Dynamic emergent leaf area in tidal wetlands: Implications for satellite-derived regional and global blue carbon estimates
Dynamic emergent leaf area in tidal wetlands: Implications for satellite-derived regional and global blue carbon estimates
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DOI：
10.1016/j.rse.2023.113553
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发表时间：
2023-05
影响因子：
13.5
通讯作者：
P. Hawman;D. Mishra;Jessica L. O’Connell
中科院分区：
文献类型：
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作者：
P. Hawman;D. Mishra;Jessica L. O’Connell
IPCC关于气候变化中的海洋和冰冻圈的特别报告强调了潮汐湿地中蓝碳在应对气候变化方面的重要性。在这项研究中，我们强调的不确定性与叶面积指数（LAI）估计在潮汐湿地，特别是盐沼，生产力模型和地球系统模型（ESM）的关键植被参数。从卫星反射率数据得出的叶面积指数与植被生态系统的大气碳交换和总初级生产力（GPP）有关。然而，估计盐沼叶面积指数是具有挑战性的，因为冠层高度和密度在短距离内变化，潮汐洪水改变了暴露在大气中的叶面积，以下称为紧急叶面积指数（ELAI），在短时间尺度。此外，在以互花米草（Spartinaalterniflora）等物种为主的潮汐湿地中，冠层高度和密度在短距离内变化。提出了一种新的方法来测量潮汐湿地ELAI的时空动态。我们模拟ELAI垂直叶面积指数配置文件，并创建跨潮汐期的空间估计。然后，我们联系ELAI与涡度相关碳（C）通量，通过足迹模型，并揭示出出露叶面积和C通量之间的相关性。接下来，我们证明，ELAI可以很容易地估计在10米的空间尺度上使用哨兵-2卫星数据，即使在涨潮（R2= 0.89; NRMSE = 10%）。最后，我们发现一个共同的产品，MODIS MYD 15 A2 H，低估（20%）叶面积指数在干旱条件下，但高估（7-93%）在高洪水。在为受海平面加速上升威胁的生态系统制定蓝碳预算时，动态ELAI可以减少卫星导出的全球全球伙伴关系产品的不确定性。
The IPCC Special Report on the Ocean and Cryosphere in a Changing Climate highlights the importance of blue carbon in tidal wetlands in combating climate change. In this study, we highlight the uncertainty associated with leaf area index (LAI) estimations in tidal wetlands, specifically salt marshes, a key vegetation parameter for productivity models and Earth System Models (ESM). LAI, derived from satellite reflectance data, is linked to atmospheric carbon exchange and gross primary production (GPP) across vegetative ecosystems. However, estimating salt marsh LAI is challenging because canopy height and density vary across short distances, and tidal flooding alters the atmosphere-exposed leaf area, hereafter called emergent leaf area index (ELAI), at short time scales. Further, in tidal wetlands dominated by species such asSpartinaalterniflora, canopy height and density vary across short distances. We present a novel approach for measuring spatiotemporal dynamics in tidal wetland ELAI. We modeled ELAI from vertical LAI profiles and created spatial estimates across tidal periods. We then linked ELAI with eddy covariance carbon (C) fluxes through footprint modeling and revealed correlations between emergent leaf area and C fluxes. Next, we demonstrated that ELAI can be readily estimated across 10-m spatial scales using Sentinel-2 satellite data, even during high tides (R2= 0.89; NRMSE = 10%). Finally, we showed a common product, MODIS MYD15A2H, underestimated (20%) LAI during dry conditions but overestimated (7–93%) during high flooding. Dynamic ELAI could reduce uncertainties in satellite-derived global GPP products when developing blue carbon budgets for ecosystems threatened by accelerated sea level rise.