Abstract
Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location’s recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area’s local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.
Highlights
Active fire in the landscape is a major catalyst for environmental change, potentially resulting in large socio-economic impacts, including the high costs and risks associated with mitigation efforts and the disruptive evacuation of communities [1]
From a fitting availability standpoint, the Broad Area Training (BAT) method significantly increases the distribution of areas that are able to have a temperature fitting applied to them
This study demonstrates the formulation of a broad-area training data derivation method for temperature fitting, for estimation of the background temperature of a pixel measured by a geostationary sensor whilst obscured by cloud, smoke or fire
Summary
Active fire in the landscape is a major catalyst for environmental change, potentially resulting in large socio-economic impacts, including the high costs and risks associated with mitigation efforts and the disruptive evacuation of communities [1]. Fire authorities and land managers are constantly seeking new techniques for the early detection of fire to assist in the timely informing and evacuation of the public from at-risk areas, the planning and prioritisation of asset management strategies, and feasibility assessment of possible suppression efforts. This requirement for active fire detection in near real-time has seen the adoption of remote sensing from satellite sensors as an objective means to quantify and characterise the location, spread and intensity of fire events to support these important decisions [2]. The launch of new sensors such as the Japanese Meteorological Agency’s
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