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Models of radiative transfer (RT) are important tools for remote sensing ofvegetation, as they facilitate forward simulations of remotely sensed data aswell as inverse estimation of biophysical and biochemical properties fromvegetation optical properties. The remote sensing estimation of foliar proteincontent is a key to monitoring the nitrogen cycle in terrestrial ecosystems inparticular to better understand photosynthetic capacity of plants and improvenitrogen management in agriculture. However, no physically based leaf RT modelcurrently allows for proper decomposition of leaf dry matter intonitrogen-based proteins and carbon-based constituents (CBC), estimated fromoptical properties of fresh or dry foliage. We developed a new version of thePROSPECT model, named PROSPECT-PRO, which separates nitrogen-based constituents(proteins) from CBC (including cellulose, lignin, hemicellulose and starch).PROSPECT-PRO was calibrated and validated on subsets of the LOPEX dataset,accounting for both fresh and dry broadleaf and grass samples. We applied aniterative model inversion optimization algorithm to identify optimal spectralsubdomains for retrieval of leaf protein and CBC contents, with 2125-2174 nmoptimal for proteins and 2025-2349 nm optimal for CBCs. PROSPECT-PRO inversionsrevealed a better performance in estimating proteins from optical properties offresh than dry leaves. We further tested the ability of PROSPECT-PRO toestimate leaf mass per area (LMA) as the sum of proteins and CBC usingindependent datasets acquired for numerous plant species. Results showed thatPROSPECT-PRO is fully compatible and comparable with its predecessor PROSPECT-Din indirect estimation of LMA. We can conclude from findings of this study thatPROSPECT-PRO has a high potential in establishing the carbon-to-nitrogen ratiobased on the retrieved CBC-to-proteins ratio.

PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents