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Remote estimation of in water constituents in coastal waters using neural networks

机译:使用神经网络对沿海水域中的水成分进行远程估算

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Remote estimations of oceanic constituents from optical reflectance spectra in coastal waters are challenging because of the complexity of the water composition as well as difficulties in estimation of water leaving radiance in several bands possibly due to inadequacy of current atmospheric correction schemes. This work focuses on development of a multiband inversion algorithm that combines remote sensing reflectance measurements at several wavelengths in the blue, green and red for retrievals of the absorption coefficients of phytoplankton, color dissolved organic matter and non-algal particulates at 443nm as well as the particulate backscatter coefficient at 443nm. The algorithm was developed, using neural networks (NN), and was designed to use as input measurements on ocean color bands matching those of the Visible Infrared Imaging Radiometer Suite (VIIRS). The NN is trained on a simulated data set generated through a bio-optical model for a broad range of typical coastal water parameters. The NN was evaluated using several statistical indicators, initially on the simulated data-set, as well as on field data from the NASA bio-Optical Marine Algorithm Data set, NOMAD, and data from our own field campaigns in the Chesapeake Bay which represent well the range of water optical properties as well as chlorophyll concentrations in coastal regions. The algorithm was also finally applied on a satellite - in situ databases that were assembled for the Chesapeake Bay region using MODIS and VIIRS satellite data. These databases were created using in-situ chlorophyll concentrations routinely measured in different locations throughout Chesapeake Bay and satellite reflectance overpass data that coexist in time with these in-situ measurements. NN application on this data-sets suggests that the blue (412 and 443nm) satellite bands are erroneous. The NN which was assessed for retrievals from VIIRS using only the 486, 551 and 671 bands showed that retrievals that omitted the 671 nm band was the most effective, possibly indicating an inaccuracy in the VIIRS 671 band that needs to be further investigated.
机译:由于水成分的复杂性以及可能由于当前大气校正方案的不足而难以估计几个频带中的水辐射率的困难,因此从沿海水域的光反射光谱进行海洋成分的远程估计是一项挑战。这项工作的重点是开发一种多波段反演算法,该算法结合了蓝色,绿色和红色几种波长下的遥感反射率测量结果,可检索浮游植物,彩色溶解有机物和非藻类颗粒在443nm处的吸收系数,以及443nm处的颗粒反向散射系数。该算法是使用神经网络(NN)开发的,旨在用作与可见红外成像辐射计套件(VIIRS)匹配的海洋色带的输入测量。 NN在通过生物光学模型生成的模拟数据集上进行训练,适用于广泛的典型沿海水域参数。使用几个统计指标对NN进行了评估,首先是在模拟数据集上,以及在NASA生物光学海洋算法数据集NOMAD上的实地数据以及切萨皮克湾我们自己的野外活动的数据中,这些数据很好地说明了这一点。沿海地区水的光学特性以及叶绿素浓度的范围。最终,该算法还应用于卫星-原位数据库,该数据库使用MODIS和VIIRS卫星数据为切萨皮克湾地区组装而成。这些数据库是使用在切萨皮克湾各处不同位置常规测量的原位叶绿素浓度以及与这些原位测量及时并存的卫星反射率天桥数据建立的。 NN在此数据集上的应用表明蓝色(412和443nm)卫星频段是错误的。对仅使用486、551和671波段从VIIRS进行检索进行评估的NN表明,省略671 nm波段的检索是最有效的,可能表明VIIRS 671波段存在误差,需要进一步研究。

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