Monitoring the Mauna Loa (Hawaii) eruption of November–December 2022 from space: Results from GOES-R, Sentinel-2 and Landsat-8/9 observations

Mauna Loa, one of the most actives volcanoes on Earth, is a shield volcano, located on the Island of Hawaii (USA). On 27 November 2022, after about 38 years of quiescence, a new eruptive activity took place at the Moku‘āweoweo caldera, continuing in the following days (i.e. until 10 December) from the fissure vents opening on the Northeast Rift Zone. In this work, we investigate the Mauna Loa November − December 2022 eruption from space, integrating the information from different satellite sensors. The analysis of short-wave infrared (SWIR) data, at 10 min temporal resolution, from the Advanced Baseline Imager (ABI), aboard the Geostationary Operational Environmental Satellites − R series (GOES-R), performed through the Normalised Hotspot Indices (NHI), indicates that the Mauna Loa eruption started on 27 November in between 23:10–23:20 LT (28 November at 09:10–09:20 UTC). The same analysis shows the increase of thermal activity and its progressive reduction from the early morning of 28 November, in agreement with the eruption migration from the summit caldera to the Northeast Rift Zone. By analysing the second phase of eruption through SWIR data from the Multispectral Instrument (MSI) and Operational Land Imager (OLI), respectively aboard Sentinel-2 and Landsat 8/9 satellites, we estimated a maximum lava flow length of 17 km. Moreover, we retrieved values of the volcanic radiative power (VRP) up to 65 GW, and a time-averaged discharge rate (TADR) of ∼1000 (±500) m3/s. These results show that SWIR observations, at different spatial and temporal resolution, may give an important contribution to the monitoring, mapping and characterisation of intense lava effusions

The VIIRS-Based RST-FLARE configuration: The Val d'Agri Oil Center Gas Flaring Investigation in between 2015-2019

The RST (Robust Satellite Techniques)-FLARE algorithm is a satellite-based method using a multitemporal statistical analysis of nighttime infrared signals strictly related to industrial hotspots, such as gas flares. The algorithm was designed for both identifying and characterizing gas flares in terms of radiant/emissive power. The Val d'Agri Oil Center (COVA) is a gas and oil pre-treatment plant operating for about two decades within an anthropized area of Basilicata region (southern Italy) where it represents a significant potential source of social and environmental impacts. RST-FLARE, developed to study and monitor the gas flaring activity of this site by means of MODIS (Moderate Resolution Imaging Spectroradiometer) data, has exported VIIRS (Visible Infrared Imaging Radiometer Suite) records by exploiting the improved spatial and spectral properties offered by this sensor. In this paper, the VIIRS-based configuration of RST-FLARE is presented and its application on the recent (2015-2019) gas flaring activity at COVA is analyzed and discussed. Its performance in gas flaring characterization is in good agreement with VIIRS Nightfire outputs to which RST-FLARE seems to provide some add-ons. The great consistency of radiant heat estimates computed with both RST-FLARE developed configurations allows proposing a multi-sensor RST-FLARE strategy for a more accurate multi-year analysis of gas flaring

Monitoring the Agung (Indonesia) ash plume of November 2017 by means of infrared Himawari 8 data

The Agung volcano (Bali; Indonesia) erupted in later November 2017 after several years of quiescence. Because of ash emissions, hundreds of flights were cancelled, causing an important air traffic disruption in Indonesia. We investigate those ash emissions from space by applying the RSTASH algorithm for the first time to Himawari-8 data and using an ad hoc implementation scheme to reduce the time of the elaboration processes. Himawari-8 is a new generation Japanese geostationary meteorological satellite, whose AHI (Advanced Himawari Imager) sensor offers improved features, in terms of spectral, spatial and temporal resolution, in comparison with the previous imagers of the MTSAT (Multi-Functional Transport Satellite) series. Those features should guarantee further improvements in monitoring rapidly evolving weather/environmental phenomena. Results of this work show that RSTASH was capable of successfully detecting and tracking the Agung ash plume, despite some limitations (e.g., underestimation of ash coverage under certain conditions; generation of residual artefacts). Moreover, estimates of ash cloud-top height indicate that the monitored plume extended up to an altitude of about 9.3 km above sea level during the period 25 November at 21:10 UTC-26 November at 00:50 UTC. The study demonstrates that RSTASH may give a useful contribution for the operational monitoring of ash clouds over East Asia and the Western Pacific region, well exploiting the 10 min temporal resolution and the spectral features of the Himawari-8 data

A self-sufficient approach for GERB cloudy radiance detection.

Geostationary Earth Radiation Budget (GERB) is the broadband radiometer onboard the Meteosat Second Generation (MSG) platform, launched at the end of August 2002 and still in commissioning phase. GERB data is planned to be used in many applications concerning Earth Radiation Budget (ERB) calculation. In order to evaluate the impact of clouds on ERB, a cloud detection is required and, at present, a cloud mask based on higher spatial and spectral resolution data acquired by Spinning Enhanced Visible and Infrared Imager (SEVIRI), the imager onboard the same MSG platform, is planned to be used in order to identify cloudy GERB soundings. As an alternative, a self-sufficient (only based on GERB data) method (OCA, the One-channel Cloudy-radiance-detection Approach) is proposed, as a time-saving and, probably, more suitable solution than the planned co-location approach. In this paper, preliminary results obtained by using several years of Meteosat data as well as GERB synthetic radiances (produced from Meteosat-7 observations) are presented. It is shown how results obtained by using GERB data alone can be comparable (and better in terms of number and spatial distribution of clear-sky GERB soundings identified) to the ones achieved if the co-location of a higher resolution cloud mask is use

Advanced Satellite Technique for Volcanic Activity Monitoring and Early Warning.

Nowadays, satellite remote sensing is an important tool for volcanic activity monitoring, thanks to several operational satellite platforms providing data everywhere with high observational frequencies and generally at low cost. Among different techniques available, an advanced satellite method, named RST (Robust Satellite Technique). based on the multitemporal analysis of satellite data, has shown a high capability in volcanic activity monitoring. This approach has proved capable of identifyimg and tracking volcanic ash Cloud and of correctly detecting and monitoring volcanic thermal anomalies. This paper analyzes some recent results, obtained applying this approach to the last eruptive events of Mt. Etna using both polar and geostationary satellites. In particular, for the first time, this approach is implemented on the present geostationary platform MSG-SEVIRI, with 15 min of temporal resolution. Preliminary results, together with a future potential of this implementation, are shown and discussed. Moreover, a differential RST index in time domain is also proposed for near real-time application, as a possible contribution to the development of an efficient early warning satellite system for volcanic hazard mitigation

AVHRR Automated detection of volcanic clouds.

A new satellite‐based technique has recently been proposed which seems suitable for an automatic detection of volcanic clouds in daytime conditions. In this paper the robustness of such a new approach, in particular in detecting early eruptive clouds, is evaluated, on several eruptive events at Mt Etna, by using five years of Advanced Very High Resolution Radiometer (AVHRR) data. The detection scheme is discussed together with its possible extension to night‐time monitoring and the improvements expected by its application to the next generation of satellite sensors (in particular Spinning Enhanced Visible and Infrared Imager (SEVIRI)) with enhanced spectral and temporal resolution. The proposed approach seems to overcome the limitations related to other proposed methods which, in some conditions (very fresh eruptive clouds, cold‐backgrounds, etc.), give false or missed detection and will no longer be applicable to the next generation of Geostationary Operational Environmental Satellites (GOES) due to the planned reduction of their thermal infrared channels until 2010

A Multi-Sensor Exportable Approach for Automatic Flooded Areas Detection and Monitoring by a Composite Satellite Constellation

Timely and frequently updated information about flood-affected areas and their space-time evolution are often crucial in order to correctly manage the emergency phases. In such a context, optical data provided by meteorological satellites, offering the highest available temporal resolution (from hours to minutes), could have a great potential. As cloud cover often occurs reducing the number of usable optical satellite images, an appropriate integration of observations coming from different satellite systems will surely improve the probability to find cloud-free images over the investigated region. To make this integration effective, appropriate satellite data analysis methodologies, suitable for providing congruent results, regardless of the used sensor, are envisaged. In this paper, a sensor-independent approach (RST, Robust Satellites Techniques-FLOOD) is presented and applied to data acquired by two different satellite systems (Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration platforms and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Earth Observing System satellites) at different spatial resolutions (from 1 km to 250 m) in the case of Elbe flood event occurred in Germany on August 2002. Results achieved demonstrated as the full integration of AVHRR and MODIS RST-FLOOD products allowed us to double the number of satellite passes daily available, improving continuity of monitoring over flood-affected regions. In addition, the application of RST-FLOOD to higher spatial resolution MODIS (250 m) data revealed to be crucial not only for mapping purposes but also for improving RST-FLOOD capability in identifying flooded areas not previously detected at lower spatial resolution