Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT)
Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT)
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DOI：
10.3389/fmars.2017.00203
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发表时间：
2017-07
影响因子：
3.7
通讯作者：
S. Losa;M. A. Soppa;T. Dinter;Aleksandra Wolanin;R. Brewin;A. Bricaud;J. Oelker;I. Peeken;B. Gentili;V. Rozanov;A. Bracher
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文献类型：
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作者：
S. Losa;M. A. Soppa;T. Dinter;Aleksandra Wolanin;R. Brewin;A. Bricaud;J. Oelker;I. Peeken;B. Gentili;V. Rozanov;A. Bracher
我们得出的叶绿素a浓度（Chla）为三个主要的浮游植物功能类型（PFT）-硅藻，颗石藻和蓝藻-通过结合卫星多光谱为基础的信息，是一个高的空间和时间分辨率，检索基于高分辨率的PFT吸收特性来自高光谱卫星测量。基于多光谱的PFT Chla反演是基于经验的OC-PFT算法的修订版本，适用于海洋颜色气候变化倡议（OC-CCI）总Chla产品。PhytoDOAS分析算法与一些修改，从SCIAMACHY高光谱测量获得PFT叶绿素a。为了协同地联合收割机组合这两个PFT产品（OC-PFT和PhytoDOAS），在给定其Chla及其先验误差统计的情况下，对PhytoDOAS像素内的每个OC-PFT子像素中的每个PFT执行最优插值。协同产品（SynSenPFT）是2002年8月至2012年3月期间，并对PFT Chla数据从原位标记色素数据和美国航天局海洋生物地球化学模型模拟和卫星信息浮游植物大小进行了评估。最具挑战性的方面的SynSenPFT算法的实施进行了讨论。SynSenPFT产品的改进和延长的时间序列在未来几十年的适应哨兵多光谱和高光谱仪器的观点突出。
We derive the chlorophyll a concentration (Chla) for three main phytoplankton functional types (PFTs) – diatoms, coccolithophores and cyanobacteria – by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral satellite measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm applied to the Ocean Color Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 March 2012 and evaluated against PFT Chla data obtained from in situ marker pigment data and the NASA Ocean Biogeochemical Model simulations and satellite information on phytoplankton size. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted.