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Pinty B.,European Commission - Joint Research Center Ispra | Pinty B.,Earth Observation Directorate | Widlowski J.-L.,European Commission - Joint Research Center Ispra | Verstraete M.M.,European Commission - Joint Research Center Ispra | And 5 more authors.
Geophysical Research Letters | Year: 2011

The fraction of radiation absorbed in the canopy depends on the amount and angular distribution of the solar irradiance reaching the top of the canopy as well as the fraction of this irradiance that is transmitted through the canopy gaps and reflected back to the vegetation by the background. This contribution shows that the presence of snow on forest floors enhances the fraction of absorbed Photosynthetically Active Radiation (PAR). A global analysis of satellite-derived products reveals that this enhancement affects evergreen and deciduous forests of the boreal zone. This snow-related effect may usefully contribute to the photosynthesis process in evergreen forests especially during spring time when radiation conditions are marginal but other physiological constraints (such as temperature) permit the necessary biochemical functions to take place. © 2011 by the American Geophysical Union. Source


Pinty B.,European Commission - Joint Research Center Ispra | Pinty B.,Earth Observation Directorate | Andredakis I.,European Commission - Joint Research Center Ispra | Clerici M.,European Commission - Joint Research Center Ispra | And 6 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2011

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. Copyright © 2011 by the American Geophysical Union. Source


Scholze M.,Lund University | Kaminski T.,The Inversion Laboratory | Knorr W.,Lund University | Blessing S.,FastOpt | And 3 more authors.
Remote Sensing of Environment | Year: 2016

Carbon dioxide (CO 2) is the most important anthropogenic greenhouse gas contributing to about half of the total anthropogenic change in the Earth's radiation budget. And about half of the anthropogenic CO2 emissions stay in the atmosphere, the remainder is taken up by the biosphere. It is of paramount importance to better understand CO2 sources and sinks and their spatio-temporal distribution. In the context of climate change this information is needed to improve the projections of future trends in carbon sinks and sources. Since the terrestrial carbon and water cycles are tightly coupled by biological plant processes, i.e. through the stomatal gas exchange with the atmosphere, it is expected that information on the soil moisture state will help to constrain terrestrial carbon fluxes. In the present feasibility study we employ the Carbon Cycle Data Assimilation System CCDAS to pioneer the assimilation of the SMOS L3 soil moisture product together with another biophysical data set - in this case atmospheric CO2 flask samples. The two data streams are assimilated into a process-based model of the terrestrial carbon cycle over two years. CCDAS aims to optimise model process parameters and subsequently land surface CO2 exchange fluxes. We find that the assimilation of SMOS data improves the agreement with independent soil moisture data from the active ASCAT instrument. In both cases the assimilation also improves the fit of modelled atmospheric CO2 to the observations at flask sampling sites which have not been used in the assimilation. Reduction of uncertainty relative to the prior is generally high for both regional net ecosystem productivity and net primary productivity and considerably higher than for assimilating CO2 only, which quantifies the added value of SMOS observations as a constraint on the terrestrial carbon cycle. The study demonstrates a high potential for a SMOS L4 carbon flux product. © 2016 Elsevier Inc. Source


Kaminski T.,FastOpt | Rayner P.J.,University of Melbourne | Vobeck M.,FastOpt | Scholze M.,University of Bristol | Koffi E.,LSCE IPSL
Atmospheric Chemistry and Physics | Year: 2012

This paper investigates the relationship between the heterogeneity of the terrestrial carbon cycle and the optimal design of observing networks to constrain it. We combine the methods of quantitative network design and carbon-cycle data assimilation to a hierarchy of increasingly heterogeneous descriptions of the European terrestrial biosphere as indicated by increasing diversity of plant functional types. We employ three types of observations, flask measurements of CO2 concentrations, continuous measurements of CO 2 and pointwise measurements of CO 2 flux. We show that flux measurements are extremely efficient for relatively homogeneous situations but not robust against increasing or unknown complexity. Here a hybrid approach is necessary, and we recommend its use in the development of integrated carbon observing systems. © 2012 Author(s). Source


Kaminski T.,FastOpt | Scholze M.,University of Bristol | Houweling S.,SRON Netherlands Institute for Space Research
Tellus, Series B: Chemical and Physical Meteorology | Year: 2010

ESA's Earth Explorer candidate mission A-SCOPE aims at observing CO2 from space with an active LIDAR instrument. This study employs quantitative network design techniques to investigate the benefit of A-SCOPE observations in a Carbon Cycle Data Assimilation System. The system links the observations to the terrestrial vegetation model BETHY via the fine resolution version of the atmospheric transport model TM3. In the modelling process chain the observations are used to reduce uncertainties in the values of BETHY's process parameters, and then the uncertainty in the process parameters is mapped forward to uncertainties in both in long-term net carbon flux and net primary productivity over three regions. A-SCOPE yields considerably better reductions in posterior uncertainties than the ground-based GLOBALVIEW station network. This is true for assimilating monthly mean values and instantaneous values, and it is true for two potential vertical weighting functions. The strength of the constraint through A-SCOPE observations is high over the range of observational uncertainties. © 2010 The Authors Tellus B © 2010 International Meteorological Institute in Stockholm. Source

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