State Key Laboratory of Fire Science, MEM Key Laboratory of Forest Fire Monitoring and Warning, School of Earth and Space Sciences, Comparative Planetary Excellence Innovation Center, University of Science and Technology of China, Hefei 230026, China; Institut de recherche sur les forêts, Université du Québec en Abitibi-Témiscamingue (UQAT), Rouyn-Noranda J9X 5E4, Canada.
State Key Laboratory of Fire Science, MEM Key Laboratory of Forest Fire Monitoring and Warning, School of Earth and Space Sciences, Comparative Planetary Excellence Innovation Center, University of Science and Technology of China, Hefei 230026, China; Institut de recherche sur les forêts, Université du Québec en Abitibi-Témiscamingue (UQAT), Rouyn-Noranda J9X 5E4, Canada.
Environ Int. 2022 Nov;169:107498. doi: 10.1016/j.envint.2022.107498. Epub 2022 Sep 5.
NO fire emissions greatly affect atmosphere and human society. The top-down NO fire emission estimation is highly influenced by satellite fire observation performance (e.g., fire detection) by affecting the derivation of emission coefficient (EC) and fire radiative power (FRP) magnitude. However, such influence is lack of comprehensive study. Here, we developed an algorithm to evaluate such impacts in northeastern Asia using multi-source data during 2012-2019. Specifically, we extracted near-concurrent fire observations from MODIS and its successor VIIRS over their orbit-overlapping area and combined respectively with OMI NO concentration to derive NO EC. We compared EC between MODIS and VIIRS, and defined a synergetic effect index (SEI) to explore the combined effects on NO fire emission estimation due to potentially different ECs and FRP between the two sensors. Finally, we applied EC to estimate NO emission and made comparison between MODIS and VIIRS. Results show that: 1) both sensors derived considerably higher NO EC for low-biomass vegetation fires (e.g., grassland fires) than other vegetation fires; however, MODIS EC is about 30% lower than VIIRS EC while similar values are derived for forest fires; 2) synergetic effects induced by different ECs and FRP magnitudes between the two sensors are more significant during fall and winter than in spring and summer; 3) annual NO emissions based on MODIS EC are 15-23% lower than that from VIIRS EC during 2012-2019, while both are lower than the conventional bottom-up emission inventories GFED and FINN by an average of 23-44%; nevertheless, the EC-based NO estimations presented high spatiotemporal correlation of R usually between 0.70 and 0.95 with GFED and FINN. These results reveal and quantify the critical impacts of satellite fire observation performance on EC derivation and fire emission estimation, which is helpful in reducing estimation uncertainty.
大火排放物对大气和人类社会有重大影响。自上而下的火灾排放量估算受到卫星火灾观测性能（例如火灾探测）的高度影响，因为它会影响排放系数（EC）和火灾辐射功率（FRP）幅度的推导。然而，这种影响缺乏全面的研究。在这里，我们使用 2012-2019 年的多源数据，在东北亚开发了一种评估这种影响的算法。具体来说，我们从 MODIS 及其后续 VIIRS 的轨道重叠区域提取了近乎同时的火灾观测，并分别与 OMI 的 NO 浓度相结合，以推导出 NO EC。我们比较了 MODIS 和 VIIRS 的 EC，并定义了协同效应指数（SEI），以探索由于两个传感器之间潜在不同的 EC 和 FRP 对 NO 火灾排放估算的综合影响。最后，我们应用 EC 来估算 NO 排放，并对 MODIS 和 VIIRS 进行了比较。结果表明：1）对于低生物质植被火灾（例如草地火灾），两个传感器都推导出了相当高的 NO EC，而对于其他植被火灾，则较低；然而，MODIS 的 EC 比 VIIRS 的 EC 低约 30%，而森林火灾则推导出类似的值；2）两个传感器之间不同的 EC 和 FRP 幅度引起的协同效应在秋季和冬季比春季和夏季更为显著；3）2012-2019 年，基于 MODIS 的 EC 的年 NO 排放量比基于 VIIRS 的 EC 低 15-23%，而与 GFED 和 FINN 相比，两者都低 23-44%；然而，基于 EC 的 NO 估算具有高时空相关性，通常与 GFED 和 FINN 的 R 值在 0.70 到 0.95 之间。这些结果揭示并量化了卫星火灾观测性能对 EC 推导和火灾排放估算的关键影响，有助于降低估算的不确定性。
