The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics‐based radiative transfer (RT) models under controlled experimental conditions. RAMI focuses on computer simulation models that mimic the interactions of radiation with plant canopies. These models are increasingly used in the development of satellite retrieval algorithms for terrestrial essential climate variables (ECVs). Rather than applying ad hoc performance metrics, RAMI‐IV makes use of existing ISO standards to enhance the rigor of its protocols evaluating the quality of RT models. ISO‐13528 was developed “to determine the performance of individual laboratories for specific tests or measurements.” More specifically, it aims to guarantee that measurement results fall within specified tolerance criteria from a known reference. Of particular interest to RAMI is that ISO‐13528 provides guidelines for comparisons where the true value of the target quantity is unknown. In those cases, “truth” must be replaced by a reliable “conventional reference value” to enable absolute performance tests. This contribution will show, for the first time, how the ISO‐13528 standard developed by the chemical and physical measurement communities can be applied to proficiency testing of computer simulation models. Step by step, the pre‐screening of data, the identification of reference solutions, and the choice of proficiency statistics will be discussed and illustrated with simulation results from the RAMI‐IV “abstract canopy” scenarios. Detailed performance statistics of the participating RT models will be provided and the role of the accuracy of the reference solutions as well as the choice of the tolerance criteria will be highlighted.

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C‐band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low‐density vegetation, using low‐incidence‐angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high‐density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal‐ and vertical‐polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual‐polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture.

[1] Remotely sensed, multiannual data sets of shortwave radiative surface fluxes are now available for assimilation into land surface schemes (LSSs) of climate and/or numerical weather prediction models. The RAMI4PILPS suite of virtual experiments assesses the accuracy and consistency of the radiative transfer formulations that provide the magnitudes of absorbed, reflected, and transmitted shortwave radiative fluxes in LSSs. RAMI4PILPS evaluates models under perfectly controlled experimental conditions in order to eliminate uncertainties arising from an incomplete or erroneous knowledge of the structural, spectral and illumination related canopy characteristics typical for model comparison with in situ observations. More specifically, the shortwave radiation is separated into a visible and near‐infrared spectral region, and the quality of the simulated radiative fluxes is evaluated by direct comparison with a 3‐D Monte Carlo reference model identified during the third phase of the Radiation transfer Model Intercomparison (RAMI) exercise. The RAMI4PILPS setup thus allows to focus in particular on the numerical accuracy of shortwave radiative transfer formulations and to pinpoint to areas where future model improvements should concentrate. The impact of increasing degrees of structural and spectral subgrid variability on the simulated fluxes is documented and the relevance of any thus emerging biases with respect to gross primary production estimates and shortwave radiative forcings due to snow and fire events are investigated.

Among others, the wavelet-based pansharpening approach tries to enhance the resolution of the multispectral (MS) image by injection of spatial details extracted from the high-resolution panchromatic (PAN) image. The problem is presented as follows, the inputs are a coarse-resolution MS image and a high-resolution detail image provided from the PAN image; therefore, one would think that the wavelet reconstruction allows combining approximations and details to construct the high-resolution MS image. However, the wavelet transform (WT) assumes that details and approximations are calculated using the same wavelet decomposition. Now, in the pansharpening case, the MS low-resolution image is assumed to be aliased and blurred due to the imaging system modulation transfer function (MTF) that is approximated as a specific low-pass filter. Meanwhile, there are no constraints about details that can be extracted from PAN using discrete WT (DWT). Approximation and details are not any more orthogonal as needed in the reconstruct of the MS high-resolution image based on DWT. For that, we propose in this paper a new fusion schema [coupled multiresolution decomposition model (CMD)] allowing the reconstruction of a high-resolution MS given its approximation and details obtained by MTF-tailored downsampling and wavelet decomposition, respectively. For validation, CMD is applied to Pléiades, GeoEye-1, and SPOT 6 images. Compared to other approaches [i.e., Gram-Schmidt (GS) adaptive, GS mode 2 (GS2), “À trous' WT (AWT), generalized Laplacian pyramid (GLP), DWT, and PCI Geomatics software algorithm], our method performs generally better.

Thermal infrared data are widely used for surface flux estimation giving the possibility to assess water and energy budgets through land surface temperature (LST). Many applications require both high spatial resolution (HSR) and high temporal resolution (HTR), which are not presently available from space. It is therefore necessary to develop methodologies to use the coarse spatial/high temporal resolutions LST remote‐sensing products for a better monitoring of fluxes at appropriate scales. For that purpose, a data assimilation method was developed to downscale LST based on particle filtering. The basic tenet of our approach is to constrain LST dynamics simulated at both HSR and HTR, through the optimization of aggregated temperatures at the coarse observation scale. Thus, a genetic particle filter (GPF) data assimilation scheme was implemented and applied to a land surface model which simulates prior subpixel temperatures. First, the GPF downscaling scheme was tested on pseudoobservations generated in the framework of the study area landscape (Crau‐Camargue, France) and climate for the year 2006. The GPF performances were evaluated against observation errors and temporal sampling. Results show that GPF outperforms prior model estimations. Finally, the GPF method was applied on Spinning Enhanced Visible and InfraRed Imager time series and evaluated against HSR data provided by an Advanced Spaceborne Thermal Emission and Reflection Radiometer image acquired on 26 July 2006. The temperatures of seven land cover classes present in the study area were estimated with root‐mean‐square errors less than 2.4 K which is a very promising result for downscaling LST satellite products.