Land
Hama Hamidou explains what dust storms are and their impacts, then discusses dust monitoring and forecasting.
Dust storms are one of the major high-impact weather phenomena that affect tropical arid and semi-arid areas, with important socio-economic impacts. Even though dust storms are generated in these regions, they can affect areas far beyond their source regions, depending on the weather systems present. In this presentation, the socio-economic impacts of dust storms, conceptual models for dust forecasting, and methods used for dust monitoring are presented.
Matshidiso Mogale discusses how weather conditions influence wildfires in Southern Africa and how satellite observations can help detect active fires.
This presentation explores wildfire risk across Southern Africa, with a focus on the Western and Eastern Cape of South Africa, highlighting the seasonal and meteorological conditions that favour wildfire development and spread. It demonstrates how satellite products can be used for wildfire detection and monitoring, while highlighting the impacts of wildfires on communities, infrastructure and the environment.
A collection of cases and colour interpretations derived from MTG proxy data from satellites such as GOES and Himawari.
Author(s): Marko Blaskovic
How to see and analyse fires using Cloud Phase RGB? What are the key channels in the RGB that help us do that?
Enter here.
Boštjan Muri introduces LSA SAF vegetation and fire products through different showcases and presents a Jupyter notebook exercise on accessing and understanding the Fire Radiating Power (FRP) data.
The operational use of LSA SAF vegetation and Fire Radiative Power (FRP) satellite products derived from Meteosat observations is presented. Vegetation products are processed and analysed using Jupyter Notebooks, enabling reproducible workflows for data handling, visualisation and analysis. The examples demonstrate how processing of vegetation products supports vegetation monitoring and analysis for agrometeorology, forestry and environmental applications, linking satellite observations with operational services.
For wildfire monitoring, a Jupyter Notebook–based tutorial is presented, analysing fire activity using LSA SAF FRP products from the MSG/SEVIRI and MTG/FCI sensors. The tutorial demonstrates data access, preprocessing and statistical analysis of high-confidence FRP observations, including full-disc overviews, temporal aggregation, spatial mapping and inter-sensor comparison. Selected case studies illustrate detailed fire analysis and differences between the sensors. The provided Jupyter Notebook materials are suitable for both basic and more advanced users. You can find the Jupyter Notebook files in the Jupyter Notebook instructions.
Pedro Venâncio introduces the Portuguese Rural Fire Information System (SIFOR) and demonstrates how the GeoSIFOR WebGIS enables users to easily access, visualise and interpret available data.
SIFOR is an integrated application ecosystem designed to centralize and share technical information across Portugal’s Integrated Rural Fire Management System (SGIFR). Within this ecosystem, GeoSIFOR acts as a user-friendly Geographic Information Viewer (WebGIS) that allows users without specialized knowledge in GIS or remote sensing to easily leverage geographic and satellite data. Through its agile interface, non-expert users can access complex information, for instance, from the Copernicus Data Space Ecosystem (CDSE), Eumetsat, LSA-SAF, NASA, as well as satellite-derived products processed by other public entities such as DGT and IPMA.
Francesca Di Giuseppe discusses ECMWF fire prediction systems and the advantages of SPARKY, a new data-driven system for predicting fire activity and burned area.
Traditional fire danger indices estimate environmental conditions conducive to fire but do not directly predict when and where fires will occur. This presentation introduces a shift toward fire activity forecasting, using data-driven approaches that integrate weather, fuel conditions, and ignition sources. By leveraging machine learning and Earth observation data, these systems provide probabilistic forecasts of actual fire occurrence and spread. The approach improves early warning capabilities, reduces false alarms in fuel-limited regions, and offers actionable information for fire management, supporting more effective preparedness, response, and long-term risk mitigation strategies.
Ivan Smiljanić presents the evolution of RGB composites used for fire detection, with a final focus on the MTG Day Land Cloud Fire RGB composite and its advantages.
The Day Land Cloud/Fire RGB (full name) is a composite designed to enhance the detection of surface features and clouds, in addition to wildfires. Compared to other fire-focused composites, it provides a more balanced depiction of smoke, vegetation, and burn scars in a single product, though with reduced ability to discriminate fire intensity or even cloud phase. It renders active fires more vividly in red while preserving a near-natural background appearance. It is particularly effective for integrated situational awareness rather than specialized analysis.
Bruno Café introduces satellite products used by operational forecasters for fire detection and monitoring, using the 2025 Portugal fire event as a case study.
This presentation explores the enhanced capabilities of the Flexible Combined Imager (FCI) sensor on board Meteosat-12 (MTG) for forest fire detection and monitoring. By leveraging better spectral, temporal, and spatial resolutions, we demonstrate how new RGB composites, specifically Fire Temperature and True Color RGBs, provide critical advantages in identifying hotspots, smoke plumes, and burn scars.
Martin Wooster discusses the LSA SAF Fire Radiative Power (FRP) product derived from geostationary satellite imagery and the improvements in active fire detection offered by MTG compared with MSG.
The session will cover the Geostationary FRP products from the EUMETSAT LSA-SAF that provide a record of active fire detections, fire radiative power (FRP) and smoke and carbon emissions over Europe, Africa and parts of South America, most recently from MTG with significant performance improvements compared to MSG. The methods used, the available products, and their performance will be reviewed in this session.
Carlos da Camara addresses the application of remote sensing for the development of an early warning system for fire danger.
In recent years, Europe has been affected by severe wildfires that are driven by vegetation stress and fire weather, and have severe impacts at social, economic and ecological levels. To assist fire managers and firefighters, the LSA SAF has developed an early warning fire danger system for Europe. The system relies on a set of regional models that provide forecasts of local meteorological fire danger based on statistical models of fire radiative power that use the so-called Fire Weather Index as a covariate. The rationale will be described in detail, and several case studies will be presented.
Célia Gouveia talks about LSA SAF vegetation data used for fire assessments and monitoring.
Fire-prone conditions and the impact of wildfires on vegetation dynamics and productivity are analysed using the land surface temperature (LST) and evapotranspiration (ET and ETREF) and their impacts on vegetation and products such as Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction of Vegetation Cover (FVC) and Gross Primary Production (GPP) disseminated by LSA SAF.
This mini-module is part of a series of MTG mini modules produced by EUMETSAT/EUMeTrain with the aim of providing quick and useful learning experience about the new satellite products.
In this exercise you will learn about FCI ability to detect fires and how it compares to previous generation of satellite instruments.
Enter the mini-module.