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

作者：Dong Li, Yapeng Wu, Katja Berger, Qianliang Kuang, Wei Feng, Jing M. Chen, Wenhui Wang, Hengbiao Zheng, Xia Yao, Yan Zhu, Weixing Cao, Tao Cheng · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2024 · DOI：10.1016/j.jag.2024.104280 · 被引用次数：9 · 研究领域：Atmospheric and Environmental Gas Dynamics、Remote Sensing in Agriculture、Atmospheric chemistry and aerosols

• This study coupled PROSAIL-PRO and a nitrogen allocation model (PROSAIL-NAM). • PROSAIL-NAM was proposed to estimate canopy nitrogen content (CNC). • The performance of PROSAIL-NAM on CNC estimation was evaluated using six datasets. • Satisfactory estimations of CNC were obtained for all datasets (RMSE = 0.54–1.56 g/m 2 ). 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 con...