Modeling the relationship between primary production, optical properties, and nutrients in the sea
Modeling the relationship between primary production, optical properties, and nutrients in the sea
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DOI：
10.1117/12.140655
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发表时间：
1992-12
期刊：
影响因子：
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通讯作者：
B. Woźniak;J. Dera;O. J. Koblentz-Mishke
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文献类型：
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作者：
B. Woźniak;J. Dera;O. J. Koblentz-Mishke
1978 - 1991年期间，波兰-俄罗斯9个大型研究考察队和其他前往世界海洋各区域的小型考察队的大量经验数据被用来编制这一统计关系的第一近似模型，主要是叶绿素a浓度与海面下太阳辐照度之间的关系，以及叶绿素a的垂直分布，浮游植物的吸收光谱、向下辐射衰减光谱、光合作用的量子产额以及不同营养度沃茨初级生产力的其他平均日变化特征。这些模型关系有助于制定一种算法，用于根据叶绿素a浓度和海面辐照度数据计算特定类型海水的光能垂直分布和初级生产特征。这些模型公式的验证与来自各种来源的经验数据的帮助下，表明它们提供了良好的结果-平均统计误差相对于在现场测量范围从10%到80%，这取决于所讨论的特性。为了提高该算法的准确性，需要大量的统计数据，并且必须更加密切地关注营养物质和其他环境因素对所评估的特征的影响。该算法在遥感海洋初级生产力方面特别有用。
Numerous empirical data from nine large Polish-Russian research expeditions and other smaller expeditions to various regions of the World Ocean during 1978 - 1991 were used to compile this first approximate model of statistical relationships, chiefly between the concentration of chlorophyll a and the solar irradiance just below the sea surface on the one hand, and the vertical distribution of chlorophyll a, phytoplankton absorption spectra, downward irradiance attenuation spectra, the quantum yield of photosynthesis, as well as other mean diurnal characteristics of primary production in waters of different trophicity on the other. These model relationships served to work out an algorithm for computing the vertical distributions of light energy and primary production characteristics in particular types of sea water from data on chlorophyll a concentration and irradiance at the sea surface. Verification of these model formulas with the aid of empirical data from a variety of sources has shown that they provide good results -- the mean statistical errors with respect to in situ measurements range from ca 10% to 80%, depending on the characteristic in question. In order to improve the accuracy of this algorithm, a much larger number of statistical data is needed, and closer attention must be paid to the effect of nutrients and other environmental factors on the characteristics being assessed. This algorithm could be especially useful in the remote sensing of primary production in the ocean.