A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO 2 using MODIS data
A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO 2 using MODIS data
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DOI：
10.5194/amt-7-4023-2014
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发表时间：
2014-12
影响因子：
3.8
通讯作者：
A. Piscini;M. Picchiani;M. Chini;S. Corradini;L. Merucci;F. Frate;S. Stramondo
中科院分区：
文献类型：
--
作者：
A. Piscini;M. Picchiani;M. Chini;S. Corradini;L. Merucci;F. Frate;S. Stramondo
抽象的。在这项工作中，神经网络 (NN) 已用于基于中分辨率成像光谱辐射计 (MODIS) 多光谱测量来检索火山灰和二氧化硫 (SO 2 ) 参数。为了检索每个参数，建立了不同的神经网络，以试验不同的拓扑并评估其性能。利用神经网络快速处理大量新数据的能力，提出了一种新颖的应用方案，旨在提供喷发产物的完整表征。作为一个测试案例，我们考虑了 2010 年 5 月的埃亚菲亚德拉冰盖喷发。一组七个 MODIS 图像已用于训练和验证阶段。为了估计与火山喷发相关的参数，例如火山灰质量、有效半径、气溶胶光学深度和SO 2 柱状丰度，已经使用众所周知的算法的检索来训练神经网络。这些基于大气顶部的模拟辐射，并通过辐射传输模型进行估计。比较了具有不同输入数量的三种神经网络拓扑：（a）三个热红外 MODIS 通道，（b）所有多光谱 MODIS 通道和（c）通过应用于所有 MODIS 通道的修剪程序选择的通道。结果表明，神经网络方法能够很好地估计火山喷发参数，显示均方根误差 (RMSE) 低于目标数据标准差 (SD)。考虑所有 MODIS 通道构建的网络在专业化方面具有更好的性能，主要是在与训练图像时间接近的图像上，而输入较少的网络在应用于独立数据集时表现出更好的泛化性能。为了提高网络的泛化能力并选择最重要的 MODIS 通道，实施了剪枝算法。修剪结果表明，对灰分参数敏感的通道对应于热红外、可见光和中红外光谱范围。神经网络方法已被证明在解决火山灰和 SO 2 云参数估计的反演问题时是有效的，可提供快速可靠的检索，这是火山危机期间的重要要求。
Abstract. In this work neural networks (NNs) have been used for the retrieval of volcanic ash and sulfur dioxide (SO 2 ) parameters based on Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral measurements. Different neural networks were built in order for each parameter to be retrieved, for experimenting with different topologies and evaluating their performances. The neural networks' capabilities to process a large amount of new data in a very fast way have been exploited to propose a novel applicative scheme aimed at providing a complete characterization of eruptive products. As a test case, the May 2010 Eyjafjallajokull eruption has been considered. A set of seven MODIS images have been used for the training and validation phases. In order to estimate the parameters associated to the volcanic eruption, such as ash mass, effective radius, aerosol optical depth and SO 2 columnar abundance, the neural networks have been trained using the retrievals from well-known algorithms. These are based on simulated radiances at the top of the atmosphere and are estimated by radiative transfer models. Three neural network topologies with a different number of inputs have been compared: (a) three thermal infrared MODIS channels, (b) all multispectral MODIS channels and (c) the channels selected by a pruning procedure applied to all MODIS channels. Results show that the neural network approach is able to estimate the volcanic eruption parameters very well, showing a root mean square error (RMSE) below the target data standard deviation (SD). The network built considering all the MODIS channels gives a better performance in terms of specialization, mainly on images close in time to the training ones, while the networks with less inputs reveal a better generalization performance when applied to independent data sets. In order to increase the network's generalization capability and to select the most significant MODIS channels, a pruning algorithm has been implemented. The pruning outcomes revealed that channel sensitive to ash parameters correspond to the thermal infrared, visible and mid-infrared spectral ranges. The neural network approach has been proven to be effective when addressing the inversion problem for the estimation of volcanic ash and SO 2 cloud parameters, providing fast and reliable retrievals, important requirements during volcanic crises.