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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing
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An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing

机译:从卫星遥感检测优势浮游植物大小类别的生物光学技术的比较

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Satellite remote sensing of ocean colour is the only method currently available for synoptically measuring wide-area properties of ocean ecosystems, such as phytoplankton chlorophyll biomass. Recently, a variety of bio-optical and ecological methods have been established that use satellite data to identify and differentiate between either phytoplankton functional types (PFTs) or phytoplankton size classes (PSCs). In this study, several of these techniques were evaluated against in situ observations to determine their ability to detect dominant phytoplankton size classes (micro-, nano- and picoplankton). The techniques are applied to a 10-year ocean-colour data series from the SeaWiFS satellite sensor and compared with in situ data (6504 samples) from a variety of locations in the global ocean. Results show that spectral-response, ecological and abundance-based approaches can all perform with similar accuracy. Detection of microplankton and picoplankton were generally better than detection of nanoplankton. Abundance-based approaches were shown to provide better spatial retrieval of PSCs. Individual model performance varied according to PSC, input satellite data sources and in situ validation data types. Uncertainty in the comparison procedure and data sources was considered. Improved availability of in situ observations would aid ongoing research in this field.
机译:卫星遥感海洋颜色是目前用于对海洋生态系统(例如浮游植物叶绿素生物量)进行广域特性的概要测量的唯一方法。最近,已经建立了多种生物光学和生态方法,这些方法使用卫星数据来识别和区分浮游植物功能类型(PFT)或浮游植物大小类别(PSC)。在这项研究中,对这些技术中的几种进行了现场观测评估,以确定它们检测主要浮游植物大小类别(微型,纳米和微微浮游生物)的能力。该技术已应用于SeaWiFS卫星传感器提供的十年海洋颜色数据系列,并与来自全球海洋各个位置的原位数据(6504个样本)进行了比较。结果表明,光谱响应,生态学和基于丰度的方法都可以以相似的精度执行。浮游生物和微微浮游生物的检测通常要优于纳米浮游生物的检测。基于丰度的方法显示可以提供更好的PSC空间检索。各个模型的性能根据PSC,输入的卫星数据源和原位验证数据类型而异。考虑了比较程序和数据来源的不确定性。改进原位观测的可用性将有助于该领域的正在进行的研究。

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