Werdell P Jeremy, McKinna Lachlan I W
Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA.
Go2Q Pty Ltd, Buderim, Queensland, Australia.
Front Earth Sci (Lausanne). 2019;7. doi: 10.3389/feart.2019.00054. Epub 2019 Mar 29.
The Earth science community seeks to develop climate data records (CDRs) from satellite measurements of ocean color, a continuous data record which now exceeds twenty years. Space agencies will launch additional instruments in the coming decade that will continue this data record, including the NASA PACE spectrometer. Inherent optical properties (IOPs) quantitatively describe the absorbing and scattering constituents of seawater and can be estimated from satellite-observed spectroradiometric data using semi-analytical algorithms (SAAs). SAAs exploit the contrasting optical signatures of constituent matter at spectral bands observed by satellite sensors. SAA performance, therefore, depends on the spectral resolution of the satellite spectroradiometer. A CDR spanning SeaWiFS, MODIS, OLCI, and PACE, for example, would include IOPs derived using varied wavelength suites if all available wavelengths were considered. Here, we explored differences in derived IOPs that stem simply from the use of (eight) different wavelength suites of input radiometric measurements. Using synthesized data and SeaWiFS Level-3 mission-long composites, we demonstrated equivalent SAA performance for all wavelength suites, but that IOP retrievals vary by several percent across wavelength suites and as a function of water type. The differences equate to roughly ≤ 6, 12, and 7% for (443), (443), and (443), respectively, for waters with ≤ 1 mg m. These values shrink for sensors with similar wavelength suites (e.g., SeaWiFS, MODIS, and MERIS) and rise to substantially larger values for higher waters. Our results also indicate that including 400 nm (in the case of OLCI) influences the derived IOPs, using longer wavelengths (>600 nm) influences the derived IOPs when there is a red signal, and, including additional spectral information shows potential for improved IOP estimation, but not without revisiting SAA parameterizations and execution. While modest in scope, we believe this study contributes to the knowledge base for CDR development. The implication of ignoring such an analysis as CDRs continue to be developed is a prolonged inability to distinguish between algorithmic and environmental contributions to trends and anomalies in the IOP time-series.
地球科学界试图从海洋颜色的卫星测量数据中开发气候数据记录（CDR），这是一个持续时间已超过20年的数据记录。航天机构将在未来十年发射更多仪器，以延续这一数据记录，包括美国国家航空航天局（NASA）的PACE光谱仪。固有光学特性（IOPs）定量描述了海水的吸收和散射成分，可以使用半解析算法（SAAs）从卫星观测的光谱辐射数据中估算出来。SAAs利用卫星传感器在光谱波段观测到的成分物质的对比光学特征。因此，SAA的性能取决于卫星光谱辐射计的光谱分辨率。例如，如果考虑所有可用波长，一个跨越SeaWiFS、MODIS、OLCI和PACE的CDR将包括使用不同波长组得出的IOPs。在这里，我们探讨了仅因使用（八个）不同的输入辐射测量波长组而导致的派生IOPs差异。使用合成数据和SeaWiFS三级任务长期合成数据，我们证明了所有波长组的SAA性能相当，但IOP反演结果在不同波长组之间以及作为水类型的函数会有几个百分点的差异。对于叶绿素浓度（Chl）≤1mg/m³的水体，对于（443）、（443）和（443），差异分别约为≤6%、12%和7%。对于具有相似波长组的传感器（如SeaWiFS、MODIS和MERIS），这些值会缩小，而对于更高叶绿素浓度的水体，这些值会大幅上升。我们的结果还表明，包括400nm（对于OLCI而言）会影响派生的IOPs，当存在红色信号时使用更长波长（>600nm）会影响派生的IOPs，并且包括额外的光谱信息显示出改进IOP估计的潜力，但这需要重新审视SAA参数化和执行情况。虽然范围有限，但我们相信这项研究有助于为CDR开发提供知识库。随着CDR的不断开发而忽略此类分析的影响是，长期无法区分算法和环境对IOP时间序列趋势和异常的贡献。
