Accurate estimates of the oceanic particulate organic carbon concentration (POC) from optical measurements have remained challenging because interactions between light and natural assemblages of marine particles are complex, depending on particle concentration, composition, and size distribution. In particular, the applicability of a single relationship between POC and the spectral particulate backscattering coefficient b_bp(λ) across diverse oceanic environments is subject to high uncertainties because of the variable nature of particulate assemblages. These relationships have nevertheless been widely used to estimate oceanic POC using, for example, in situ measurements of b_bp from Biogeochemical (BGC)-Argo floats. Despite these challenges, such an in situbased approach to estimate POC remains scientifically attractive in view of the expanding global-scale observations with the BGC-Argo array of profiling floats equipped with optical sensors. In the current study, we describe an improved empirical approach to estimate POC which takes advantage of simultaneous measurements of b_bp and chlorophyll-a fluorescence to better account for the effects of variable particle composition on the relationship between POC and b_bp. We formulated multivariable regression models using a dataset of field measurements of POC, b_bp, and chlorophyll-a concentration (Chla), including surface and subsurface water samples from the Atlantic, Pacific, Arctic, and Southern Oceans. The analysis of this dataset of diverse seawater samples demonstrates that the use of b_bp and an additional independent variable related to particle composition involving both b_bp and Chla leads to notable improvements in POC estimations compared with a typical univariate regression model based on b_bp alone. These multivariable algorithms are expected to be particularly useful for estimating POC with measurements from autonomous BGC-Argo floats operating in diverse oceanic environments. We demonstrate example results from the multivariable algorithm applied to depth-resolved vertical measurements from BGC-Argo floats surveying the Labrador Sea.

从光学测量中准确估计海洋颗粒有机碳浓度 (POC) 仍然具有挑战性，因为光与海洋颗粒的自然组合之间的相互作用很复杂，具体取决于颗粒浓度、组成和尺寸分布。特别是 POC 与光谱微粒后向散射系数之间单一关系的适用性bp _{_}由于颗粒组合的可变性，不同海洋环境中的 (λ) 具有很高的不确定性。然而，这些关系已被广泛用于估计海洋 POC，例如，原位的测量bp _{_}来自生物地球化学 (BGC)-Argo 漂浮物。尽管存在这些挑战，但这样一个原位鉴于配备光学传感器的 BGC-Argo 剖面浮标阵列正在扩大全球规模的观测，基于估计 POC 的方法仍然具有科学吸引力。在当前的研究中，我们描述了一种改进的经验方法来估计 POC，它利用同时测量bp _{_}和叶绿素-a 荧光，以更好地解释可变颗粒组成对 POC 和bp _{_}. 我们使用 POC 现场测量数据集制定了多变量回归模型，bp _{_}和叶绿素-a 浓度 (Chla)，包括来自大西洋、太平洋、北极和南大洋的地表水和地下水样品。对不同海水样本数据集的分析表明，使用bp _{_}和一个与粒子组成相关的额外自变量，涉及两者bp _{_}与基于基于bp _{_}独自的。预计这些多变量算法对于使用在不同海洋环境中运行的自主 BGC-Argo 浮标的测量值来估计 POC 特别有用。我们展示了多变量算法的示例结果，该算法应用于 BGC-Argo 浮标测量拉布拉多海的深度分辨垂直测量。