During the oil extraction procedure, natural gases escape from wells, and the process of recuperating such gases requires important investments from oil and gas companies. That is why, most often, they favor burning them with flares. This practice, which is frequently employed by oil-producing companies, is a major cause of greenhouse gas emissions. Under growing demands from the World Bank and environmental defenders, many producer countries are devoted to decreasing gas flaring. For this reason, several researchers in the oil and gas industry, academia, and governments are working to propose new methods for estimating flared gas volumes, and among the most used techniques are those that exploit remote sensing data, particularly Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light (NTL) ones. Indeed, it is possible to extract, from such data, some physical parameters of flames produced by gas flares. In this investigation, a linear spectral unmixing-based approach, which addresses the spectral variability phenomenon, was designed to estimate accurate physical parameters from VIIRS NTL data. Then, these parameters are used to derive flared gas volumes through intercepting zero polynomial regression models that exploit in situ measurements. Experiments based on synthetic data were first conducted to validate the proposed linear spectral unmixing-based approach. Second, experiments based on real VIIRS NTL data covering the flare, named FIT-M8-101A-1U and located in the Berkine basin (Hassi Messaoud) in Algeria, were carried out. Then, the obtained flared gas volumes were compared with in situ measurements.

Land degradation neutrality as a global goal, needs a permanent spatial monitoring. However, the spatial landcover maps produced until now, by the African countries (to supply the international studies/statistics) have been often full with confusion (medium quality) especially in the vegetal classes. In order to provide a more precision, the aim of this work is to produce landcover maps at a national level of Algeria, using a multisource approach (based on MODIS scenes) using DEM data, NDVI layers, supervised classification and high-resolution image for validation from the Algerian optical satellites “Alsat 2”.

Estimation of solar irradiance received on the ground is essential in many solar energy applications, particularly those relying on concentrating solar technologies. In this work, we propose a model to estimate the spatial distribution of global Surface Solar Irradiance (SSI) from a Bird & Hulstrom radiometric model/MODIS atmospheric data combination. The theoretical model selected is the radiometric model of Bird & Hulstrom, and it has been modified to take into account the effect of the atmosphere by adding a transmittance function that obeys the Beer-Lambert law, implemented with atmospheric parameters (water vapor, ozone, aerosols, etc.) from the Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products. The effectiveness of the proposed methodology is tested on the Algerian territory. The obtained results show that the proposed approach concurs with results provided by the tested Meteosat data-based method while providing SSI maps of better spatial resolution. Moreover, the obtained solar irradiances show a root mean square error of about 140 Wh.m-2 in comparison with the Copernicus Atmosphere Monitoring Service (CAMS) radiation extracted from the Solar Radiation Database for the environment (SoDa) server.

Exploiting remote sensing data is a promising approach to estimate surface solar irradiance (SSI). In this study, we propose a method to estimate global SSI using a lookup table and Landsat data. Despite the low temporal resolution of the data used, the developed method produces SSI maps with adequate spatial resolution. It combines physical parameters extracted from Landsat metadata files with the physical laws governing global solar irradiance, its transmission through the atmosphere, and surface reflectance. The results obtained are compared with those in the literature, particularly one study that uses Meteosat data and two others that use radiometric spectral and temporal models. Additionally, experiments are conducted at three sites in Algeria: Oran, In Amenas, and Tamanghasset. The findings indicate that the proposed approach aligns with the tested literature methods while providing SSI maps with superior spatial resolution. Furthermore, the obtained solar irradiances exhibit a root mean square error of approximately 190 W.m-2 .µm-1 compared with those of the Bird and Riordan spectral model, and approximately 50 W•m-2 compared with the results from the Bird and Hulstrom temporal model, and are also comparable to the results of previous studies.