发表机构
Princeton University; University of Exeter（普林斯顿大学; 埃克塞特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用符号回归从多光谱和高光谱反射率中识别稀疏方程估算叶绿素a浓度，发现高光谱模型比标准OC6算法精度提升约12%，尤其在浑浊高叶绿素水体中优势显著。
AI 中文摘要
卫星海洋水色算法将离水辐射率转化为现场采样无法实现的时空尺度上的生态信息。由离水辐射率导出的一个重要变量是叶绿素a浓度（$\mathrm{CHL\text{-}a}$），它是浮游植物生物量和生理状态的广泛使用的指标。经验反演算法常用于$\mathrm{CHL\text{-}a}$估算，但其性能在不同传感器和光学多样性的水体中可能有所差异。浮游生物、气溶胶、云、海洋生态系统（PACE）任务提供了前所未有的光谱分辨率，扩展了可用于海洋水色反演的可见光光谱信息，并提出了一个实际的算法设计问题：如何利用这种精细分辨率光谱来推导下一代可解释的$\mathrm{CHL\text{-}a}$反演算法？我们使用符号回归来识别从多光谱和高光谱遥感反射率估算$\log_{10}(\mathrm{CHL\text{-}a})$的稀疏方程。分析首先测试标准多光谱海洋水色算法（OC3--OC6）输入，以询问符号回归是否能恢复标准波段比值结构，然后将搜索扩展到类似PACE的高光谱反射率。发现的最佳表达式在留出数据上实现了0.253的均方根偏差（以$\log_{10}$ $\mathrm{CHL\text{-}a}$计），而同一数据划分上拟合的OC6多项式为0.307。消融实验表明，在留出数据上，高光谱模型的预测技能在均方根偏差方面比最佳标准海洋水色反演模型好近12%。按环境区域评估时，高叶绿素样本（通常代表浑浊水体条件）的差异更大。
英文摘要
Satellite ocean color algorithms translate water-leaving radiance into ecological information at spatial and temporal scales that cannot be achieved by field sampling alone. One important variable derived from water-leaving radiance is chlorophyll-a concentration ($\mathrm{CHL\text{-}a}$), a widely used indicator of phytoplankton biomass and physiology. Empirical retrieval algorithms are commonly used for $\mathrm{CHL\text{-}a}$ estimation, but their performance can vary across sensors and optically diverse waters. The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission provides unprecedented spectral resolution, expanding the visible spectral information available for ocean color retrievals and raising a practical algorithm design question: how can this fine resolution spectrum be used to derive the next generation of interpretable $\mathrm{CHL\text{-}a}$ retrieval algorithms? We use symbolic regression to identify sparse equations that estimate $\log_{10}(\mathrm{CHL\text{-}a})$ from multi- and hyperspectral remote sensing reflectances. The analysis first tests the standard multispectral ocean color algorithm (OC3--OC6) inputs to ask whether symbolic regression recovers standard band ratio structure, then extends the search to hyperspectral PACE-like reflectances. The best expression discovered achieved a held out root mean square deviation of 0.253 in log$_{10}$ $\mathrm{CHL\text{-}a}$, compared with 0.307 for a fitted OC6 polynomial on the same split. Ablations showed that the predictive skill of hyperspectral models is almost 12\% better than the best standard ocean color retrieval models in root mean squared deviation against held out data. When evaluated by environmental regime, differences were larger for high-chlorophyll samples, which often represent turbid water conditions.
