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The synergic use of satellite data at moderate spatial resolution (i.e., 20–30 m) from the new Collection 2 (C2) Landsat-8/9 (L8/9) Operational Land Imager (OLI) and Sentinel-2 (S2) Multispectral Instrument (MSI) provides a new perspective in the remote sensing applications for gas flaring (GF) identification and monitoring, thanks to a significant improvement in the revisiting time (up to ~3 days). In this study, the daytime approach for gas flaring investigation (DAFI), recently developed for identifying, mapping and monitoring GF sites on a global scale using the L8 infrared radiances, has been ported on a virtual constellation (VC) (formed by C2 L8/9 + S2) to assess its capability in understanding the GF characteristics in the space-time domain. The findings achieved for the regions of Iraq and Iran, ranked at the second and third level among the top 10 gas flaring countries in 2022, demonstrate the reliability of the developed system, with improved levels of accuracy and sensitivity (+52%). As an outcome of this study, a more realistic picture of GF sites and their behavior is achieved. A new step aimed at quantifying the GFs radiative power (RP) has been added in the original DAFI configuration. The preliminary analysis of the daily OLI- and MSI-based RP, provided for all the sites by means of a modified RP formulation, revealed their good matching. An agreement of 90% and 70% between the annual RPs computed in Iraq and Iran and both their gas-flared volumes and carbon dioxide emissions were also recorded. Being that gas flaring is one of the main sources of greenhouse gases (GHG) worldwide, the RP products may concur to infer globally the GHGs GF emissions at finer spatial scales. For the presented achievements, DAFI can be seen as a powerful satellite tool able to automatically assess the gas flaring dimension on a global scale. [ABSTRACT FROM AUTHOR]

Climate-induced drought events are responsible for forest decline and mortality in different areas of the world. Forest response to drought stress periods may be different, in time and space, depending on vegetation type and local factors. Stress analysis may be carried out by using field methods, but the use of remote sensing may be needed to highlight the effects of climate-change-induced phenomena at a larger spatial and temporal scale. In this context, satellite-based analyses are presented in this work to evaluate the drought effects during the 2000s and the possible climatological forcing over oak forests in Southern Italy. To this aim, two approaches based on the well-known Normalized Difference Vegetation Index (NDVI) were used: one based on NDVI values, averaged over selected decaying and non-decaying forests; another based on the Robust Satellite Techniques (RST). The analysis of the first approach mainly gave us overall information about 1984–2011 rising NDVI trends, despite a general decrease around the 2000s. The second, more refined approach was able to highlight a different drought stress impact over decaying and non-decaying forests. The combined use of the RST-based approach, Landsat satellite data, and Google Earth Engine (GEE) platform allowed us to identify in space domain and monitor over time significant oak forest changes and climate-driven effects (e.g., in 2001) from the local to the Basilicata region scale. By this way, the decaying status of the Gorgoglione forest was highlighted two years before the first visual field evidence (e.g., dryness of apical branches, bark detachment, root rot disease). The RST exportability to different satellite sensors and vegetation types, the availability of suitable satellite data, and the potential of GEE suggest the possibility of long-term monitoring of forest health, from the local to the global scale, to provide useful information to different end-user classes. [ABSTRACT FROM AUTHOR]

The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2A/2B and Landsat 8 satellites, thanks to their features especially in terms of spatial/spectral resolution, represents two important instruments for investigating thermal volcanic activity from space. In this study, we used data from those sensors to test an original multichannel algorithm, which aims at mapping volcanic thermal anomalies at a global scale. The algorithm, named Normalized Hotspot Indices (NHI), combines two normalized indices, analyzing near infrared (NIR) and short wave infrared (SWIR) radiances, to identify hotspot pixels in daylight conditions. Results, achieved studying a number of active volcanoes located in different geographic areas and characterized by a different eruptive behavior, demonstrated the NHI capacity in mapping both subtle and more intense volcanic thermal anomalies despite some limitations (e.g., missed detections because of clouds/volcanic plumes). In addition, the study shows that the performance of NHI might be further increased using some additional spectral/spatial tests, in view of a possible usage of this algorithm within a known multi-temporal scheme of satellite data analysis. The low processing times and the straight forth exportability to data from other sensors make NHI, which is sensitive even to other high temperature sources, suited for mapping hot volcanic targets integrating information provided by current and well-established satellite-based volcanoes monitoring systems. [ABSTRACT FROM AUTHOR]