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Multi-site assessment of the potential of fine resolution red-edge vegetation indices for estimating gross primary production
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-08-20 , DOI: 10.1016/j.jag.2022.102978
Shangrong Lin, Dalei Hao, Yi Zheng, Hu Zhang, Cong Wang, Wenping Yuan

Gross primary production (GPP) models driven by fine resolution remote sensing data characterize the spatial and temporal heterogeneities in plant photosynthesis, which is largely dependent on biome-specific maximum photosynthetic capacity. The red-edge reflectance, sensitive to leaf chlorophyll content, is a good proxy of maximum photosynthetic capacity. More importantly, studies show that the red-edge reflectance-related chlorophyll content index (CIr) multiplied by the incident photosynthetic active radiation (PARin) strongly correlates to GPP estimated at carbon flux towers (GPPflux). Yet, to the best of our knowledge, there is no systematic study investigating the general relationship between fine spatial resolution CIr and GPP among biomes and the relationship between CIr and maximum photosynthetic capacity in GPP models. To provide an overview on incorporating space-borne CIr into a GPP model, we applied fine resolution Sentinel-2-derived CIr and GPPflux over 57 flux sites representative of 10 biomes. We investigated the relationship between CIr and GPPflux, and the spatio-temporal relationship between CIr and ecosystem maximum photosynthetic capacity indicated by the potential ecosystem light use efficiency (LUEpot). We also evaluated the relationship between other five vegetation indices (VIs) and GPPflux. Results showed that the CIr multiplied by PARin has a higher agreement (R2 > 0.5) with GPP than other VIs. A universal relationship exists between the CIr multiplied by PARin and GPP, except for forest biomes. The CIr also strongly (R2 > 0.5) relates to the LUEpot during the peak of the growing season. The CIr has a low spatial variance (CV = 0.25) among biomes, highlighting that CIr can be a proxy of maximum photosynthetic capacity in GPP models that do not require biome-dependent coefficients. This study provides insight for incorporating CIr into GPP models and better quantifying global terrestrial photosynthesis.



中文翻译:

多地点评估高分辨率红边植被指数在估算初级生产总值方面的潜力

由高分辨率遥感数据驱动的总初级生产 (GPP) 模型表征了植物光合作用的空间和时间异质性,这在很大程度上取决于生物群落特定的最大光合作用能力。对叶片叶绿素含量敏感的红边反射率是最大光合能力的良好代表。更重要的是,研究表明红边反射率相关叶绿素含量指数 (CIr) 乘以入射光合有效辐射 (PAR in ) 与在碳通量塔估计的 GPP (GPP通量)。然而,据我们所知,尚无系统研究调查生物群落中精细空间分辨率 CIr 和 GPP 之间的一般关系,以及 GPP 模型中 CIr 与最大光合能力之间的关系。为了概述将星载 CIr 纳入 GPP 模型,我们在代表 10 个生物群落的 57 个通量位点上应用了高分辨率 Sentinel-2 衍生的 CIr 和 GPP通量。我们研究了 CIr 和 GPP通量之间的关系,以及 CIr 与生态系统最大光合能力之间的时空关系,由潜在的生态系统光利用效率 (LUE pot ) 指示。我们还评估了其他五个植被指数 (VI) 与 GPP通量之间的关系. 结果表明,CIr 乘以 PARGPP的一致性(R 2  > 0.5)高于其他 VI。除森林生物群系外, CIr 乘以 PAR 与 GPP 之间存在普遍关系。CIr 也与生长季节高峰期的 LUE罐密切相关(R 2 > 0.5)。CIr 在生物群落中具有较低的空间方差 (CV = 0.25),突出表明 CIr 可以代表不需要生物群落相关系数的 GPP 模型中的最大光合能力。这项研究为将 CIr 纳入 GPP 模型和更好地量化全球陆地光合作用提供了见解。

更新日期:2022-08-20
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