浮游植物粒级结构是海洋生态系统中的一个重要生物学因子。基于生物光学参数反演浮游植物粒级结构变化是当前水色遥感研究的热点问题。本文综合南海北部海区多年航次调查数据, 对现有几类反演算法进行了区域性优化和验证评价。根据叶绿素a浓度(Chl a)或浮游植物吸收系数(aph(443))的阈值可实现南海北部海区小型(Micro)和微微型(Pico)浮游植物主导的划分, 微型(Nano)的判别精度较差。基于归一化吸收光谱提取的粒级指数可定性地表征浮游植物粒级结构的综合变化趋势。基于叶绿素a浓度的三组分模型, 较好地模拟浮游植物粒级结构的变化规律, 可实现分粒级叶绿素a浓度的定量反演, Pico粒级的反演精度较高; 在此基础上, 耦合浮游植物吸收光谱变化规律和总叶绿素a浓度定量反演粒级结构的模型, 进一步提高了Micro和Nano粒级的反演精度, 且线性相关程度增强。

Phytoplankton size distribution is an important biological factor in marine ecosystem. Estimating phytoplankton size classes (PSC) based on bio-optical models has been a hot topic in recent studies about ocean color remote sensing. Based on a large dataset collected in the Northern South China Sea, several of these models were validated against in situ observation after locally parameterized, and the ability to detect dominant phytoplankton size classes (micro-, nano-, and Pico plankton) was evaluated. Results show that the dominant roles of micro- and pico-plankton can be generally discriminated according to the chlorophyll a concentration (Chl a) and phytoplankton absorption coefficients (aph(443) with high accuracy compared with the low accuracy for nano-plankton. The size parameter deduced from the normalized phytoplankton spectragenerally shows the trend of PSC. Variations of size-specific chlorophyll a concentration with the total Chl a can be described with the simple three component model, especially for the picoplankton. The PSC retrieval algorithm was further optimized by coupling aph(λ) and total ［Chl a］which improved the accuvacy for micro- and nano-plankton.