Photosynthetic rates derived from satellite-based chlorophyll concentration
Photosynthetic rates derived from satellite-based chlorophyll concentration
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DOI：
10.4319/lo.1997.42.1.0001
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发表时间：
1997-01-01
影响因子：
4.5
通讯作者：
Falkowski, PG
中科院分区：
文献类型：
--
作者：
Behrenfeld, MJ;Falkowski, PG
我们组装了基于 C-14 的生产力测量数据集，以了解根据海面色素浓度 (C-sat) 测量准确评估每日深度综合浮游植物碳固定 (PPeu) 所需的关键变量。根据该数据集，我们开发了一个依赖于光的深度分辨碳固定模型（VGPM），该模型将影响初级生产的环境因素分为影响初级生产相对垂直分布的因素（P-z）和控制生产力概况的最佳同化效率的因素（P-opt（B））。通过使用 P-opt(B) 的测量值，VGPM 占观察到的 P-z 变异性的 79% 和 PPeu 变异性的 86%。我们的结果表明，生产力算法在估计 PPeu 时的准确性主要取决于准确表示 P-opt(B) 变异性的能力。我们开发了一个与温度相关的 P-opt(B) 模型，该模型与 C-sat 的每月气候图像、海面温度和表面辐照度的云校正估计值结合使用，计算出全球每年浮游植物碳固定率 (PPannu) 为 43.5 Pg C yr(-1)。 PPannu 的地理分布与之前模型的结果明显不同。我们的结果说明了将 P-opt(B) 模型开发重点放在时间和空间而不是垂直变化上的重要性。
We assembled a dataset of C-14-based productivity measurements to understand the critical variables required for accurate assessment of daily depth-integrated phytoplankton carbon fixation (PPeu) from measurements of sea surface pigment concentrations (C-sat). From this dataset, we developed a light-dependent, depth-resolved model for carbon fixation (VGPM) that partitions environmental factors affecting primary production into those that influence the relative vertical distribution of primary production (P-z) and those that control the optimal assimilation efficiency of the productivity profile (P-opt(B)). The VGPM accounted for 79% of the observed variability in P-z and 86% of the variability in PPeu by using measured values of P-opt(B). Our results indicate that the accuracy of productivity algorithms in estimating PPeu is dependent primarily upon the ability to accurately represent variability in P-opt(B). We developed a temperature-dependent P-opt(B), model that was used in conjunction with monthly climatological images of C-sat, sea surface temperature, and cloud-corrected estimates of surface irradiance to calculate a global annual phytoplankton carbon fixation (PPannu) rate of 43.5 Pg C yr(-1). The geographical distribution of PPannu was distinctly different than results from previous models. Our results illustrate the importance of focusing P-opt(B) model development on temporal and spatial, rather than the vertical, variability.