Forests for forests: combining vegetation indices with solar-induced chlorophyll fluorescence in random forest models improves gross primary productivity prediction in the boreal forest
Forests for forests: combining vegetation indices with solar-induced chlorophyll fluorescence in random forest models improves gross primary productivity prediction in the boreal forest
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森林换森林：在随机森林模型中将植被指数与太阳诱导叶绿素荧光相结合，提高了北方森林总初级生产力的预测
DOI：
10.1088/1748-9326/aca5a0
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发表时间：
2022-11
影响因子：
6.7
通讯作者：
Z. Pierrat;J. Bortnik;Bruce Johnson;A. Barr;T. Magney;D. Bowling;N. Parazoo;C. Frankenberg;U. Seibt;J. Stutz
中科院分区：
文献类型：
--
作者：
Z. Pierrat;J. Bortnik;Bruce Johnson;A. Barr;T. Magney;D. Bowling;N. Parazoo;C. Frankenberg;U. Seibt;J. Stutz
遥感是了解植物通过光合作用、总初级生产力（GPP）跨空间和时间吸收碳的有力工具。遥感测量的成功可归因于它们能够捕获有关植物结构（物理）和功能（生理）的宝贵信息，这两者都影响GPP。然而，没有单一的遥感测量能够提供对GPP的普遍约束，而且遥感测量和GPP之间的关系往往是特定地点的，从而限制了更广泛的用途，并忽略了这些信号中的重要细微差别。我们必须改进如何将遥感测量与GPP联系起来，特别是在传统上对遥感研究具有挑战性的北方生态系统中。本文采用随机森林模型作为定量框架，结合太阳诱导荧光（SIF）和植被指数（VIs）提供的物理和生理信息，改进了GPP预测。我们分析了北美北方针叶林北部和南部两个野外地点2.5年的塔基遥感数据（SIF和VIs）。我们发现(a)遥感产品包含与理解GPP动态相关的信息，(b)随机森林模型捕获定量的SIF、GPP和光可用性关系，以及(c)在随机森林模型中结合SIF和VIs优于传统的仅基于SIF的GPP参数化。我们基于SIF和VIs预测GPP的新方法提高了我们量化北方生态系统陆地碳交换的能力，并具有应用于其他生物群系的潜力。
Remote sensing is a powerful tool for understanding and scaling measurements of plant carbon uptake via photosynthesis, gross primary productivity (GPP), across space and time. The success of remote sensing measurements can be attributed to their ability to capture valuable information on plant structure (physical) and function (physiological), both of which impact GPP. However, no single remote sensing measure provides a universal constraint on GPP and the relationships between remote sensing measurements and GPP are often site specific, thereby limiting broader usefulness and neglecting important nuances in these signals. Improvements must be made in how we connect remotely sensed measurements to GPP, particularly in boreal ecosystems which have been traditionally challenging to study with remote sensing. In this paper we improve GPP prediction by using random forest models as a quantitative framework that incorporates physical and physiological information provided by solar-induced fluorescence (SIF) and vegetation indices (VIs). We analyze 2.5 years of tower-based remote sensing data (SIF and VIs) across two field locations at the northern and southern ends of the North American boreal forest. We find (a) remotely sensed products contain information relevant for understanding GPP dynamics, (b) random forest models capture quantitative SIF, GPP, and light availability relationships, and (c) combining SIF and VIs in a random forest model outperforms traditional parameterizations of GPP based on SIF alone. Our new method for predicting GPP based on SIF and VIs improves our ability to quantify terrestrial carbon exchange in boreal ecosystems and has the potential for applications in other biomes.