The fourth phase of the radiative transfer model intercomparison (RAMI) exercise : Actual canopy scenarios and conformity testing

The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RI model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals. (C) 2015 The Authors. Published by Elsevier Inc.Peer reviewe

Extracting quantitative sub-pixel heterogeneity information from optical remote sensing data

Ce travail présente le développement et l’utilisation de deux approches pour quantifier le degré d’hétérogénéité intra-pixel des surfaces terrestres à partir de données de télédétection spatiale. La première approche utilise une technique d’inversion qui consiste à comparer les données mesurées par un satellite avec celles contenues dans une table de recherche pour les mêmes conditions géométriques de mesures. La table contient les valeurs modélisées de champs radiatifs émergeant du système couple végétation-atmosphère dans le cas de plusieurs types d’atmosphère et de couverts végétaux. Les réflectances spectrales sont modélisées de manière réaliste puisque que le transfert radiatif dans les couverts végétaux est représenté selon les trois dimensions de l’espace. Parmi les milieux géophysiques possibles proposés dans la table, une solution probable est sélectionnée grâce à des critères de convergence, tels que la cohérence temporelle et un niveau d’incertitude, qui peut être défini par une valeur minimale de différence entre les réflectances modélisées et celles mesurées. Une caractérisation complète de l’hétérogénéité intra-pixel de surface peut être déduite à partir de critères imposés sur les propriétés de structure et optiques des surfaces. Une série d’études portant sur l’estimation de l’unicité de différents types d’hétérogénéité de structure de surface est conduite à plusieurs échelles spatiales et montre que la modélisation radiative de surfaces terrestres homogènes est généralement capable de représenter les mesures (spectrales et angulaires) de réflectance, au moins pour un éventail donné de résolution spatiale. La seconde approche est basée sur l’étude de la «forme» de la courbe de la réflectance en fonction des angles d’observation, qui représente les propriétés d’anisotropie radiatives. On utilise le paramètre k de la fonction modifiée de Minnaert (incluse dans le modèle paramétrique RPV développé par Rahman et al. 1993 pour quantifier le degré d’hétérogénéité intra-pixel des surfaces terrestres Une valeur de k 1 se réfère à la courbe en «cloche» (les bords incurvés vers le bas) qui correspond soit à une structure homogène soit à une structure hétérogène d’un couvert végétal, à condition que ses objets présentent une structure érectophile, suffisamment sombres et opaques et avec une distribution clairsemée sur une surface relativement plus brillante. De telles conditions se produisent la plupart du temps dans la bande rouge du spectre solaire. Elles dépendent de la résolution spatiale et varient avec la brillance du sol sous-jacent. Dans un premier temps, une étude est menée sur les variations des valeurs de k en fonction de l’angle zénithal solaire pour caractériser l’orientation principale des feuilles dans les canopées homogènes, c’est-à-dire la distribution normale des feuilles. Dans un deuxième temps, on montre que la structure des couverts végétaux peut être caractérisée par des statistiques décrivant la non-stationnarité (‹H1›) et l’intermittence (‹C1›). Finalement, le plus important est la documentation de l’organisation des types de surfaces avec des propriétés d’anisotropie k > 1 dans le repère (‹H1›, ‹C1›), pour la bande rouge du spectre solaire. L’essentiel de ce manuscrit montre que l’information sur la structure des couverts végétaux peut être dérivée à partir de données multi-angulaires de télédétection spatiale. La disponibilité récente d’instruments multi-angulaires (tel que MISR) permet d’évaluer les performances de ces deux approches, et la validité des concepts qui leur sont associés. Ces résultats vont à la fois permettre d’améliorer les cartes d’occupation du sol et surtout aider à l’élaboration innovatrice de futurs instruments d’observation de la Terre.This work describes two approaches for the extraction of quantitative information on the degree of surface heterogeneity at the subpixel scale of optical remote sensing data. The first approach is a look-up-table based inversion scheme, where the spectral reflectances of realistically modelled three-dimensional vegetation canopies are pre-computed for a variety of atmospheric optical depths, and subsequently compared against actual satellite observations under identical conditions of observation and illumination. An optimal solution is selected from amongst the set of predefined surface type candidates, using criteria such as temporal consistency and required accuracy, as defined by the difference between the simulated and measured reflectance. An exhaustive characterization of the surface heterogeneity at the subpixel scale can subsequently be derived from the imposed set of optical and structural surface properties. A series of multi-scale assessments concerning the radiative uniqueness of structurally heterogeneous surface types lead to the conjecture that structurally homogeneous surface representations will generally be capable of explaining the measured (angular and spectral) reflectance data, at least over some range of spatial resolutions. The second approach is based on quantifying “the shape” of the spectral reflectance field using the modified Minnaert function parameter, k in the parametric RPV model of Rahman et al. (1993a). Values of k 1 refer to “bell-shaped” anisotropy patterns that may occur for both, structurally homogeneous and heterogeneous vegetation canopies, if their dominant structures are dark, vertically oriented, sufficiently opaque and sparsely distributed over a relatively brighter background surface Bell shaped reflectance anisotropy patterns are most likely to occur in the red spectral band. They are spatial resolution dependent and vary with the degree of soil brightness. Variations of k as a function of the solar zenith angle were shown to characterize the preferred vertical orientation of the dominant structures in spatially homogeneous leaf canopies i.e., the leaf normal distribution. Finally it is shown that vegetation canopy structure may be characterized in the small scale limit by statistical measures such as non-stationarity (‹H1›) and intermittency (‹C1›) exponents, using an ensemble of canopy height transects of different orientations and origins. More importantly, however, surface types with a bell-shaped reflectance field (in the red) are documented to form clusters with illumination dependent sizes in ‹H1› ‹C1› space. In essence, this thesis shows that information on the structure of vegetation canopies can be derived from satellite remote sensing data, provided such measurements are acquired as a function of observation angles. The recent availability of multiangular instruments (such as MISR) permitted to evaluate the feasibility of these approaches and the validity of the associated Concepts. These results, in turn, will lead to improved land cover classifications and innovative designs for future Earth Observation instruments

Extracting Quantitative Sub-Pixel Heterogeneity Information from Optical Remote Sensing Data

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Conformity testing of satellite-derived quantitative surface variables

Reliable compliance information of quantitative Earth Observation products is a prerequisite for the usage of satellite-derived evidence in 1) regulatory initiatives dealing with climate and environment-relevant matters (among others), and 2) liability debates between customers and providers of value-added (quantitative) EO products. In this contribution the quality objectives compiled by the Global Climate Observing System (GCOS) for a series of biophysical essential climate variables (ECVs) are investigated from the point of view of conformity testing as used in legal metrology. Unlike current validation efforts, conformity testing requires that the maximum permissible uncertainty of the candidate and reference ECV estimates does not exceed a predefined fraction of the applicable tolerance interval. Given that the GCOS accuracy criterion is defined with respect to a local reference it is the uncertainty of both the in situ and the satellite ECV estimates that matter. Our findings suggest that, 1) current GCOS quality objectives must be complemented before they may serve as quality requirements for conformity testing, 2) a consensus on the choice of decision rule must be sought (between data providers and users) since this has a direct impact on what is deemed compliant, and 3) the uncertainty associated with current field validation methods for biophysical ECVs is unlikely to meet the ISO-13528 criteria. The latter thus challenges the eligibility of current field validation methods to provide the reference needed in efforts assessing the GCOS conformity of third party datasets.JRC.H.5-Land Resources Managemen

On the Bias of Instantaneous FAPAR Estimates in Open-Canopy Forests

Global products of the fraction of absorbed photosynthetically active radiation (FAPAR) are operationally available from a variety of space agencies. A proper validation of these products is essential and hinges on the acquisition of accurate ground-based FAPAR estimates of the vegetation contained within the field of view of the space sensor at the time of satellite overpass. Often remotely sensed FAPAR products are defined with respect to theoretical rather than ambient illumination conditions which complicates in situ validation efforts. Similarly, the spatial complexity and substantial heights of certain plant environments may prevent the reliable sampling of certain radiation fluxes. As a consequence, many field campaigns are carried out on agricultural crops or within young tree plantations where canopy height is not an issue. This contribution compares different approaches for estimating instantaneous FAPAR in tall, open-canopy forest stands under a variety of architectural, spectral and illumination related conditions. The bias associated with these estimations is separated into a sampling error and a transfer bias. The former relates to the impact of both the number and location of the measurements whereas the latter addresses the quality of the theory that relates these measurements to the actual canopy FAPAR. Among the various methods tested it was the 2-flux FAPAR estimator (1-TPAR) that performs best in open forest canopies under typical summer conditions. The quality of the 1-TPAR canopy FAPAR estimator changes, however, with illumination conditions, foliage colour and especially with the background brightness. Similarly, the smaller the size of the area for which the FAPAR is to be estimated the larger the variability of the bias is going to be (and this irrespective of the choice of in situ estimation techniques). Evidence is provided that working under overcast sky conditions will reduce the sampling error but may well increase the transfer error when compared to clear sky conditions. A parametric relationship is developped that allows to predict the instantaneous canopy FAPAR for arbitrary diffuse-to-total-incident-radiation ratios (at any given solar zenith angle). This approach has a similar transfer bias to the 1-TPAR method when the forest floor is dark but dramatically outperforms the 2-flux approach under snowy background conditions (RMSE = 0.9934 versus 0.5801, respectively). The number of samples acquired was found to be crucial in reducing the variability of the bias of a given FAPAR estimator. Both random and grid-based sampling schemes result in similar FAPAR biases but do not lend themselves easily to the acquisition of hundreds of data points needed for reliable estimations under direct-only illumination conditions. Transect sampling¿which is shown to deliver best results if carried out at ninety degrees to the solar azimuth angle¿appears ideally suited to acquire the necessary numbers of samples enabling the generation of accurate quasi-instantaneous FAPAR estimates in open-canopy forests.JRC.DDG.H.3-Global environement monitorin

An Overview of Two Decades of Systematic Evaluations of Canopy Radiative Transfer Models

Space borne observations constitute a highly appropriate source of information to quantify and monitor Earth surface processes. The reliability that may be associated with the outcome of interpretation and assimilation efforts of these data, however, relies heavily on the actual performance of the available modeling tools. Scientists, space agencies and policy makers that want to make use or support the derivation of quantitative information from space observations must therefore have access to indicators describing the quality of the models and algorithms that are used in retrievals. As a formalization of earlier model verification efforts the RAdiation transfer Model Intercomparison (RAMI) initiative was launched in 1999 in an attempt to shed light on the reliability and accuracy of physically-based canopy radiative transfer models simulating the interactions between sunlight and vegetation. This contribution documents the evolution and achievements of RAMI and provides an outlook of challenges and opportunities that still lie ahead.JRC.DDG.H.3-Global environement monitorin

10 years of RAMI: Overview, Achievements and Outlook

In 1999 the RAdiative transfer Model Intercomparison (RAMI) activity was launched by the European Commission¿s DG Joint Research Centre (http://rami-benchmark.jrc.ec.europa.eu/) in an effort to formalise and extend earlier evaluation attempts of canopy radiative transfer (RT) models. These physically-based modeling tools simulate the transfer of radiation at or near the Earth¿s surface, i.e., in plant canopies and over soil surfaces, and are increasingly used in the quantitative interpretation of remotely sensed data sets. The accuracy and reliability of RT models thus has a direct impact on the quality of the surface products they help retrieving ¿ in this case, for example, LAI, FAPAR, and albedo ¿ and therefore also on the quality of outcomes generated by downstream applications that assimilate or ingest these products.JRC.DDG.H.3-Global environement monitorin

Abstract tree crowns in 3D radiative transfer models: Impact on simulated open-canopy reflectances

Three-dimensional (3D) radiative transfer models of vegetation canopies are increasingly used to study the reflective properties of specific land cover types and to interpret satellite-based remote sensing observations of such environments. In doing so, most 3D canopy reflectance models simplify the structural representation of individual tree crowns, for example, by using a single ellipsoidal envelope or a series of cubic volumes (known as voxels) to approximate the actual crown shape and the 3D distribution of scatterers therein. Often these tree abstractions ignore or simplify the woody architectures as well. Focusing on broad-leafed Savanna trees, this study investigates the impact that architectural simplifications may have on the fidelity of simulated satellite observations at the bottom-of-the-atmosphere for a variety of spatial resolutions, spectral bands, as well as, viewing and illumination geometries. As quality objective for the simulated bidirectional reflectance factors (BRFs) the typical uncertainty associated with vicarious calibration efforts is used, i.e., 5%. Our results indicate that the size of the voxel as well as the spectral, viewing, and illumination conditions drive the BRF bias at a given spatial resolution. The simulation of remote sensing data at medium spatial resolution is not affected by canopy abstractions except in the NIR for cases where woody structures are completely omitted. Here the BRF simulations of the abstract tree crowns exceeded the 5% tolerance limit even at spatial resolutions coarser than 100m. For high resolution satellite imagery, i.e. for nominal pixel sizes of 1×1 m2 or finer, BRF biases in excess of ±50% can occur within individual tree crowns and/or inside their shadows on the background. The sign of the local BRF bias is determined by the relative weight of the single-uncollided and single-collided BRF components.JRC.H.7-Climate Risk Managemen

RAMI4PILPS: Assessing Shortwave Radiation Fluxes in Land Surface Schemes

The purpose of the RAMI4PILPS experiment suite is to evaluate and compare the different approaches by which land surface schemes (LSS) in climate and weather prediction models quantify the radiation transfer within and beneath vegetation canopies. Given the availability of remotely-sensed, multi-annual, global datasets of radiative surface fluxes, that allow to capture both the spatial and temporal evolution of the energy partitioning (albedo, absorption, transmission) of terrestrial surfaces, RAMI4PILPS can be envisaged as a quality control mechanism that assesses the appropriateness of radiative flux formulations in current LSS in the light of future assimilation efforts of remote sensing products into climate and weather prediction models. Benefits to PILPS land surface models and their users include: 1) to quantify the typical errors associated with different modes of estimating the radiative surface fluxes in LSS; 2) to identify the impact that structural and spectral sub-grid variability may have on these flux estimates; and 3) to verify the conservation of energy at the level of the surface, as well as inconsistencies arising from the derivation of flux quantities from different sources with different levels of assumptions/simplifications. RAMI4PILPS will assess the quality of the prescribed/simulated radiative fluxes in LSS by direct comparison with reference solutions obtained from credible models identified during the third phase of the RAMI benchmarking exercise.JRC.H.3-Global environement monitorin

A model for deriving voxel-level tree leaf area density estimates from ground-based LiDAR

Forest canopy structure has long been known to be a major driver of the processes regulating the exchange of CO2 and water vapour between terrestrial ecosystems and the atmosphere. It is also an important driver of terrestrial vegetation dynamics. Information about fine-scale ecosystem structure is needed to better understand and predict how terrestrial ecosystems respond to and affect environmental change. LiDAR remote sensing from ground-based instruments is a promising technology for providing such information, and physically-based models are ideally suited to process the data and derive reliable products. While complex ray tracing algorithms have been developed to help in the interpretation of LiDAR data, none of these tools are currently widely available. In this paper we present the VoxLAD model; a parametric model using computational geometry that allows to compute estimates of leaf area density at the voxel scale on the basis of terrestrial LiDAR data. This modeling framework removes the need to compute the exact point of entry and exit into and out of the voxels for all individual laser pulses, and thus allows for easier usage. The model requires that each point in the LiDAR point cloud should be classified as wood, foliage, or noise. Here we provide the algorithmic details of the model, and demonstrate that the output of the model closely fits the output of a model using more complex ray tracing techniques.JRC.H.7-Climate Risk Managemen