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首页> 外文期刊>Estuarine Coastal and Shelf Science >Estimation of total suspended matter concentration from MODIS data using a neural network model in the China eastern coastal zone
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Estimation of total suspended matter concentration from MODIS data using a neural network model in the China eastern coastal zone

机译：利用神经网络模型从MODIS数据估算总悬浮物浓度

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The objectives of this study are to evaluate the applicability of three existing retrieval models of total suspended matter (TSM) in the Bohai, Yellow, and East China Seas (BYES), and to propose a multilayer back propagation neural network (MBPNN) model. Based on the comparison of TSM predicted by these models with in situ measurements taken in the BYES, it was found that the MBPNN model produces a superior performance to the three existing models selected. Near-infrared band-based and shortwave infrared band-based combined model was used to remove the atmospheric effects from the moderate resolution imaging spectroradiometer (MODIS) data, and the TSM concentration was quantified from the MODIS data after atmospheric correction using the MBPNN model. It was found that the MBPNN model produces 41.4% uncertainty in deriving TSM concentration from the MODIS data. The MBPNN model-based climatological seasonal mean TSM concentration indicated that the highest TSM concentrations were found around the river estuaries, while the lowest values were found in the far offshore regions of the BYES. The MBPNN model-based monthly mean TSM concentration revealed that the highest TSM concentrations occurred in February, and the lowest in July. The diffuse attenuation coefficient at 490 am (K-d(490)) depended heavily on TSM concentration in the BYES. The significant relationship (R-2 = 0.72, p < 0.05) between K-d(490) and TSM concentration indicated that the BYES waters are TSM dominated types. (C) 2015 Elsevier Ltd. All rights reserved.
机译：这项研究的目的是评估三个现有的总悬浮物（TSM）检索模型在渤海，黄海和东海（BYES）中的适用性，并提出多层反向传播神经网络（MBPNN）模型。根据这些模型预测的TSM与BYES中进行的现场测量的比较，发现MBPNN模型比选择的三个现有模型具有更高的性能。使用基于近红外波段和基于短波红外波段的组合模型从中分辨率成像光谱仪（MODIS）数据中消除了大气影响，并使用MBPNN模型从大气校正后的MODIS数据中量化了TSM浓度。已经发现，MBPNN模型在从MODIS数据推导TSM浓度时产生41.4％的不确定性。基于MBPNN模型的气候季节平均TSM浓度表明，在河口周围发现了最高的TSM浓度，而在BYES的远海地区发现了最低的TSM浓度。基于MBPNN模型的月平均TSM浓度显示，最高TSM浓度发生在2月，而最低是7月。 490 am（K-d（490））处的扩散衰减系数在很大程度上取决于BYES中的TSM浓度。 K-d（490）与TSM浓度之间的显着关系（R-2 = 0.72，p <0.05）表明BYES水是TSM主导的类型。 （C）2015 Elsevier Ltd.保留所有权利。

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