.[1] This contribution illustrates results from a large-scale application of the Joint Research Centre Two-stream Inversion Package (JRC-TIP), using MODIS broadband visible and near-infrared white sky surface albedos as inputs. The discussion focuses on products (based on the mean and one-sigma values of the probability distribution functions (PDFs)) obtained during the summer and winter. This paper discusses the retrieved model parameters including the effective leaf area index (LAI), the background brightness, and the scattering efficiency of the vegetation elements. The similarity between the derived LAI seasonal maps and earlier distributions of this variable comforts us in the quality of the albedo products as well as in the ability of the JRC-TIP to interpret the latter meaningfully. The opportunity to generate global maps of new products, such as the background albedo, underscores the advantages of using state of the art algorithmic approaches capable of fully exploiting accurate satellite remote sensing data sets. The detailed analyses of the retrieval uncertainties highlight the central role and contribution of the LAI, the main process parameter to interpret radiation transfer observations over vegetated surfaces. The estimation of the radiation fluxes that are absorbed, transmitted, and scattered by the vegetation layer and its background is achieved on the basis of the retrieved PDFs of the model parameters. Results from this latter step are discussed in a companion paper.

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[1] The two-stream model parameters and associated uncertainties retrieved by inversion against MODIS broadband visible and near-infrared white sky surface albedos were discussed in a companion paper. The present paper concentrates on the partitioning of the solar radiation fluxes delivered by the Joint Research Centre Two-stream Inversion Package (JRC-TIP). The estimation of the various flux fractions related to the vegetation and the background layers separately capitalizes on the probability density functions of the model parameters discussed in the companion paper. The propagation of uncertainties from the observations to the model parameters is achieved via the Hessian of the cost function and yields a covariance matrix of posterior parameter uncertainties. This matrix is propagated to the radiation fluxes via the model's Jacobian matrix of first derivatives. Results exhibit a rather good spatiotemporal consistency given that the prior values on the model parameters are not specified as a function of land cover type and/or vegetation phenological states. A specific investigation based on a scenario imposing stringent conditions of leaf absorbing and scattering properties highlights the impact of such constraints that are, as a matter of fact, currently adopted in vegetation index approaches. Special attention is also given to snow-covered and snow-contaminated areas since these regions encompass significant reflectance changes that strongly affect land surface processes. A definite asset of the JRC-TIP lies in its capability to control and ultimately relax a number of assumptions that are often implicit in traditional approaches. These features greatly help us understand the discrepancies between the different data sets of land surface properties and fluxes that are currently available. Through a series of selected examples, the inverse procedure implemented in the JRC-TIP is shown to be robust, reliable, and compliant with large-scale processing requirements. Furthermore, this package ensures the physical consistency between the set of observations, the two-stream model parameters, and radiation fluxes. It also documents the retrieval of associated uncertainties.

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Volcanic eruptions pose a significant risk to human lives, property and infrastructure, despite rapid advances in monitoring and early warning science and technology. Some elements of risk -such as the number of people living close to volcanoes -are increasing, and the unpredictable nature of eruptions may overwhelm the local response capacity and turn into a disaster, sometimes requiring international assistance. To deal effectively with these crises, the international humanitarian community needs a global, science-based early warning system that should assimilate the state-of-the-art monitoring and early warning techniques, as well as being able to provide a preliminary impact assessment, and issue appropriate and relevant alerts. Current volcano warning systems are either only local in context or are not suited to the needs of global early warning.In this paper we propose an outline for a volcano warning system aimed at issuing alerts to the humanitarian aid community. It is designed as a four-level system, incorporating the latest monitoring and hazard modelling techniques that are applicable on a global scale. Alerts are mainly based on the predicted humanitarian impact of the modelled hazards. Systematic handling of volcanic manifestations, such as thermal signals and ash clouds from space-borne instruments, make it possible to create such a system. The Global Disaster Alert and Coordination System (GDACS), a joint effort by the United Nations and the European Commission, has been operating in a similar spirit for other natural disasters for a number of years and could fulfil the role of the desired volcano system. This paper discusses the needs and issues of this undertaking.