Plant trait data have been used in various studies related to ecosystem functioning, community ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a set of key plant traits, which are relatively easy to measure and have a stable and strong predictive response to ecosystem functions. However, the field measurements of plant trait data are still limited to small area, to a certain moment in time and to certain number of species only. Therefore, remote sensing (RS) offers potential to complement or even replace field measurements of some plant traits. It offers instantaneous spatially contiguous information, covers larger areas and in case of satellite observations profits from their revisit capacity. In this review, we first introduce RS concepts of light-vegetation interactions, RS instruments for vegetation studies, RS methods, and scaling between field and RS observations. Further we discuss in detail current achievements and challenges of optical RS for mapping of key plant traits. We concentrate our discussion on three categorical plant traits (plant growth and life forms, flammability properties and photosynthetic pathways and activity) and on five continuous plant traits (plant height, leaf phenology, leaf mass per area, nitrogen and phosphorous concentration or content). We review existing literature to determine the retrieval accuracy of the continuous plant traits. The relative estimation error using RS ranged between 10% and 45% of measured mean value, i.e. around 10% for plant height of tall canopies, 20% for plant height of short canopies, 15% for plant nitrogen, 25% for plant phosphorus content/concentration, and 45% for leaf mass per area estimates.The potential of RS to map plant traits is particularly high when traits are related to leaf biochemistry, photosynthetic processes and canopy structure. There are also other plant traits, i.e. leaf chlorophyll content, water content and leaf area index, which can be retrieved from optical RS well and can be of importance for plant scientists.We underline the need that future assessments of ecosystem functioning using RS should require comprehensive and integrated measurements of various plant traits together with leaf and canopy spectral properties. By doing so, the interplay between plant structural, physiological, biochemical, phenological and spectral properties can be better understood. Disciplines Medicine and Health Sciences | Social and Behavioral Sciences Publication DetailsHomolova, L., Malenovsky, Z., Clevers, J. G. P. W., Garcia-Santos, G. & Schaepman, M. E. (2013 ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a 22 set of key plant traits, which are relatively easy to measure and have a stable and strong 23 predictive response to ecosystem functions. However, the field measurements of plant trait 24 data are still limited to small area, to a certain moment in time and to certain number of 25 species only. Therefore, remote sensing (RS) offers potential...

Leaf chlorophyll is central to the exchange of carbon, water and energy between the biosphere and the atmosphere, and to the functioning of terrestrial ecosystems. This paper presents the first spatially continuous view of terrestrial leaf chlorophyll content (ChlLeaf) across a global scale. Weekly maps of ChlLeaf were produced from ENIVSAT MERIS full resolution (300 m) satellite data with a two-stage physically-based radiative transfer modelling approach. Firstly, leaf-level reflectance was derived from top-of-canopy satellite reflectance observations using 4-Scale and SAIL canopy radiative transfer models 3 for woody and non-woody vegetation, respectively. Secondly, the modelled leaf-level reflectance was used in the PROSPECT leaf-level radiative transfer model to derive ChlLeaf. The ChlLeaf retrieval algorithm was validated with measured ChlLeaf data from sample measurements at field locations, and covering six plant functional types (PFTs). Modelled results show strong relationships with field measurements, particularly for deciduous broadleaf forests (R 2 = 0.67; RMSE = 9.25 µg cm -2 ; p<0.001), croplands (R 2 = 0.41; RMSE = 13.18 µg cm -2 ; p<0.001) and evergreen needleleaf forests (R 2 = 0.47; RMSE = 10.63 µg cm -2 ; p<0.001). When the modelled results from all PFTs were considered together, the overall relationship with measured ChlLeaf remained good (R 2 = 0.47, RMSE = 10.79 µg cm -2 ; p<0.001).This result was an improvement on the relationship between measured ChlLeaf and a commonly used chlorophyll-sensitive spectral vegetation index; the MERIS Terrestrial Chlorophyll Index (MTCI; R 2 = 0.27, p<0.001). The global maps show large temporal and spatial variability in ChlLeaf, with evergreen broadleaf forests presenting the highest leaf chlorophyll values with global annual median of 54.4 µg cm -2 . Distinct seasonal ChlLeaf phenologies are also visible, particularly in deciduous plant forms, associated with budburst and crop growth, and leaf senescence. It is anticipated that this global ChlLeaf product will make an important step towards the explicit consideration of leaf-level biochemistry in terrestrial water, energy and carbon cycle modelling.

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