Anthropogenic carbon emissions directly contribute to global warming, which has induced severe environmental concerns like extreme droughts and devastating fires. To evaluate the effects of fires on carbon cycling and climate change, it is crucial to accurately estimate the amount of carbon released during fires. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission offers spaceborne light detection and ranging (LiDAR) measurements of global forests, providing excellent opportunities for monitoring forest dynamics and carbon emissions. The ICESat-2 Land and vegetation height product (ATL08), Landsat 8 data, and Sentinel-1 data were integrated to estimate biomass burning emissions with a three-phase hierarchy model. In Phase 1, the least absolute shrinkage and selection operator (LASSO) regression was used to establish the relationship between ATL08 LiDAR metrics and reference aboveground biomass (AGB), R² 0.77 and RMSE 56.67 Mg ha⁻¹. In Phase 2, the LASSO regression predicted AGB for all ATL08 segments. A Random Forest Regression model was trained with Landsat 8 reflectance data and derived vegetation indices, Sentinel-1 data, and terrain variables as predictors and with the LASSO predicted AGB as the response variable, R² 0.71 and RMSE 45.91 Mg ha⁻¹. Wall-to-wall maps of pre-fire and post-fire AGB were produced with the Random Forest model. In Phase 3, the difference between the pre-fire and the post-fire AGB was converted to carbon emissions. The estimated biomass burning emissions of the 2018 Carr fire group, the 2018 Camp fire, and the 2019 Walker fire in California are 3.54 (± 0.067) Tg C, 1.66 (± 0.041) Tg C, and 0.45 (± 0.022) Tg C, respectively. Forests have the highest biomass burning emissions with an average emission of 30.09 Mg ha⁻¹, approximately twice and four times the average emissions from shrubs and grasses. This study highlights the merit of integrating spaceborne remote sensing data, including LiDAR data, optical data, and radar data, for estimating biomass burning emissions, opening an avenue for accurate forest biomass and carbon dynamics monitoring, as well as climate change mitigation.

人为碳排放直接导致全球变暖，这引发了严重的环境问题，如极端干旱和毁灭性火灾。为了评估火灾对碳循环和气候变化的影响，准确估计火灾期间释放的碳量至关重要。冰、云和陆地高程卫星 2 (ICESat-2) 任务提供全球森林的星载光探测和测距 (LiDAR) 测量，为监测森林动态和碳排放提供了绝佳机会。ICESat-2 土地和植被高度产品 (ATL08)、Landsat 8 数据和 Sentinel-1 数据被整合，以使用三相层次模型估算生物质燃烧排放。在第一阶段，² 0.77 和 RMSE 56.67 Mg ha ^-1。在第 2 阶段，LASSO 回归预测了所有 ATL08 段的 AGB。随机森林回归模型使用 Landsat 8 反射率数据和导出的植被指数、Sentinel-1 数据和地形变量作为预测变量进行训练，并使用 LASSO 预测的 AGB 作为响应变量，R ² 0.71 和 RMSE 45.91 Mg ha ^-1. 使用随机森林模型生成火灾前和火灾后 AGB 的墙到墙图。在第 3 阶段，火灾前和火灾后 AGB 之间的差异转化为碳排放量。2018 年卡尔火灾组、2018 年坎普火灾和 2019 年加利福尼亚州沃克火灾的估计生物质燃烧排放量为 3.54 (± 0.067) Tg C、1.66 (± 0.041) Tg C 和 0.45 (± 0.022) Tg C，分别。森林的生物质燃烧排放量最高，平均排放量为 30.09 Mg ha ^-1，大约是灌木和草的平均排放量的两倍和四倍。本研究强调了整合星载遥感数据（包括 LiDAR 数据、光学数据和雷达数据）在估算生物质燃烧排放、准确监测森林生物量和碳动态以及减缓气候变化方面的优势。