Estimating Fire Background Temperature at a Geostationary Scale—An Evaluation of Contextual Methods for AHI-8

An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel’s background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this background value. The most commonly used method of background temperature estimation is through derivation from the surrounding obscuration-free pixels available in the same image, in a contextual estimation process. This method of contextual estimation performs well in cloud-free conditions and in areas with homogeneous landscape characteristics, but increasingly complex sets of rules are required when contextual coverage is not optimal. The effects of alterations to the search radius and sample size on the accuracy of contextually derived brightness temperature are heretofore unexplored. This study makes use of imagery from the AHI-8 geostationary satellite to examine contextual estimators for deriving background temperature, at a range of contextual window sizes and percentages of valid contextual information. Results show that while contextual estimation provides accurate temperatures for pixels with no contextual obscuration, significant deterioration of results occurs when even a small portion of the target pixel’s surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65% of a target pixel’s total contextual coverage is recommended. The study also examines the use of expanding window sizes and their effect on temperature estimation. Results show that the accuracy of temperature estimation decreases significantly when expanding the examined window, with a 50% increase in temperature variability when using a larger window size than 5×5 pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4%–4.4% of all pixels examined). The work also presents a number of case study regions taken from the AHI-8 disk in more depth, and examines the causes of excess temperature variation over a range of topographic and land cover conditions.

任何遥感火灾检测与归因方法的核心组成部分，均需对目标像元的背景温度进行估算。由于无法脱离火灾辐射直接测量该温度，因此必须采用间接方法来估算该背景值。目前最常用的背景温度估算方法，是在上下文估计流程中，从同一幅影像中可用的周围无遮挡像元推导得到。该上下文估计方法在无云条件与地表特征均质的区域中表现良好，但当上下文覆盖情况不佳时，则需要愈发复杂的规则集。此前尚无研究探索搜索半径与样本量的改变，对上下文推导亮温精度的影响。本研究利用AHI-8对地静止卫星的影像数据，在一系列上下文窗口尺寸与有效上下文信息占比的条件下，对用于推导背景温度的上下文估计器展开研究。研究结果表明，尽管上下文估计可为无上下文遮挡的像元提供准确温度，但即便目标像元周边仅有一小部分区域被遮挡，估算结果也会出现显著劣化。为维持温度估算精度，建议使用目标像元总上下文覆盖范围内至少65%的有效数据。本研究还探讨了扩大窗口尺寸对温度估算的影响。结果显示，当扩大检测窗口时，温度估算精度会显著下降：当使用大于5×5像元的窗口时，温度变异性提升50%，但总体上仅能小幅增加可获得温度估算的像元总数（仅为所有检测像元的0.4%~4.4%）。本研究还选取了AHI-8全圆盘视场内的多个案例研究区域进行深入分析，并探究了不同地形与土地覆盖条件下温度过度变异的成因。