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A model for estimating size-fractioned phytoplankton absorption coefficients in coastal and oceanic waters from satellite data

机译:根据卫星数据估算沿海和海洋水域浮游植物的大小分解吸收系数的模型

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One of the central goals of using satellite ocean color remote sensing is to detect and identify different phytoplankton groups (size classes) and describe their variability continuously and synoptically for various applications including marine ecosystem dynamics, carbon and biogeochemical cycles, and related fields in oceanography. Taking the advantage of phytoplankton communities having distinct optical properties, this study presents a new model to explicitly detect and differentiate between three phytoplankton size-classes namely, picophytoplankton, nanophytoplankton, and microphytoplankton, based on distinct differences in the optical signatures of these phytoplankton groups in a wide variety of coastal and oceanic waters. The model is based on the assumption that there is a significant relationship between chlorophyll-a concentration, and total as well as the size-fractioned absorption coefficients of phytoplankton. The new model is validated using three different in-situ datasets collected from a wide variety of locations in the global and regional oceans (including turbid coastal and eutrophic waters) and its results are further compared with those of the existing two- and three-component models. The new model performs better than other models in terms of yielding more accurate estimates of the total and size-dependent phytoplankton absorption coefficients across the entire visible wavelengths. Since satellite observation of 'ocean color' as detected by a remote sensor provides an estimate of the chlorophyll-a concentration, commonly used as an index of phytoplankton biomass, the new model is also applied to regional and global images of seasonal climatology over a decade of satellite ocean color observations provided by the MODIS-Aqua sensor. When applied to the MODIS-Aqua images of the Arabian Sea dominated by spatially intense algal blooms, the present model is generally excellent at predicting and describing the spatial distribution of these phytoplankton groups within cyclonic eddies and adjacent regions in the Arabian Sea. Conversely, size-fractioned phytoplankton absorption coefficients derived from global images of seasonal climatology are found to vary depending on the season and ocean basin. These global images imply that when phytoplankton abundance increases, larger size-classes are added incrementally to a background of smaller cells. Further examination of these data showed that picophytoplankton population is generally low, although dominating a major part of the surface ocean during summer and winter. Nanophytoplankton and microphytoplankton populations are high in surface waters of the North and South Atlantic, North and South Pacific Oceans, Arabian Sea, and equatorial region, showing an increasing trend in summer and a decreasing trend in winter in each hemisphere. These results suggest that the new model is an important tool which will inspire further research to investigate different phytoplankton size classes and their variability on regional and global scales. (C) 2014 Elsevier Inc All rights reserved.
机译:使用卫星海洋彩色遥感技术的主要目标之一是检测和识别不同的浮游植物群(大小类别),并连续地和概要地描述其变化性,以用于各种应用,包括海洋生态系统动力学,碳和生物地球化学循环以及海洋学的相关领域。利用具有不同光学特性的浮游植物群落的优势,本研究提出了一种新模型,可以基于这些浮游植物群体在光学特征上的明显差异,显着地检测和区分微浮游植物,纳米浮游植物和微浮游植物的三种大小类别。各种各样的沿海和海洋水域。该模型基于以下假设:叶绿素a浓度与浮游植物的总吸收系数以及大小分馏吸收系数之间存在显着关系。使用从全球和区域海洋(包括浑浊的沿海和富营养化水域)的各种位置收集的三个不同的原位数据集验证了该新模型,并将其结果与现有的两分量和三分量数据进行了进一步比较楷模。新模型在产生更准确的整个可见光波长范围内的总浮游植物吸收系数和大小相关的估计方面,比其他模型表现更好。由于通过遥感器对卫星观测到的“海洋颜色”进行估算,可以估算叶绿素-a的浓度(通常用作浮游植物生物量的指标),因此该新模型还可以应用于十年来的区域和全球季节性气候图像MODIS-Aqua传感器提供的卫星海洋颜色观测数据。当应用于以空间密集藻华为主的阿拉伯海的MODIS-Aqua影像时,本模型通常非常适合预测和描述阿拉伯海旋风涡旋及邻近区域内这些浮游植物群的空间分布。相反,发现根据季节气候的全球图像得出的按大小划分的浮游植物吸收系数根据季节和洋盆而变化。这些整体图像暗示,当浮游植物的丰度增加时,较大的类别将逐渐添加到较小细胞的背景中。对这些数据的进一步检查表明,尽管在夏季和冬季,浮游植物的浮游生物种群总体较低,尽管在表层海洋中占主要部分。在北大西洋和南大西洋,北太平洋和南太平洋,阿拉伯海和赤道地区的地表水中,纳米浮游植物和微浮游植物的数量很高,每个半球的夏季呈上升趋势,冬季呈下降趋势。这些结果表明,新模型是一种重要的工具,将激发进一步的研究,以研究不同浮游植物的大小类别及其在区域和全球范围内的变异性。 (C)2014 Elsevier Inc保留所有权利。

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