Observing optically complex oceans in situ and from space : a radiative transfer approach to determining improved algorithms and uncertainties.

卫星载海洋颜色传感器可以提供悬浮在自然水域的物质在空间尺度上的数据，这是任何其他传感器平台无法比拟的。可以从系泊天文台的光学传感器获得更高时间分辨率的数据。两种数据收集模式的结合对于为生态系统和气候变化模型提供数据具有重要意义。然而，目前在获取水质参数（如叶绿素浓度和扩散衰减系数）的解释算法的性能方面存在严重的局限性。标准算法通常针对清澈的海水进行了优化，但在光学复杂的水域中表现不佳，其中非藻类物质包括无机颗粒（例如沉积物和灰尘），有色溶解有机物质（CDOM）甚至气泡都会对性能产生负面影响。另一个主要问题是，遥感数据的最终用户不容易获得关于遥感产品准确性的信息。该项目将通过现场光学测量和辐射传输模拟相结合，解决影响遥感光学信号解释的一些不确定因素。将特别注意发展高质量的原位吸收测量，这些测量目前在浑浊水中受到不完全解决的散射误差的影响。将开发方法将吸收和散射系数（来自原位和遥感数据）划分为不同物质成分（藻类和非藻类）的组成部分。将开发从原位和遥感光学数据中检索CDOM和颗粒有机碳（POC）的新算法。将使用现场数据集和通过辐射转移建模获得的更大范围成分的模拟数据集，评估遥感算法对不同水类型的性能。将确定遥感产品的误差，并以彩色编码的地图表示，提供给最终用户。这项工作将改进分析沿海水域遥感信号的算法，并将有助于更好地了解海洋环境中生物地球物理过程与气候变化因素之间的关系。改进的解释算法将为监测机构提供更高质量的数据产品，以确保遵守法律义务，并使决策者做出明智的决定。

Satellite-borne ocean colour sensors can provide data on the materials suspended in natural waters on spatial scales that are unmatched by any other sensor platform. Higher temporal resolution data can be obtained from optical sensors on moored observatories. The combination of both modes of data collection is important for providing data for models of ecosystems and climate change. However, there are currently serious limitations in the performance of interpretation algorithms for retrieving water quality parameters such as concentrations of chlorophyll and diffuse attenuation coefficients. Standard algorithms have generally been optimised for clear oceanic waters and perform poorly in optically complex waters, where non-algal materials including inorganic particles (e.g. sediments and dusts), coloured dissolved organic materials (CDOM) and even bubbles can have a negative impact on performance. Another major problem is the fact that end-users of remote sensing data do not have easy access to information about the accuracy of remote sensing products. This project will resolve some of the uncertainties affecting interpretation of remote sensing optical signals through a combination of in situ optical measurements and radiative transfer simulations. Particular attention will be paid to developing improved quality in situ absorption measurements that are currently subject to incompletely resolved scattering errors in turbid waters. Methods will be developed for partitioning absorption and scattering coefficients (from in situ and remote sensing data) into components for different material constituents (algal and non-algal). New algorithms will be developed to retrieve CDOM and particulate organic carbon (POC) from in situ and remote sensing optical data. The performance of remote sensing algorithms will be assessed for different water types using in situ data sets and a simulated data set over extended ranges of constituents obtained by radiative transfer modelling. The errors for remote sensing products will be determined and presented as colour-coded maps that will be made available to end-users. This work will result in improved algorithms for analysing remote sensing signals for coastal waters and will contribute to an improved understanding of relationships between biogeophysical processes and climate change factors in the marine environment. Improved interpretation algorithms will result in better quality data products for monitoring agencies to assure compliance with legal obligations and policy makers to make informed decisions with.