PROSPECT-PRO: a leaf radiative transfer model for estimation of leaf protein content and carbon-based constituents

Leaf nitrogen content is key information for ecological and agronomic processes. A number of studies aiming at estimation of leaf nitrogen content used chlorophyll content as a proxy due to a moderate to strong correlation between chlorophyll and nitrogen content during vegetative growth stages. Since leaf nitrogen content is directly linked to leaf protein content, the capacity to accurately estimate leaf protein content may improve robustness of an operational nitrogen monitoring. In the past, the introduction of proteins - as an absorbing input constituent of the PROSPECT leaf model - has been attempted numerous times. Yet, the attempts suffered from a certain number of shortcomings, including limited applicability to both fresh and dry vegetation, inaccurate definition of the specific absorption coefficients, or incomplete accounting for different constituents of leaf dry matter.

Here, we introduce PROSPECT-PRO, a new version of the PROSPECT model simulating leaf optical properties based on their biochemical properties and including protein and carbon-based constituents (CBC) as new input variables. These two additional chemical constituents correspond to two complementary constituents of LMA. Specific absorption coefficients for proteins and CBC were produced splitting LOPEX dataset into 50% for calibration and 50%for validation. Both data sets included fresh and dry samples. Our objective is to keep compatibility between PROSPECT-PRO and PROSPECT-D, the previous version of the model, and to ensure the same performances for the estimation of LMA even through its decomposition into two constituents. Therefore, the full validation consisted of two steps:

1) PROSPECT-PRO inversion using an iterative optimization approach to retrieve proteins and CBC from LOPEX data

2) Testing the compatibility with PROSPECT-D by estimating LMA as the sum of protein and CBC content from independent datasets

The capacity of PROSPECT-PRO for the accurate estimation of leaf proteins and CBC on LOPEX could be evidenced, with slightly higher performances for the estimation of fresh leaf proteins (NRMSE = 17.3%, R² = 0.75) than of dry leaf proteins (NRMSE =24.0%, R² = 0.62). Good overall performances were obtained for the estimation of CBC (NRMSE<15%, R²>0.90). Based on these results, the carbon/nitrogen ratio of leaves could be modelled accurately.

The indirect estimation of LMA through PROSPECT-PRO inversion led to similar or slightly improved results when compared to the estimation of LMA with PROSPECT-D. Hence, PROSPECT-PRO might be of particular interest for precision agriculture applications in the context of nitrogen sensing using observations of current and forthcoming satellite imaging spectroscopy missions.