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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)
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An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

机译：气候研究中使用的海洋颜色时间序列：海洋颜色气候变化倡议（OC-CCI）的经验

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摘要

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.
机译：全球气候观测系统（GCOS）将海洋颜色识别为基本气候变量（ECV）；可见光范围内的光谱分辨出的水辐射率（或遥感反射率）和叶绿素a浓度被确定为必需的ECV产品。从海洋颜色数据得出的全球尺度和高空间分辨率产品的时间序列，是研究季节性和年际尺度上浮游植物动态的关键；它们在海洋生物地球化学中的作用；全球碳循环；调节浮游植物如何在海洋上层分配太阳热；以及海洋生态系统对气候变化和变化的响应。但是，从海洋颜色数据中生成这些产品的长时间序列并不是一件容易的事：必须从可用于卫星信号的大气校正和获取卫星信号的数量中选择最适合气候研究的算法。叶绿素a浓度；由于卫星的寿命有限，因此必须合并来自多个传感器的数据以创建单个时间序列，并且任何未校正的传感器间偏差都可能在该序列中引入伪影，例如，不同的传感器在不同的波段监视辐射，从而产生一致的反射时间序列并非易事。另一个要求是必须对产品进行现场观察验证。此外，必须对产品中的不确定性进行量化，最好是逐个像素地进行量化，以促进与数据质量一致的应用和解释。本文概述了一种方法，该方法使用了来自欧洲航天局MERIS（中光谱分辨率成像光谱仪）传感器的数据来生成用于气候研究的海洋时间序列；美国国家航空航天局（美国）的SeaWiFS（海景宽视场传感器）和MODIS-Aqua（中等分辨率成像光谱辐射计-Aqua）传感器；美国国家海洋和大气管理局（美国）的VIIRS（可见光和红外成像辐射计套件）。现在的时间范围是从1997年末到2018年末。为了确保产品尽可能满足用户群体，海洋生态系统建模者和遥感科学家的要求，他们对眼前和长期的要求，以及对用于气候研究的海洋颜色数据的期望。考虑到用户需求，建立了一系列客观标准，针对这些标准评估了可用于处理海洋颜色数据的算法并对其进行了排名。选择了在气候用户需求方面表现最佳的算法来处理来自卫星传感器的数据。将来自MODIS-Aqua，MERIS和VIIRS的遥感反射率数据进行了频移以匹配SeaWiFS的波段。重叠数据用于校正每个像素处传感器之间的平均偏差。合并从传感器获得的遥感反射率数据，并将选定的水中算法应用于合并后的数据，以生成叶绿素浓度，SeaWiFS波长处的固有光学性质以及490 nm处的漫反射系数的图。合并后的产品针对现场观察进行了验证。在与原位数据比较的基础上建立的不确定度与模糊反射法的遥感反射率数据的光学分类相结合，并用于生成每种产品的不确定度（均方根差和偏差）每个像素。

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