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Semi-analytical approach combined & neural network technology model chlorophyll-a concentration by remote sensing

机译:半解析结合神经网络技术通过遥感模拟叶绿素a浓度

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The accurate assessment of chlorophyll-a concentration in turbid coastal waters by means of remote sensing is quite challenging. In this study, a semi-analytical approach is used to analyze the mathematical relationship between chlorophyll-a concentration and remote sensing reflectance. Through evaluation by field measurements, it is shown that our model produces 31.4% uncertainty in quantifying chlorophyll-a concentration from the YS & ECS. Moreover, the performance of new model was compared with four existing models, and the results indicate that the use of our model for quantifying chlorophyll-a in the YS & ECS can decrease uncertainty by >58% in comparison to the four existing models. The atmospheric influences on MODIS data are removed using a near-infrared-shortwave infrared model.
机译:通过遥感准确评估沿海混浊水域中叶绿素a的浓度非常具有挑战性。在这项研究中,使用半分析方法来分析叶绿素a浓度与遥感反射率之间的数学关系。通过现场测量评估,表明我们的模型在从YS和ECS定量叶绿素a浓度时产生了31.4%的不确定性。此外,将新模型的性能与现有的四个模型进行了比较,结果表明,与现有的四个模型相比,使用我们的模型对YS和ECS中的叶绿素a进行定量可以将不确定性降低58%以上。使用近红外-短波红外模型消除了大气对MODIS数据的影响。

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