Search

Operational weather geostationary satellites have acquired data for more than two decades and offer thus the possibility to generate long time series of Essential Climate Variables like surface albedo as suggested by GCOS. This paper investigates the possibility to generate consistent global, to the exception of the polar regions, surface broadband albedo product from these satellites. In this context, the paper addresses two specific issues. Firstly, the spatial consistency of surface albedo derived from five different geostationary satellites is examined in detail. Secondly, this product is compared with the equivalent MODIS one to define the temporal consistency between surface albedo derived with old geostationary instruments and technologically advanced radiometers like MODIS. The analysis of the surface albedo products has revealed a good agreement between the products derived from the various geostationary satellites. Comparison of this global product with the one routinely derived from MODIS shows that on the average, the mean relative difference between these two data sets agree within 10%. These first encouraging results open therefore a new avenue for the exploitation of the archived data for the generation of long time series, covering the last 25 years or so, of global surface albedo from geostationary weather satellites.

This paper discusses the accuracy of the operational Medium Resolution Imaging Spectrometer (MERIS) Level 2 land product which corresponds to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The FAPAR value is estimated from daily MERIS spectral measurements acquired at the top-of-atmosphere, using a physically based approach. The products are operationally available at the reduced spatial resolution, i.e. 1.2 km, and can be computed at the full spatial resolution, i.e. at 300 m, from the top-of-atmosphere MERIS data by using the same algorithm. The quality assessment of the MERIS FAPAR products capitalizes on the availability of five years of data acquired globally. The actual validation exercise is performed in two steps including, first, an analysis of the accuracy of the FAPAR algorithm itself with respect to the spectral measurements uncertainties and, second, with a direct comparison of the FAPAR time series against ground-based estimations as well as similar FAPAR products derived from other optical sensor data. The results indicate that the impact of top-of-atmosphere radiance uncertainties on the operational MERIS FAPAR products accuracy is expected to be at about 5–10% and the agreement with the ground-based estimates over different canopy types is achieved within ± 0.1.

The capability of the non-linear Rahman–Pinty–Verstraete (RPV) model to 1) accurately fit a large variety of Bidirectional Reflectance Factor (BRF) fields and 2) return parameter values of interest for land surface applications motivate the development of a computer efficient inversion package. The present paper describes such a package based on the 3 and 4 parameter versions of the RPV model. This software environment implements the adjoint code, generated using automatic differentiation techniques, of the cost function. This cost function itself balances two main contributions reflecting 1) the a priori knowledge on the model parameter values and, 2) BRF uncertainties together with the requirement to minimize the mismatch between the measurements and the RPV simulations. The individual weights of these contributions is specified notably via covariance matrices of the uncertainties in the a priori knowledge on the model parameters and the observations. This package also reports on the probability density functions of the retrieved model parameter values that thus permit the user to evaluate the a posteriori uncertainties on these retrievals. This is achieved by evaluating the Hessian of the cost function at its minimum. Results from a variety of tests are shown in order to document and analyze software performance against complex synthetic BRF fields simulated by radiation transfer models as well as against actual MISR-derived surface BRF products.

This article demonstrates the use of spectral decomposition for analyzing the spectral response of different semiarid vegetation species found throughout Mediterranean Europe. Using this technique, it is possible to decompose a spectral data set into a smaller number of significant factors that represent the key variables affecting vegetation spectral response. The results presented here show how spectral decomposition can be used to determine the intrinsic number and identity of the significant factors affecting the multispectral response. For the dataset investigated here, which comprises field spectral recorded over 1130 wavelengths, using a GER single field-of-view IRIS (SIRIS) spectroradiometer, it was found that a combination of just four factors was responsible for the majority of spectral variance. Interpretation of these factors was carried out by graphical analysis stepwise regeneration of the original spectra, and correlation with biophysical data. Considering the identity of these factors, it was found that the second most significant factor (factor 2) was strongly related to the proportion of directly irradiated green leaves within the field-of-view of the spectradiometer. In addition, it was found that the fourth most significant factor (factor 4) provided a good summary of the spectral response of the different samples in the region of strong chlorophyll absorption. This demonstrates the possibility of using spectral decomposition techniques, particularly in environments dominated by spectrally similar vegetation classes, to model the mixed spectral population as mixtures of fundamental biophysical parameters rather than as mixtures of the classes themselves.

[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.

[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.

[1] The broadband white sky surface albedo (bihemispherical reflectance) products available from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared at regional and continental scales with similar products generated from the Multiangle Imaging Spectroradiometer (MISR) land surface bidirectional reflectance factor (BRF) parameters. This paper describes the methodology applied to derive MISR white sky albedos over four spectral broadbands of interest, namely, 0.3–0.7 μm, 0.4–1.1 μm, 0.7–3.0 μm, and 0.3–3.0 μm, as well as an evaluation of the strategy adopted to compare the MODIS and MISR products. The results are very encouraging since the two data sets show very good statistical agreement over large areas and over a full year of measurements, despite the many differences that exist in the suite of algorithms applied to retrieve these surface quantities from each of these instruments separately.

The main goal of this study is to help bridge the gap between available remote sensing products and large-scale global climate models. We present results from the application of an inversion method conducted using both MODIS and MISR derived broadband visible and near-infrared surface albedo products. This contribution is an extension of earlier efforts to optimally retrieve land surface fluxes and associated two-stream model parameters (pinty etal., 2007). It addresses complex geophysical scenarios involving snow occurrence in mid and high-latitude evergreen and deciduous forest canopy systems. The detection of snow during the winter and spring seasons is based on the MODIS snow product. This information is used by our package to adapt the prior values, specifically the maximum likelihood and width of the 2-D probability density functions (PDF) characterizing the background conditions of the forest floor. Our results (delivered as a Gaussian approximation of the PDFs of the retrieved model parameter values and radiant fluxes) illustrate the capability of the inversion package to retrieve meaningful land vegetation fluxes and associated model parameters during the year, despite the rather high temporal variability in the input products, in large part due to the occurence of snow events. As a matter of fact, most of this temporal variability, as well as the small differences between the MODIS and MISR broadband albedos, appear to be largely captured by the albedo of the forest canopy background., JRC.H.5-Land Resources Management

[1] We present a computer-efficient software package enabling us to assimilate operational remote-sensing flux products into a state-of-the-art two-stream radiation transfer scheme suitable for climate models. This package implements the adjoint and Hessian codes, generated using automatic differentiation techniques, of a cost function balancing (1) the deviation from the a priori knowledge on the model parameter values and (2) the misfit between the observed remote-sensing fluxes and the two-stream model simulations. The individual weights of these contributions are specified notably via covariance matrices of the uncertainties in the a priori knowledge on the model parameters and the measurements. The proposed procedure delivers a Gaussian approximation of the PDFs of the retrieved model parameter values. The a posteriori covariance matrix is further exploited to evaluate, in turn, the posterior probability density functions of the radiant fluxes simulated by the two-stream model, including those that are not measured, for example, the fraction of radiation absorbed in the ground. Applications are conducted using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) broadband surface albedo products. It turns out that the differences between these two albedo sets may translate into discernible signatures on some retrieved model parameters. Meanwhile, adding the Joint Research Centre (JRC)-Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Sea-viewing Wide Field-of-view Sensor (SeaWiFS) products into the measurements yields a significant reduction of uncertainties. Results from these applications indicate that the products retrieved from the two-stream inversion procedure (1) exhibit much less variability than those generated by the operational algorithms for the LAI and FAPAR, and (2) are in good agreement with the available ground-based estimates.

International audience; 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.

[1] This paper discusses the quality and the accuracy of the Joint Research Center (JRC) fraction of absorbed photosynthetically active radiation (FAPAR) products generated from an analysis of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. The FAPAR value acts as an indicator of the presence and state of the vegetation and it can be estimated from remote sensing measurements using a physically based approach. The quality of the SeaWiFS FAPAR products assessed in this paper capitalizes on the availability of a 6-year FAPAR time series over the full globe. This evaluation exercise is performed in two phases involving, first, an analysis of the verisimilitude of the FAPAR products under documented environmental conditions and, second, a direct comparison of the FAPAR values with ground-based estimations where and when the latter are available. This second phase is conducted following a careful analysis of problems arising for performing such a comparison. This results in the grouping of available field information into broad categories representing different radiative transfer regimes. This strategy greatly helps the interpretation of the results since it recognizes the various levels of difficulty and sources of uncertainty associated with the radiative sampling of different types of vegetation canopies.

[1] The Radiation Transfer Model Intercomparison (RAMI) initiative is a community-driven exercise to benchmark the models of radiation transfer (RT) used to represent the reflectance of terrestrial surfaces. Systematic model intercomparisons started in 1999 as a self-organized, open-access, voluntary activity of the RT modeling community. The results of the first phase were published by Pinty et al. [2001]. The present paper describes the benchmarking protocol and the results achieved during the second phase, which took place during 2002. This second phase included two major components: The first one included a rerun of all direct-mode tests proposed during the first phase, to accommodate the evaluation of models that have been upgraded since, and the participation of new models into the entire exercise. The second component was designed to probe the performance of three-dimensional models in complex heterogeneous environments, which closely mimic the observations of actual space instruments operating at various spatial resolutions over forest canopy systems. Phases 1 and 2 of RAMI both confirm not only that a majority of the radiation transfer models participating in RAMI are in good agreement between themselves for relatively simple radiation transfer problems but also that these models exhibit significant discrepancies when considering more complex but nevertheless realistic geophysical scenarios. Specific recommendations are provided to guide the future of this benchmarking program (Phase 3 and beyond).