A significant challenge in global change research is understanding how vegetation interacts with the environment to influence ecosystem gross primary productivity (GPP) through carbon assimilation. One emerging objective is to consistently predict GPP fluctuations worldwide by establishing a robust scaling relationship between GPP measured at flux towers and satellite spectral reflectance data. However, a major hurdle in achieving this goal is the discrepancy in spatial resolution between early satellite measurements and eddy flux measurements. By using a large set of growing season data covering 100 site-years in North and Central America, we explored the potential of transforming incident and reflected shortwave (R) and photosynthetically active radiation (PAR) measurements into a broadband normalized difference vegetation index (NDVI) and near-infrared (NIR) reflectance of vegetation (NIRv) which simultaneously explains the GPP variability. We found that the broadband NDVI and NIRv derived from R and PAR measurements at the daily time scale were highly correlated with Planet Fusion, Landsat-8/9, and Sentinel-2 narrowband NDVI and NIRv across a wide range of climate and ecological gradients. The differences between satellite and broadband NDVI and NIRv were found to be significantly associated with soil background variations, phenological stages, water stress and signal saturation of broadband NIR reflectance at high biomass. The seasonal variability of broadband NDVI and NIRv remarkably captured the seasonality of vegetation phenology, evaporative fraction, GPP and rainfall in different ecosystems. Although saturation of GPP at high NDVI was evident, a linear relationship between broadband NIRv times incident PAR versus GPP indicated the effectiveness of NIRv-based approach to capture the hidden light use efficiency impacts on GPP. Our study concludes that inexpensive measurement of R and PAR components can provide reliable information on NDVI, NIRv, and GPP uninterruptedly. This enhances the sensing capability of flux tower sites without requiring additional spectrometer measurements. The proposed in-situ vegetation indices make a compelling case on using radiation signals for handshaking between ecosystem-scale measurements and remote sensing observables relevant to carbon uptake.

中文翻译：

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宽带短波和光合有效辐射的净通量补充了 NDVI 和植被的近红外反射率，以解释整个生态系统和气候的总光合作用变异性

全球变化研究的一个重大挑战是了解植被如何与环境相互作用，通过碳同化影响生态系统总初级生产力（GPP）。一个新兴目标是通过在通量塔测量的 GPP 与卫星光谱反射数据之间建立稳健的比例关系来一致预测全球 GPP 波动。然而，实现这一目标的一个主要障碍是早期卫星测量和涡流测量之间的空间分辨率差异。通过使用覆盖北美和中美洲 100 个地点年的大量生长季节数据，我们探索了将入射和反射短波 (R) 和光合有效辐射 (PAR) 测量值转换为宽带归一化植被指数 (NDVI) 的潜力。 ）和植被近红外（NIR）反射率（NIRv）同时解释了 GPP 的变异性。我们发现，从每日时间尺度的 R 和 PAR 测量得出的宽带 NDVI 和 NIRv 在广泛的气候和生态梯度范围内与 Planet Fusion、Landsat-8/9 和 Sentinel-2 窄带 NDVI 和 NIRv 高度相关。卫星和宽带 NDVI 和 NIRv 之间的差异被发现与土壤背景变化、物候阶段、水分胁迫和高生物量下宽带 NIR 反射率的信号饱和度显着相关。宽带NDVI和NIRv的季节变化显着地捕捉了不同生态系统植被物候、蒸发分数、GPP和降雨量的季节性变化。尽管高 NDVI 下的 GPP 饱和很明显，但宽带 NIRv 时间入射 PAR 与 GPP 之间的线性关系表明基于 NIRv 的方法可以有效捕获隐藏的光利用效率对 GPP 的影响。我们的研究得出结论，R 和 PAR 分量的廉价测量可以不间断地提供有关 NDVI、NIRv 和 GPP 的可靠信息。这增强了通量塔站点的传感能力，而无需额外的光谱仪测量。拟议的原位植被指数为使用辐射信号在生态系统规模测量和与碳吸收相关的遥感观测值之间进行握手提供了令人信服的案例。