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).

Characterising the dynamics of landscape-scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and Northern and Southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP data set, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 % and 65–77 % respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35 % over the Northern Africa region to 89 % over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near-real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 "mega-fire" event focused on Peloponnese (Greece) and used within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring Service (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from a geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions data sets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET (Aerosol Robotic Network) AOD indicates that the former is overestimated by ~ 20–30 %, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those implemented in the Monitoring Atmospheric Composition and Climate (MACC) series of projects for the CAMS.

Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Thermal Anomaly (FTA) detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) across the Central African Republic (CAR). Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI’s omission rate can be attributed to a coupling between SEVIRI’s low spatial resolution detection bias (i.e., the inability to detect fires below a certain size and intensity) and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI’s commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i) future improvements to the SEVIRI FTA detection algorithm; (ii) the assimilation of the SEVIRI and MODIS active fire products; and (iii) the potential inclusion of SEVIRI into a network of geostationary sensors designed to achieve global diurnal active fire monitoring.

Fire radiative power (FRP) retrievals are now routinely made from polar and geostationary instruments, providing a means to estimate fuel consumption and trace gas and aerosol emissions directly from remotely sensed observations. This study presents the first investigation of the impact of vegetation canopy structure (percentage canopy cover and leaf area index, LAI) on FRP retrievals, based on 3D radiative transfer model simulations. The Discrete Anisotropic Radiative Transfer (DART) model is used to simulate above-canopy observations made through 3D vegetation canopies with different structural arrangements, under which a centrally positioned uniform landscape fire is burning. The vegetation canopy is modelled in two ways, as an opaque structure and as a hybrid turbid medium. The percentage canopy cover in each simulated scene is varied between 5 and 95%, and the FRP retrieved above the canopy is found to decrease in proportion to percentage canopy cover when the canopy is opaque, a finding that is in agreement with a series of small scale outdoor measurements conducted to evaluate the realism of the simulations. However, when the canopy is modelled as a turbid medium, which is in some ways a more realistic representation of a real ‘gappy’ vegetation canopy, the degree of FRP interception occurring at any particular canopy cover decreases by ~ 14%, due to some fire emitted thermal energy being transmitted through the canopy gaps. The simulations also reveal the impact of canopy LAI on above-canopy FRP retrievals, reducing these by 6% when both canopy cover and LAI are low (5% and < 1.0 respectively), but by up to 92% when canopy cover and scene LAI are high (95% and ~8 respectively). We use the derived relationships between FRP interception and canopy structure, along with MODIS LAI and percentage tree cover data, to adjust 2004–2012 fire radiative energy (FRE) estimates calculated from FRP data collected by the geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument. The adjusted annual FRE is on average 15% greater than estimated, and is characterized by low inter-annual variability as result of the majority of fire activity occurring in areas where percentage tree cover remains below 40%. Landscape burning occurs more frequently in areas of higher tree cover in southern hemisphere rather than northern hemisphere Africa, leading to a larger annual FRE adjustment (18.5% compared to 16.3%). This study illustrates the impact that canopy interception has on FRP for the first time at the satellite scale, and over Africa demonstrates a large but temporally consistent underestimation which can be accounted for using LAI and percentage tree cover metrics when estimating fuel consumption and atmospheric emissions from the FRP retrievals.

Urban land cover is one of the fastest global growing land cover types which permanently alters land surface properties and atmospheric interactions, often initiating an urban heat island effect. Urbanisation comprises a number of land cover changes within metropolitan regions. However, these complexities have been somewhat neglected in temperature analysis studies of the urban heat island effect, whereby over-simplification ignores the heterogeneity of urban surfaces and associated land surface temperature dynamics. Accurate spatial information pertaining to these land cover change—temperature relationships across space is essential for policy integration regarding future sustainable city planning to mitigate urban heat impacts. Through a multi-sensor approach, this research disentangles the complex spatial heterogeneous variations between changes in land cover (Landsat data) and land surface temperature (MODIS data), to understand the urban heat island effect dynamics in greater detail for appropriate policy integration. The application area is the rapidly expanding Perth Metropolitan Region (PMR) in Western Australia (WA). Results indicate that land cover change from forest to urban is associated with the greatest annual daytime and nighttime temperature change of 0.40 °C and 0.88 °C respectively. Conversely, change from grassland to urban minimises temperature change at 0.16 °C and 0.77 °C for annual daytime and nighttime temperature respectively. These findings are important to consider for proposed developments of the city as such detail is not currently considered in the urban growth plans for the PMR. The novel intra-urban research approach presented can be applied to other global metropolitan regions to facilitate future transition towards sustainable cities, whereby urban heat impacts can be better managed through optimised land use planning, moving cities towards alignment with the 2030 sustainable development goals and the City Resilience Framework (CRF).

Earth observation data can provide valuable assessments for monitoring the spatial extent of (un)sustainable urban growth of the world’s cities to better inform planning policy in reducing associated economic, social and environmental costs. Western Australia has witnessed rapid economic expansion since the turn of the century founded upon extensive natural resource extraction. Thus, Perth, the state capital of Western Australia, has encountered significant population and urban growth in response to the booming state economy. However, the recent economic slowdown resulted in the largest decrease in natural resource values that Western Australia has ever experienced. Here, we present multi-temporal urban expansion statistics from 1990 to 2015 for Perth, derived from Landsat imagery. Current urban estimates used for future development plans and progress monitoring of infill and density targets are based upon aggregated census data and metrics unrepresentative of actual land cover change, underestimating overall urban area. Earth observation provides a temporally consistent methodology, identifying areal urban area at higher spatial and temporal resolution than current estimates. Our results indicate that the spatial extent of the Perth Metropolitan Region has increased 45% between 1990 and 2015, over 320 km². We highlight the applicability of earth observation data in accurately quantifying urban area for sustainable targeted planning practices.