Methane (CH₄) is one of the most contributing anthropogenic greenhouse gases (GHGs) in terms of global warming. Industry is one of the largest anthropogenic sources of methane, which are currently only roughly estimated. New satellite hyperspectral imagers, such as PRISMA, open up daily temporal monitoring of industrial methane sources at a spatial resolution of 30 m. Here, we developed the Characterization of Effluents Leakages in Industrial Environment (CELINE) code to inverse images of the Korpezhe industrial site. In this code, the in-Scene Background Radiance (ISBR) method was combined with a standard Optimal Estimation (OE) approach. The ISBR-OE method avoids the use of a complete and time-consuming radiative transfer model. The ISBR-OEM developed here overcomes the underestimation issues of the linear method (LM) used in the literature for high concentration plumes and controls a posteriori uncertainty. For the Korpezhe site, using the ISBR-OEM instead of the LM -retrieved CH₄ concentration map led to a bias correction on CH₄ mass from 4 to 16% depending on the source strength. The most important CH₄ source has an estimated flow rate ranging from 0.36 ± 0.3 kg·s⁻¹ to 4 ± 1.76 kg·s⁻¹ on nine dates. These local and variable sources contribute to the CH₄ budget and can better constrain climate change models.

This paper synthesizes the technical requirements made by a group of French scientists and defence users expert in hyperspectral imagery to design a new space borne imaging spectrometer. This project called HYPXIM is currently in phase 0 mission study and two French aerospace companies have proposed solutions that are analysed by the CNES. These technical requirements are converted into at-sensor radiance specifications for each scientific application and the final radiance set used for the instrument design is defined.

Hyperspectral sensors generally acquire images in the spectral range in more than one hundred contiguous narrow channels with a (deca)metric spatial resolution. Each pixel of the image is thus associated with a continuous spectrum which can be used to identify or map surface minerals. The most powerful algorithms (e.g., USGS (United States Geological Survey) Tetracorder) run with a standardized spectral library, are often supervised and require some expert knowledge. In this paper, we present an original method for mineral identification and mapping. Its originality lies in its fully automatic functioning for the full spectral range, from initialization using spectral derivatives, to spectral deconvolution and mineral identification, with a global approach. The modelling combines exponential Gaussians, a continuum including the fundamental water absorption at and deals with overfitting to keep only the relevant Gaussians. We tested the method in the SWIR (Short-Wave InfraRed, ) and for 14 minerals representative of industrial environments (e.g., quarries, mines, industries). More than 98% of the simulated spectra were correctly identified. When applied to two AVIRIS (Airborne Visible/InfraRed Imaging Spectrometer) images, results were consistent with ground truth data. The method could be improved by extending it to the VNIR (Visible and Near-InfraRed, ) spectral range to include iron oxides and by managing spectral mixtures.

Hyperspectral imagery is a widely used technique to study atmospheric composition. For several years, many methods have been developed to estimate the abundance of gases. However, existing methods do not simultaneously retrieve the properties of aerosols and often use standard aerosol models to describe the radiative impact of particles. This approach is not suited to the characterization of plumes, because plume particles may have a very different composition and size distribution from aerosols described by the standard models given by radiative transfer codes. This article presents a new method to simultaneously retrieve carbon dioxide (CO₂) and aerosols inside a plume, combining an aerosol retrieval algorithm using visible and near-infrared (VNIR) wavelengths and a CO₂ estimation algorithm using shortwave infrared (SWIR) wavelengths. The microphysical properties of the plume particles, obtained after aerosol retrieval, are used to calculate their optical properties in the SWIR. Then, a database of atmospheric terms is generated with the radiative transfer code, Moderate Resolution Atmospheric Transmission (MODTRAN). Finally, pixel radiances around the 2.0 μm absorption feature are used to retrieve the CO₂ abundances. After conducting a signal sensitivity analysis, the method was applied to two airborne visible/infrared imaging spectrometer (AVIRIS) images acquired over areas of biomass burning. For the first image, in situ measurements were available. The results show that including the aerosol retrieval step before the CO₂ estimation: (1) induces a better agreement between in situ measurements and retrieved CO₂ abundances (the CO₂ overestimation of about 15%, induced by neglecting aerosols has been corrected, especially for pixels where the plume is not very thick); (2) reduces the standard deviation of estimated CO₂ abundance by a factor of four; and (3) causes the spatial distribution of retrieved concentrations to be coherent.