The accurate estimation of biomass burning emissions has played a crucial role in air quality and climate forecast modeling. Satellite-based fire radiative power (FRP) has proven effective for calculating biomass burning emissions. However, FRP-based emission estimations in East Asia often rely on polar-orbiting satellites owing to the unstable performance of Japan Aerospace Exploration Agency Advanced Himawari Imager (JAXA AHI) from poor detection capability and unproper FRP retrieval method. To address this, we improve the FRP by machine learning based on mid-infrared (MIR) radiance method, leveraging the superior fire detection model developed in our previous study. In addition, we propose a multi-satellite distance-based weighted ensemble FRP estimation method. Compared to traditional MIR radiance methods, the machine learning-based FRP estimation model exhibited promising performance (correlation coefficient: 1, mean bias error: 0.2, mean absolute percentage error: 1.9%). The integration of machine learning-based FRP estimation and fire detection model dramatically mitigated the underestimation issues from the JAXA AHI. The machine learning-based FRP was combined with the Moderate Resolution Imaging Spectroradiometer FRP to create a multi-satellite ensemble FRP. Comparative assessments using the TROPOspheric Monitoring Instrument and conventional bottom-up method demonstrated that the proposed method produced reliable output. Furthermore, impact analysis revealed that missing peaks or underestimated burn scars could lead to fatally low emissions; however, the proposed method was relatively robust against missing data owing to its multi-satellite ensemble. By identifying potential FRP problems and their impact on emission estimations, this study provides valuable insights for FRP-based emission estimation studies.

中文翻译：

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减少先进 Himawari 成像仪火灾排放的低估：机器学习和多卫星集成方法

生物质燃烧排放的准确估算在空气质量和气候预测建模中发挥了至关重要的作用。事实证明，基于卫星的火辐射功率（FRP）对于计算生物质燃烧排放量非常有效。然而，由于日本宇宙航空研究开发机构先进向日葵成像仪（JAXA AHI）探测能力差和FRP反演方法不当，导致东亚地区基于FRP的排放估算往往依赖于极轨卫星。为了解决这个问题，我们通过基于中红外 (MIR) 辐射率方法的机器学习来改进 FRP，并利用我们之前研究中开发的高级火灾探测模型。此外，我们提出了一种基于多卫星距离的加权集合FRP估计方法。与传统的 MIR 辐射率方法相比，基于机器学习的 FRP 估计模型表现出良好的性能（相关系数：1，平均偏差误差：0.2，平均绝对百分比误差：1.9%）。基于机器学习的 FRP 估计和火灾探测模型的集成极大地缓解了 JAXA AHI 的低估问题。基于机器学习的 FRP 与中分辨率成像光谱仪 FRP 相结合，创建了多卫星集成 FRP。使用对流层监测仪器和传统自下而上方法的比较评估表明，所提出的方法产生了可靠的输出。此外，影响分析显示，缺失峰值或低估烧痕可能导致排放量过低；然而，由于其多卫星群，所提出的方法对于丢失数据相对稳健。通过识别潜在的 FRP 问题及其对排放估算的影响，本研究为基于 FRP 的排放估算研究提供了宝贵的见解。