An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2)

The DART (discrete anisotropic radiative transfer) model simulates radiative transfer in heterogeneous 3-D scenes that may comprise different landscape features; i.e., leaves, grass, trunks, water, soil. The scene is divided into a rectangular cell matrix, i.e., building block for simulating larger scenes. Cells are parallelipipedic. Their optical properties are represented by individual scattering phase functions that are directly input into the model or are computed with optical and structural characteristics of elements within the cell. Radiation scattering and propagation are simulated with the exact kernel and discrete ordinate approaches; any set of discrete direction can be selected. In addition to topography and hot spot, leaf specular and first-order polarization mechanisms are modeled. Two major iterative steps are distinguished: 1) Cell illumination with direct sun radiation: Within cell multiple scattering is accurately simulated. 2) Interception and scattering of previously scattered radiation: Atmospheric radiation, possibly anisotropic, is input at this stage. Multiple scattering is stored as spherical harmonics expansions, for reducing computer memory constraints. The model iterates on step 2, for all cells, and stops with the energetic equilibrium. Two simple accelerating techniques can be used: 1) Gauss Seidel method, i.e., simulation of scattering with radiation already scattered at the iteration stage, and (2) decrease of the spherical harmonics expansion order with the iteration order. Moreover, convergence towards the energetic equilibrium is accelerated with an exponential fitting technique. This model predicts the bidirectional reflectance distribution function of 3-19 canopies. Radiation components associated with leaf volume and surface mechanisms are distin

[1] The Radiation Transfer Model Intercomparison (RAMI) initiative benchmarks canopy reflectance models under well-controlled experimental conditions. Launched for the first time in 1999, this triennial community exercise encourages the systematic evaluation of canopy reflectance models on a voluntary basis. The first phase of RAMI focused on documenting the spread among radiative transfer (RT) simulations over a small set of primarily 1-D canopies. The second phase expanded the scope to include structurally complex 3-D plant architectures with and without background topography. Here sometimes significant discrepancies were noted which effectively prevented the definition of a reliable ''surrogate truth,'' over heterogeneous vegetation canopies, against which other RT models could then be compared. The present paper documents the outcome of the third phase of RAMI, highlighting both the significant progress that has been made in terms of model agreement since RAMI-2 and the capability of/need for RT models to accurately reproduce local estimates of radiative quantities under conditions that are reminiscent of in situ measurements. Our assessment of the self-consistency and the relative and absolute performance of 3-D Monte Carlo models in RAMI-3 supports their usage in the generation of a ''surrogate truth'' for all RAMI test cases. This development then leads (1) to the presentation of the ''RAMI Online Model Checker'' (ROMC), an open-access web-based interface to evaluate RT models automatically, and (2) to a reassessment of the role, scope, and opportunities of the RAMI project in the future.

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International audienceAim: The size structure of a forest canopy is an important descriptor of the forest environment that may yield information on forest biomass and ecology. However, its variability at regional scales is poorly described or understood because of the still prohibitive cost of very high-resolution imagery as well as the lack of an appropriate methodology. We here employ a novel approach to describe and map the canopy structure of tropical forests. Location Amazonia. Methods: We apply Fourier transform textural ordination (FOTO) techniques to subsamples of very high-resolution satellite imagery freely available through virtual globe software (e.g. Google Earth®) to determine two key structural variables: apparent mean crown size and heterogeneity in crown size. A similar approach is used with artificial forest canopy images generated by the light interaction model (discrete anisotropic radiative transfer, DART) using three-dimensional stand models. The effects of sun and viewing angles are explored on both model and real data. Results: It is shown that in the case of canopies dominated by a modal size class our approach can predict mean canopy size to an accuracy of 5%. In Amazonia, we could evidence a clear macrostructure, despite considerable local variability. Apparent crown size indeed consistently increases from about 14 m in wet north-west Amazonia to more than 17 m for areas of intermediate dry season length (1–3 months) in south and east Amazonia, before decreasing again towards the ecotone with the Cerrado savanna biome. This general trend reflects the known variation of other forest physiognomic properties (height) reported for South America and Africa. Some regions show significantly greater canopy heterogeneity, a feature that may be related to substratum, perturbation rate and/or forest turnover rate. Main conclusions: Our results demonstrate the feasibility and interest of large-scale assessment of rain forest canopy structur

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

Satellite and airborne optical sensors are increasingly used by scientists, and OPEN ACCESS

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