Meteosat SEVIRI Fire Radiative Power (FRP) products from the Land Surface Analysis Satellite Applications Facility (LSA SAF) – Part 1: Algorithms, product contents and analysis

Abstract. Characterising changes in landscape scale fire activity at very high temporal resolution is best achieved using thermal observations of actively burning fires made from geostationary Earth observation (EO) satellites. Over the last decade or more, a series of research and/or operational "active fire" products have been developed from these types of geostationary observations, often with the aim of supporting the generation of data related to biomass burning fuel consumption and trace gas and aerosol emission fields. The Fire Radiative Power (FRP) products generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from data collected by the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) are one such set of products, and are freely available in both near real-time and archived form. Every 15 min, the algorithms used to generate these products identify and map the location of new SEVIRI observations containing actively burning fires, and characterise their individual rates of radiative energy release (fire radiative power; FRP) that is believed proportional to rates of biomass consumption and smoke emission. The FRP-PIXEL product contains the highest spatial resolution FRP dataset, delivered for all of Europe, northern and southern Africa, and part of South America at a spatial resolution of 3 km (decreasing away from the west African sub-satellite point) at the full 15 min temporal resolution. The FRP-GRID product is an hourly summary of the FRP-PIXEL data, produced at a 5° grid cell size and including simple bias adjustments for meteorological cloud cover and for the regional underestimation of FRP caused, primarily, by the non-detection of low FRP fire pixels at SEVIRI's relatively coarse pixel size. Here we describe the enhanced geostationary Fire Thermal Anomaly (FTA) algorithm used to detect the SEVIRI active fire pixels, and detail methods used to deliver atmospherically corrected FRP information together with the per-pixel uncertainty metrics. Using scene simulations and analysis of real SEVIRI data, including from a period of Meteosat-8 "special operations", we describe some of the sensor and data pre-processing characteristics influencing fire detection and FRP uncertainty. We show that the FTA algorithm is able to discriminate actively burning fires covering down to 10−4 of a pixel, and is more sensitive to fire than algorithms used within many other widely exploited active fire products. We also find that artefacts arising from the digital filtering and geometric resampling strategies used to generate level 1.5 SEVIRI data can significantly increase FRP uncertainties in the SEVIRI active fire products, and recommend that the processing chains used for the forthcoming Meteosat Third Generation attempt to minimise the impact of these types of operations. Finally, we illustrate the information contained within the current Meteosat FRP-PIXEL and FRP-GRID products, providing example analyses for both individual fires and multi-year regional-scale fire activity. A companion paper (Roberts et al., 2015) provides a full product performance evaluation for both products, along with examples of their use for prescribing fire smoke emissions within atmospheric modelling components of the Copernicus Atmosphere Monitoring Service (CAMS).

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LSA SAF Meteosat FRP products – Part 1: Algorithms, product contents, and analysis

Atmospheric Chemistry and Physics ◽

10.5194/acp-15-13217-2015 ◽

2015 ◽

Vol 15 (22) ◽

pp. 13217-13239 ◽

Cited By ~ 35

Author(s):

M. J. Wooster ◽

G. Roberts ◽

P. H. Freeborn ◽

W. Xu ◽

Y. Govaerts ◽

...

Keyword(s):

Temporal Resolution ◽

Land Surface ◽

Regional Scale ◽

Detection Algorithm ◽

Radiative Energy ◽

Data Set ◽

Measurement Capability ◽

Active Fire ◽

Aerosol Emission ◽

Fire Activity

Abstract. Characterizing changes in landscape fire activity at better than hourly temporal resolution is achievable using thermal observations of actively burning fires made from geostationary Earth Observation (EO) satellites. Over the last decade or more, a series of research and/or operational "active fire" products have been developed from geostationary EO data, often with the aim of supporting biomass burning fuel consumption and trace gas and aerosol emission calculations. Such Fire Radiative Power (FRP) products are generated operationally from Meteosat by the Land Surface Analysis Satellite Applications Facility (LSA SAF) and are available freely every 15 min in both near-real-time and archived form. These products map the location of actively burning fires and characterize their rates of thermal radiative energy release (FRP), which is believed proportional to rates of biomass consumption and smoke emission. The FRP-PIXEL product contains the full spatio-temporal resolution FRP data set derivable from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) imager onboard Meteosat at a 3 km spatial sampling distance (decreasing away from the west African sub-satellite point), whilst the FRP-GRID product is an hourly summary at 5° grid resolution that includes simple bias adjustments for meteorological cloud cover and regional underestimation of FRP caused primarily by underdetection of low FRP fires. Here we describe the enhanced geostationary Fire Thermal Anomaly (FTA) detection algorithm used to deliver these products and detail the methods used to generate the atmospherically corrected FRP and per-pixel uncertainty metrics. Using SEVIRI scene simulations and real SEVIRI data, including from a period of Meteosat-8 "special operations", we describe certain sensor and data pre-processing characteristics that influence SEVIRI's active fire detection and FRP measurement capability, and use these to specify parameters in the FTA algorithm and to make recommendations for the forthcoming Meteosat Third Generation operations in relation to active fire measures. We show that the current SEVIRI FTA algorithm is able to discriminate actively burning fires covering down to 10−4 of a pixel and that it appears more sensitive to fire than other algorithms used to generate many widely exploited active fire products. Finally, we briefly illustrate the information contained within the current Meteosat FRP-PIXEL and FRP-GRID products, providing example analyses for both individual fires and multi-year regional-scale fire activity; the companion paper (Roberts et al., 2015) provides a full product performance evaluation and a demonstration of product use within components of the Copernicus Atmosphere Monitoring Service (CAMS).

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Defining a Trade-off Between Spatial and Temporal Resolution of a Geosynchronous SAR Mission for Soil Moisture Monitoring

Remote Sensing ◽

10.3390/rs10121950 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1950 ◽

Cited By ~ 3

Author(s):

Luca Cenci ◽

Luca Pulvirenti ◽

Giorgio Boni ◽

Nazzareno Pierdicca

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Temporal Resolution ◽

Added Value ◽

Sar Images ◽

Radar Images ◽

Spatio Temporal ◽

Set Up ◽

The Impact ◽

Hydrological Applications

The next generation of synthetic aperture radar (SAR) systems could foresee satellite missions based on a geosynchronous orbit (GEO SAR). These systems are able to provide radar images with an unprecedented combination of spatial (≤1 km) and temporal (≤12 h) resolutions. This paper investigates the GEO SAR potentialities for soil moisture (SM) mapping finalized to hydrological applications, and defines the best compromise, in terms of image spatio-temporal resolution, for SM monitoring. A synthetic soil moisture–data assimilation (SM-DA) experiment was thus set up to evaluate the impact of the hydrological assimilation of different GEO SAR-like SM products, characterized by diverse spatio-temporal resolutions. The experiment was also designed to understand if GEO SAR-like SM maps could provide an added value with respect to SM products retrieved from SAR images acquired from satellites flying on a quasi-polar orbit, like Sentinel-1 (POLAR SAR). Findings showed that GEO SAR systems provide a valuable contribution for hydrological applications, especially if the possibility to generate many sub-daily observations is sacrificed in favor of higher spatial resolution. In the experiment, it was found that the assimilation of two GEO SAR-like observations a day, with a spatial resolution of 100 m, maximized the performances of the hydrological predictions, for both streamflow and SM state forecasts. Such improvements of the model performances were found to be 45% higher than the ones obtained by assimilating POLAR SAR-like SM maps.

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Tropical Andes Radar Precipitation Estimates Need High Temporal and Moderate Spatial Resolution

Water ◽

10.3390/w11051038 ◽

2019 ◽

Vol 11 (5) ◽

pp. 1038 ◽

Cited By ~ 1

Author(s):

Mario Guallpa ◽

Johanna Orellana-Alvear ◽

Jörg Bendix

Keyword(s):

Spatial Resolution ◽

Temporal Resolution ◽

Time Resolution ◽

Weather Radar ◽

Radar Data ◽

Rain Gauge ◽

Tropical Andes ◽

The Andes ◽

Significant Difference ◽

The Impact

Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Different time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with different features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant difference was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the Andes Mountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.

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An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

Advances in Geosciences ◽

10.5194/adgeo-44-89-2017 ◽

2017 ◽

Vol 44 ◽

pp. 89-100 ◽

Cited By ~ 8

Author(s):

Luca Cenci ◽

Luca Pulvirenti ◽

Giorgio Boni ◽

Marco Chini ◽

Patrick Matgen ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Spatial Resolution ◽

Temporal Resolution ◽

Hydrological Model ◽

High Spatial Resolution ◽

Flash Flood ◽

Soil Moisture Data ◽

Time Period ◽

The Impact

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

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Severe Fire Danger Index: A Forecastable Metric to Inform Firefighter and Community Wildfire Risk Management

Fire ◽

10.3390/fire2030047 ◽

2019 ◽

Vol 2 (3) ◽

pp. 47 ◽

Cited By ~ 4

Author(s):

W. Matt Jolly ◽

Patrick H. Freeborn ◽

Wesley G. Page ◽

Bret W. Butler

Keyword(s):

United States ◽

Early Warning ◽

Wildland Fire ◽

Fire Danger ◽

Numerical Weather ◽

Area Burned ◽

Active Fire ◽

Radiative Power ◽

Very High ◽

Fire Radiative Power

Despite major advances in numerical weather prediction, few resources exist to forecast wildland fire danger conditions to support operational fire management decisions and community early-warning systems. Here we present the development and evaluation of a spatial fire danger index that can be used to assess historical events, forecast extreme fire danger, and communicate those conditions to both firefighters and the public. It uses two United States National Fire Danger Rating System indices that are related to fire intensity and spread potential. These indices are normalized, combined, and categorized based on a 39-yr climatology (1979–2017) to produce a single, categorical metric called the Severe Fire Danger Index (SFDI) that has five classes; Low, Moderate, High, Very High, and Severe. We evaluate the SFDI against the number of newly reported wildfires and total area burned from agency fire reports (1992–2017) as well as daily remotely sensed numbers of active fire pixels and total daily fire radiative power for large fires (2003–2016) from the Moderate-Resolution Imaging Spectroradiometer (MODIS) across the conterminous United States. We show that the SFDI adequately captures geographic and seasonal variations of fire activity and intensity, where 58% of the eventual area burned reported by agency fire records, 75.2% of all MODIS active large fire pixels, and 81.2% of all fire radiative power occurred when the SFDI was either Very High or Severe (above the 90th percentile). We further show that SFDI is a strong predictor of firefighter fatalities, where 97 of 129 (75.2%) burnover deaths from 1979 to 2017 occurred when SFDI was either Very High or Severe. Finally, we present an operational system that uses short-term, numerical weather predictions to produce daily SFDI forecasts and show that 76.2% of all satellite active fire detections during the first 48 h following the ignition of nine high-profile case study fires in 2017 and 2018 occurred under Very High or Severe SFDI conditions. The case studies indicate that the extreme weather events that caused tremendous damage and loss of life could be mapped ahead of time, which would allow both wildland fire managers and vulnerable communities additional time to prepare for potentially dangerous conditions. Ultimately, this simple metric can provide critical decision support information to wildland firefighters and fire-prone communities and could form the basis of an early-warning system that can improve situational awareness and potentially save lives.

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Advances in the estimation of high Spatio-temporal resolution pan-African top-down biomass burning emissions made using geostationary fire radiative power (FRP) and MAIAC aerosol optical depth (AOD) data

Remote Sensing of Environment ◽

10.1016/j.rse.2020.111971 ◽

2020 ◽

Vol 248 ◽

pp. 111971 ◽

Cited By ~ 2

Author(s):

Hannah M. Nguyen ◽

Martin J. Wooster

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Temporal Resolution ◽

Biomass Burning ◽

Top Down ◽

Pan African ◽

Radiative Power ◽

Spatio Temporal ◽

Fire Radiative Power

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Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire ◽

10.3390/fire4040065 ◽

2021 ◽

Vol 4 (4) ◽

pp. 65

Author(s):

Gernot Ruecker ◽

David Leimbach ◽

Joachim Tiemann

Keyword(s):

Remote Sensing ◽

Fuel Consumption ◽

Spatial Resolution ◽

Fire Behavior ◽

Remote Sensing Data ◽

Fire Intensity ◽

Rate Of Spread ◽

Radiative Power ◽

Sentinel 2 ◽

Fire Radiative Power

Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

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Vertical distribution of aerosols over the Maritime Continent during El Nino

10.5194/acp-2016-1179 ◽

2017 ◽

Author(s):

Jason Blake Cohen ◽

Daniel Hui Loong Ng ◽

Alan Wei Lun Lim ◽

Xin Rong Chua

Keyword(s):

Vertical Distribution ◽

El Niño ◽

Radiative Forcing ◽

El Nino ◽

Small Scale ◽

Fire Plume ◽

Radiative Power ◽

Fire Properties ◽

The Impact ◽

Fire Radiative Power

Abstract. The vertical distribution of aerosols over Southeast Asia, a critical factor of aerosol lifetime, and impact on radiative forcing and precipitation, is examined for the 2006 post El-Nino fire burning season. Additionally, through analysis of measurements and modeling, we have reconfirmed the hypothesis that fire radiative power is underestimated. Our results are significantly different from what others are using. The horizontally constrained Maritime Continent’s fire plume median height, using the maximum variance of satellite observed Aerosol Optical Depth as the spatial and temporal constraint, is found to be 2.17 &pm; 1.53 km during the 2006 El Nino season. This is 0.96 km higher than random sampling and all other past studies, with 62 % of particles in the free troposphere. The impact is that the aerosol lifetime will be significantly longer, and that the aerosols will disperse in a direction different from if they were in the boundary layer. Application of a simple plume rise model using measurements of fire properties underestimates the median plume height by 0.34 km and more in the bottom-half of the plume. The center of the plume can be reproduced when fire radiative power is increased by 20 % (range from 0 % to 100 %). However, to reduce the biases found, improvements are required in terms of measurements of fire properties when cloud covered, representation of small scale convection, and inclusion of aerosol direct and semi-direct effects. The results provide the unique aerosol signature of fire under El-Nino conditions.

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Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa

Remote Sensing ◽

10.3390/rs10101591 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1591 ◽

Cited By ~ 5

Author(s):

Gareth Roberts ◽

Martin Wooster ◽

Weidong Xu ◽

Jiangping He

Keyword(s):

Fuel Consumption ◽

Southern Hemisphere ◽

Land Surface ◽

Sub Saharan Africa ◽

Fuel Load ◽

Biomass Combustion ◽

Fire Emissions ◽

Fire Activity ◽

Sub Saharan ◽

The Impact

African landscape fires are widespread, recurrent and temporally dynamic. They burn large areas of the continent, modifying land surface properties and significantly affect the atmosphere. Satellite Earth Observation (EO) data play a pivotal role in capturing the spatial and temporal variability of African biomass burning, and provide the key data required to develop fire emissions inventories. Active fire observations of fire radiative power (FRP, MW) have been shown to be linearly related to rates of biomass combustion (kg s−1). The Meteosat FRP-PIXEL product, delivered in near real-time by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF), maps FRP at 3 km resolution and 15-min intervals and these data extend back to 2004. Here we use this information to assess spatio-temporal variations in fire activity across sub-Saharan Africa, and identify an overall trend of decreasing annual fire activity and fuel consumption, agreeing with the widely-used Global Fire Emissions Database (GFEDv4) based on burned area measures. We provide the first comprehensive assessment of relationships between per-fire FRE-derived fuel consumption (Tg dry matter, DM) and temporally integrated Moderate Resolution Imaging Spectroradiometer (MODIS) net photosynthesis (PSN) (Tg, which can be converted into pre-fire fuel load estimates). We find very strong linear relationships over southern hemisphere Africa (mean r = 0.96) that are partly biome dependent, though the FRE-derived fuel consumptions are far lower than those derived from the accumulated PSN, with mean fuel consumptions per unit area calculated as 0.14 kg DM m−2. In the northern hemisphere, FRE-derived fuel consumption is also far lower and characterized by a weaker linear relationship (mean r = 0.76). Differences in the parameterization of the biome look up table (BLUT) used by the MOD17 product over Northern Africa may be responsible but further research is required to reconcile these differences. The strong relationship between fire FRE and pre-fire fuel load in southern hemisphere Africa is encouraging and highlights the value of geostationary FRP retrievals in providing a metric that relates very well to fuel consumption and fire emission variations. The fact that the estimated fuel consumed is only a small fraction of the fuel available suggests underestimation of FRE by Spinning Enhanced Visible and Infrared Imager (SEVIRI) and/or that the FRE-to-fuel consumption conversion factor of 0.37 MJ kg−1 needs to be adjusted for application to SEVIRI. Future geostationary imaging sensors, such as on the forthcoming Meteosat Third Generation (MTG), will reduce the impact of this underestimation through its ability to detect even smaller and shorter-lived fires than can the current second generation Meteosat.

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