Phytoplankton pigments, hyperspectral downwelling irradiance and remote sensing reflectance during POLARSTERN cruises ANT-XXIII/1, ANT-XXIV/1, ANT-XXIV/4, ANT-XXVI/4, and Maria S. Merian cruise MSM18/3@en

The composition and abundance of algal pigments provide information on phytoplankton community characteristics such as photoacclimation, overall biomass and taxonomic composition. In particular, pigments play a major role in photoprotection and in the light-driven part of photosynthesis. Most phytoplankton pigments can be measured by high-performance liquid chromatography (HPLC) techniques applied to filtered water samples. This method, as well as other laboratory analyses, is time consuming and therefore limits the number of samples that can be processed in a given time. In order to receive information on phytoplankton pigment composition with a higher temporal and spatial resolution, we have developed a method to assess pigment concentrations from continuous optical measurements. The method applies an empirical orthogonal function (EOF) analysis to remote-sensing reflectance data derived from ship-based hyperspectral underwater radiometry and from multispectral satellite data (using the Medium Resolution Imaging Spectrometer - MERIS - Polymer product developed by Steinmetz et al., 2011, doi:10.1364/OE.19.009783) measured in the Atlantic Ocean. Subsequently we developed multiple linear regression models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables. The model results show that surface concentrations of a suite of pigments and pigment groups can be well predicted from the ship-based reflectance measurements, even when only a multispectral resolution is chosen (i.e., eight bands, similar to those used by MERIS). Based on the MERIS reflectance data, concentrations of total and monovinyl chlorophyll a and the groups of photoprotective and photosynthetic carotenoids can be predicted with high quality. As a demonstration of the utility of the approach, the fitted model based on satellite reflectance data as input was applied to 1 month of MERIS Polymer data to predict the concentration of those pigment groups for the whole eastern tropical Atlantic area. Bootstrapping explorations of cross-validation error indicate that the method can produce reliable predictions with relatively small data sets (e.g., < 50 collocated values of reflectance and pigment concentration). The method allows for the derivation of time series from continuous reflectance data of various pigment groups at various regions, which can be used to study variability and change of phytoplankton composition and photophysiology.藻类色素的组成与丰度可反映浮游植物群落的多项特征，包括光适应（photoacclimation）、总生物量以及分类学组成。具体而言，色素在光保护及光合作用的光驱动环节中发挥核心作用。绝大多数浮游植物色素可通过高效液相色谱（high-performance liquid chromatography, HPLC）技术对过滤水样进行检测。该方法与其他实验室分析手段一样耗时较长，因此限制了单位时间内可处理的样品总量。 为获取时空分辨率更高的浮游植物色素组成信息，我们开发了一种基于连续光学测量数据估算色素浓度的方法。该方法将经验正交函数（empirical orthogonal function, EOF）分析应用于两类遥感反射率数据集：一类为船载高光谱水下辐射计获取的数据，另一类源自多光谱卫星数据（使用Steinmetz等人2011年开发的中分辨率成像光谱仪（Medium Resolution Imaging Spectrometer, MERIS）- Polymer产品，doi:10.1364/OE.19.009783），数据采集区域为大西洋海域。 随后我们构建了多元线性回归模型，以实测（同步匹配）的色素浓度作为响应变量，以EOF载荷作为预测变量。模型结果显示，即便仅采用多光谱分辨率（即8个波段，与MERIS所使用的波段配置相近），基于船载反射率测量数据仍可较好地预测一系列色素及色素类群的表层浓度。基于MERIS反射率数据，总叶绿素a、单乙烯基叶绿素a，以及光保护类胡萝卜素与光合类胡萝卜素类群的浓度均可被高精度预测。 为展示该方法的应用价值，我们将以卫星反射率数据为输入的拟合模型应用于为期1个月的MERIS Polymer数据集，以预测整个东热带大西洋海域的上述色素类群浓度。交叉验证误差的自助法（Bootstrapping）探索结果表明，该方法可利用相对较小的数据集（例如少于50组反射率与色素浓度的同步匹配值）生成可靠的预测结果。该方法可基于不同区域的连续反射率数据生成各类色素类群的时间序列，用于研究浮游植物组成及光生理特性的动态变化与演变。