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

Models of radiative transfer (RT) are important tools for remote sensing vegetation, allowing forward simulations remotely sensed data as well inverse estimation biophysical and biochemical traits from vegetation optical properties. Estimation foliar protein content is the key to monitor nitrogen cycle in terrestrial ecosystems, particular assess photosynthetic capacity plants improve management agriculture. However, until now physically based leaf RT models have not allowed proper spectral decomposition dry matter nitrogen-based proteins other carbon-based constituents (CBC) properties fresh foliage. Such an achievement subsequent upscaling canopy level development new Earth observation applications. Therefore, we developed a version PROSPECT model, named PROSPECT-PRO, which separates (proteins) CBC (including cellulose, lignin, hemicellulose, starch sugars). PROSPECT-PRO was calibrated validated on subsets LOPEX dataset, accounting both broadleaf grass samples. We applied iterative model inversion optimization algorithm identified optimal ranges retrieval CBC. When combining reflectance transmittance within selected domains, inversions revealed similarly accurate estimates samples (respective validation R2 = 0.96 0.95, NRMSE 9.6% 13.4%), whereas better performance obtained than leaves when estimating 0.79 0.57, 15.1% 26.1%). The attributed feature selection procedure. further tested ability estimate mass per area (LMA) sum using independent datasets acquired numerous plant species. Results showed that PROSPECT-D were able produce comparable LMA across dataset gathering 1685 (validation 0.90 16.5% 18.3% PROSPECT-D). Findings also capable assessing carbon-to‑nitrogen ratio retrieved CBC-to-proteins (R2 0.87 15.7% leaves, 0.65 28.1% leaves). assessment newly designed demonstrates promising potential its involvement precision agriculture ecological applications aiming at carbon contents observations current forthcoming airborne satellite imaging spectroscopy sensors.