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.

Surface skin temperature (T_skin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving T_skin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI T_skin product (i.e., T_ANN) is evaluated against T_skin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI T_ANN and the T_skin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust T_skin dataset suitable for T_skin long-term spatio-temporal variability and trend analysis.

We propose a new denoising method for 3D hyperspectral images for the future MetOp-Second Generation series satellite incorporating the new IASI-NG interferometer, to be launched in 2021. This adaptive method retrieves the data model directly from the input noisy granule, using the following techniques: dual clustering (spectral and spatial), dimensionality reduction by adaptive PCA, and Bayesian denoising. The use of dimensionality reduction by PCA has been already proven an effective denoising technique because of intrinsic data redundancy. We demonstrate here that by combining a local PCA dimensionality reduction with a dual clustering and a Bayesian denoising, it is possible to improve significantly the PSNR with respect to PCA reduction alone. This noise reduction hints at the possibility to multiply of the resolution of the satellite by factor 4, while keeping an acceptable SNR.