Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects.We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives. Geosphere-Biosphere Program (IGBP) and DIVERSITAS, the TRY database (TRY-not an acronym, rather a statement of sentiment; https ://www.try-db.org; Kattge et al., 2011) was proposed with the explicit assignment to improve the availability and accessibility of plant trait data for ecology and earth system sciences. The Max Planck Institute for Biogeochemistry (MPI-BGC) offered to host the database and the different groups joined forces for this community-driven program. Two factors were key to the success of TRY: the support and trust of leaders in the field of functional plant ecology submitting large databases and the long-term funding by the Max Planck Society, the MPI-BGC and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, which has enabled the continuous development of the TRY database.

We investigate combined continuum removal and radiative transfer (RT) modeling to retrieve leaf chlorophyll a & b content (Cab) from the AISA Eagle airborne imaging spectrometer data of sub-meter (0.4 m) spatial resolution. Based on coupled PROSPECT-DART RT simulations of a Norway spruce (Picea abies (L.) Karst.) stand, we propose a new Cab sensitive index located between 650 and 720 nm and termed ANCB650-720. The performance of ANCB650-720 was validated against ground-measured Cab of ten spruce crowns and compared with Cab estimated by a conventional artificial neural network (ANN) trained with continuum removed RT simulations and also by three previously published chlorophyll optical indices: normalized difference between reflectance at 925 and 710 nm (ND925&710), simple reflectance ratio between 750 and 710 nm (SR750/710) and the ratio of TCARI/OSAVI indices. Although all retrieval methods produced visually comparable Cab spatial patterns, the ground validation revealed that the ANCB650-720 and ANN retrievals are more accurate than the other three chlorophyll indices (R2 = 0.72 for both methods). ANCB650-720 estimated Cab with an RMSE = 2.27 μg cm− 2 (relative RRMSE = 4.35%) and ANN with an RMSE = 2.18 μg cm− 2 (RRMSE = 4.18%), while SR750/710 with an RMSE = 4.16 μg cm− 2 (RRMSE = 7.97%), ND925&710 with an RMSE = 9.07 μg cm− 2 (RRMSE = 17.38%) and TCARI/ OSAVI with an RMSE = 12.30 μg cm− 2 (RRMSE = 23.56%). Also the systematic RMSES was lower than the unsystematic one only for the ANCB650-720 and ANN retrievals. Our results indicate that the newly proposed index can provide the same accuracy as ANN except for Cab values below 30 μg cm− 2, which are slightly overestimated (RMSE = 2.42 μg cm− 2). The computationally efficient ANCB650-720 retrieval provides accurate high spatial resolution airborne Cab maps, considerable as a suitable reference data for validating satellite-based Cab products. Disciplines Medicine and Health Sciences | Social and Behavioral Sciences Publication DetailsMalenovsky, Z., Homolova, L., Zurita-Milla, R., Lukes, P., Kaplan, V., Hanus, J., Gastellu-Etchegorry, J. & Schaepman, M. E. (2013). Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer. Page | 2 Abstract 26We investigate combined continuum removal and radiative transfer ( considerable as a suitable reference data for validating satellite-based C ab products. 48

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Abstract:Coniferous species are present in almost all major vegetation biomes on Earth, though they are the most abundant in the northern hemisphere, where they form the northern tree and forest lines close to the Arctic Circle. Monitoring coniferous forests with satellite and airborne remote sensing is active, due to the forests' great ecological and economic importance. We review the current understanding of spectral behavior of different components forming coniferous forests. We look at the spatial, directional, and seasonal variations in needle, shoot, woody element, and understory spectra in coniferous forests, based on measurements. Through selected case studies, we also demonstrate how coniferous canopy spectra vary at different spatial scales, and in different viewing angles and seasons. Finally, we provide a synthesis of gaps in the current knowledge on spectra of elements forming coniferous forests that could also serve as a recommendation for planning scientific efforts in the future.

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