO with a nitrogen allocation model, which divided the total nitrogen into non-photosynthetic nitrogen (NPN) and photosynthetic nitrogen (PN). At the canopy level, PN and NPN are assumed to be proportional to canopy chlorophyll content (CCC) and canopy dry matter content (CDM), respectively. The PROSAIL-PRO model was first used to estimate CCC and CDM, and then the resulting CCC and CDM were fed to the nitrogen allocation model to estimate CNC. The estimation accuracy of CNC was assessed with six diverse datasets: four from field crop experiments across geographic sites, one from multiple ecosystems, and one from a satellite-ground joint experiment. Our results showed that satisfactory estimations of CNC were obtained when CCC and CDM were estimated using a model inversion method (RMSE = 0.54–1.56 g/m2) and a hybrid retrieval method (RMSE = 0.49–2.25 g/m2). The model inversion method was comparable with the hybrid retrieval method for ground platforms, but performed better for airborne and satellite platforms. In addition, the traditional protein-nitrogen conversion model obtained CNC from the canopy protein content and led to clear overestimations of CNC with RMSE > 1.95 g/m2. This study represents a first attempt to develop a robust approach by coupling PROSAIL-PRO with a nitrogen allocation model for accurate estimation of CNC across geographic sites, ecosystems, and platforms. These finding will advance the monitoring of CNC from regional to global scales.