An Artificial Neural Network Algorithm to Retrieve Chlorophyll a for Northwest European Shelf Seas from Top of Atmosphere Ocean Colour Reflectance
An Artificial Neural Network Algorithm to Retrieve Chlorophyll a for Northwest European Shelf Seas from Top of Atmosphere Ocean Colour Reflectance
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DOI：
10.3390/rs14143353
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发表时间：
2022-05
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通讯作者：
Madjid Hadjal;E. Medina-Lopez;Jinchang Ren;A. Gallego;D. McKee
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作者：
Madjid Hadjal;E. Medina-Lopez;Jinchang Ren;A. Gallego;D. McKee
由于非藻类物质对大气校正和标准Chl算法性能的影响，对于相对浑浊的沿海沃茨水域，从海洋颜色遥感中提取叶绿素a（Chl）存在问题。人工神经网络（NN）提供了一种替代方法，从空间和结果检索的叶绿素在2002-2020年期间，西北欧陆架海。NN在15个MODIS-Aqua可见光和红外波段上工作，并使用大气层底部（BOA）、大气层顶部（TOA）和瑞利校正TOA反射率（RC）进行测试。在每种情况下，与该地区使用的当前最先进的算法相比，由3层15个神经元组成的NN架构提高了性能和数据可用性。在TOA反射率上操作的NN优于BOA和RC版本。通过对TOA反射率数据的处理，神经网络方法克服了沿海沃茨中常见但困难的大气校正问题。此外，NN提供了其他算法经常掩盖浑浊的水或低天顶角标志的区域的数据。NN方法的一个显着特点是生成相关的产品不确定性的训练数据集的基础上产生的值为每个像素的分布的多个restrom，并示出了一个例子，在北海的沿海时间序列。NN方法的最终输出由基于每个像素的中位数的最佳估计图像和基于每个像素的标准差表示不确定性的第二图像组成，提供最终产品中的不确定性的像素特定估计。
Chlorophyll-a (Chl) retrieval from ocean colour remote sensing is problematic for relatively turbid coastal waters due to the impact of non-algal materials on atmospheric correction and standard Chl algorithm performance. Artificial neural networks (NNs) provide an alternative approach for retrieval of Chl from space and results for northwest European shelf seas over the 2002–2020 period are shown. The NNs operate on 15 MODIS-Aqua visible and infrared bands and are tested using bottom of atmosphere (BOA), top of atmosphere (TOA) and Rayleigh corrected TOA reflectances (RC). In each case, a NN architecture consisting of 3 layers of 15 neurons improved performance and data availability compared to current state-of-the-art algorithms used in the region. The NN operating on TOA reflectance outperformed BOA and RC versions. By operating on TOA reflectance data, the NN approach overcomes the common but difficult problem of atmospheric correction in coastal waters. Moreover, the NN provides data for regions which other algorithms often mask out for turbid water or low zenith angle flags. A distinguishing feature of the NN approach is generation of associated product uncertainties based on multiple resampling of the training data set to produce a distribution of values for each pixel, and an example is shown for a coastal time series in the North Sea. The final output of the NN approach consists of a best-estimate image based on medians for each pixel, and a second image representing uncertainty based on standard deviation for each pixel, providing pixel-specific estimates of uncertainty in the final product.