Satellite earth observation (EO) data plays a vital role quantifying vegetation structural and functional metrics across spatio-temporal scales. However, the degree of coupling between satellite derived spectral signals and the rate of photosynthesis, as estimated by Gross Primary Productivity (GPP), both before and after bushfire remain understudied, yet these are a critical part of the global carbon cycle. This study evaluated a combination of passive optical and active LiDAR satellite data to quantify the disturbance and recovery of photosynthesis from a major fire event. The work was completed at the Tumbarumba long-term tall eucalypt flux site following a catastrophic bushfire in December 2019. TROPOMI solar-induced fluorescence (SIF) and Sentinel 2 derived greenness and burn severity metrics (NDVI, EVI, NIRv, and NBR) were investigated, termed ‘spectral metrics’ herewith. Detailed in-situ observations from leaf-to-canopy scales were utilised to examine variations in vegetation structural-functional parameters.

中文翻译：

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通过卫星镜头在长期高大的桉树通量站点进行丛林大火恢复：结合多尺度数据以获得结构功能洞察


卫星地球观测 （EO） 数据在量化时空尺度上的植被结构和功能指标方面发挥着至关重要的作用。然而，在丛林大火之前和之后，卫星衍生的光谱信号与光合作用速率之间的耦合程度（如总初级生产力 （GPP） 估计的）仍未得到充分研究，但这些是全球碳循环的关键部分。本研究评估了无源光学和有源 LiDAR 卫星数据的组合，以量化重大火灾事件对光合作用的干扰和恢复。在 2019 年 12 月发生灾难性的森林大火后，这项工作在 Tumbarumba 长期高大的桉树助熔剂站点完成。研究了 TROPOMI 太阳诱导荧光 （SIF） 和 Sentinel 2 衍生的绿度和燃烧严重程度指标（NDVI、EVI、NIRv 和 NBR），以下简称“光谱指标”。利用从叶到冠层尺度的详细原位观察来检查植被结构功能参数的变化。