Appl Opt. 2020 May 1;59(13):3971-3984. doi: 10.1364/AO.389189.
The increasing use of hyperspectral optical data in oceanography, both in situ and via remote sensing, holds the potential to significantly advance characterization of marine ecology and biogeochemistry because, in principle, hyperspectral data can provide much more detailed inferences of ecosystem properties via inversion. Effective inferences, however, require careful consideration of the close similarity of different signals of interest, and how these interplay with measurement error and uncertainty to reduce the degrees of freedom (DoF) of hyperspectral measurements. Here we discuss complementary approaches to quantify the DoF in hyperspectral measurements in the case of in situ particulate absorption measurements, though these approaches can also be used on other such data, e.g., ocean color remote sensing. Analyses suggest intermediate (${\sim}5 $∼5) DoF for our dataset of global hyperspectral particulate absorption spectra from the Tara Oceans expedition, meaning that these data can yield coarse community structure information. Empirically, chlorophyll is an effective first-order predictor of absorption spectra, meaning that error characteristics and the mathematics of inversion need to be carefully considered for hyperspectral data to provide information beyond that which chlorophyll provides. We also discuss other useful analytical tools that can be applied to this problem and place our results in the context of hyperspectral remote sensing.
高光谱光学数据在海洋学中的应用日益增多，无论是原位测量还是遥感测量，都有可能极大地推动海洋生态学和生物地球化学的特征描述，因为原则上，高光谱数据可以通过反演提供更详细的生态系统特性推断。然而，有效的推断需要仔细考虑不同感兴趣信号的紧密相似性，以及这些信号如何与测量误差和不确定性相互作用，从而降低高光谱测量的自由度（DoF）。在这里，我们讨论了在原位颗粒吸收测量的情况下量化高光谱测量自由度的补充方法，尽管这些方法也可以用于其他类似的数据，例如海洋颜色遥感。分析表明，我们从 Tara Oceans 考察中获得的全球高光谱颗粒吸收光谱数据集的自由度为中等（${\sim}5$），这意味着这些数据可以提供粗略的群落结构信息。经验上，叶绿素是吸收光谱的有效一阶预测因子，这意味着需要仔细考虑高光谱数据的误差特征和反演数学，以提供超出叶绿素提供的信息。我们还讨论了可应用于该问题的其他有用分析工具，并将我们的结果置于高光谱遥感的背景下。
