An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)-猫眼课题宝

DOI：

10.3390/s19194285

发表时间：

2019-10

期刊：

Sensors (Basel, Switzerland)

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

S. Sathyendranath;R. Brewin;C. Brockmann;V. Brotas;Ben Calton;A. Chuprin;P. Cipollini;André B. Couto;James Dingle;R. Doerffer;C. Donlon;M. Dowell;Alex Farman;M. Grant;S. Groom;A. Horseman;Thomas Jackson;H. Krasemann;S. Lavender;V. Martínez-Vicente;C. Mazeran;F. Mélin;T. Moore;D. Müller;P. Regner;Shovonlal Roy;C. Steele;F. Steinmetz;J. Swinton;M. Taberner;A. Thompson;André Valente;Marco Zühlke;V. Brando;Hui Feng;G. Feldman;B. Franz;R. Frouin;R. Gould;S. Hooker;M. Kahru;S. Kratzer;B. Mitchell;F. Müller-Karger;H. Sosik;K. Voss;J. Werdell;T. Platt

海洋颜色被全球气候观测系统（GCOS）确认为基本气候变量（ECV）;可见光区的光谱分辨离水辐射率（或遥感反射率）和叶绿素a浓度被确定为所需的ECV产品。从海洋颜色数据中获得的全球范围高空间分辨率产品时间序列，对于研究浮游植物在季节和年际范围内的动态、它们在海洋生物地球化学中的作用、全球碳循环、浮游植物如何在海洋上层分配太阳热量的调节以及海洋生态系统对气候多变性和变化的反应等问题至关重要。然而，从海洋颜色数据生成这些产品的长时间序列并不是一项微不足道的任务：必须从可用于卫星信号大气校正和叶绿素a浓度反演的若干算法中选择最适合气候研究的算法;由于卫星具有有限的寿命，来自多个传感器的数据必须被合并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系列中引入伪像，例如，不同的传感器监视不同波段的辐射，使得产生一致的反射率时间序列不是简单的。另一个要求是，必须根据现场观测结果对产品进行验证。此外，产品中的不确定性必须量化，最好是逐像素量化，以便于与数据质量一致的应用和解释。本文概述了利用MERIS数据生成气候研究用海洋色时间序列的方法欧洲航天局的中光谱分辨率成像光谱仪传感器; SeaWiFS（海景宽视场传感器）和MODIS-Aqua（美国国家航空航天局的中分辨率成像光谱辐射计-Aqua）传感器;和VIIRS（可见光和红外成像辐射计套件）。时间序列现在涵盖1997年底至2018年底。为确保这些产品尽可能满足用户群体的要求，从一开始就征求了海洋生态系统建模者和遥感科学家的意见，了解他们的近期和长期要求以及他们对海洋颜色数据用于气候研究的期望。考虑到用户的要求，制定了一系列客观标准，并根据这些标准对处理海洋颜色数据的现有算法进行了评价和排序。选择在气候用户要求方面表现最好的算法来处理来自卫星传感器的数据。对来自MODIS-Aqua、MERIS和VIIRS的遥感反射率数据进行了带移，以匹配SeaWiFS的波段。使用重叠数据来纠正每个像素处传感器之间的平均偏差。将来自传感器的遥感反射率数据合并，并将选定的水中算法应用于合并的数据，以生成叶绿素浓度、SeaWiFS波长处的固有光学特性和490 nm处的漫衰减系数的地图。合并的产品进行了验证，对原位观察。在与实地数据比较的基础上确定的不确定性与使用模糊逻辑方法对遥感反射率数据进行的光学分类相结合，并用于产生每个像素处每个产品的不确定性（均方根差和偏差）。

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.

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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