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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data
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Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data

机译：使用ASTER数据检索东京湾的叶绿素a，悬浮固体和有色溶解有机物

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摘要

The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) has three bands in the visible and near-infrared (VNIR) with 15-m spatial resolution. The high spatial resolution has advantages for studying small aquatic areas, such as bay and lakes. Coastal areas are optically characterized by high concentrations of colored suspended matter, various phytoplankton pigments and colored dissolved organic matter (CDOM). The color ratio bio-optical algorithms often used for open sea are very difficult to apply in optically complex coastal water, since it is assumed that the constituents of seawater are mainly phytoplankton pigments. The Neural Network (NN) method, which is one of inverse modeling, has the potential to estimate chlorophyll a, suspended matter and CDOM from remotely sensed data. In the present investigation, we implemented the NN method in the analysis of ASTER data of Tokyo Bay, as a case study in the coastal waters in order to demonstrate the usefulness of remote sensing with high spatial resolution. After validation of the NN using simulated data sets and a field data set observed from a ship, estimation of the concentration ofTSS and Chl-a was reasonably accurate. However, in the case of CDOM, the result is not reliable. Disadvantages of the NN method are discussed in this paper.
机译：先进的星载热发射和反射辐射计（ASTER）在可见光和近红外（VNIR）中具有三个波段，空间分辨率为15 m。高空间分辨率对于研究较小的水域（如海湾和湖泊）具有优势。沿海地区的光学特征是高浓度的有色悬浮物，各种浮游植物色素和有色溶解有机物（CDOM）。由于假定海水的成分主要是浮游植物色素，因此通常用于公海的色比生物光学算法很难应用于光学复杂的沿海水域。神经网络（NN）方法是一种逆向建模方法，具有从遥感数据估算叶绿素a，悬浮物和CDOM的潜力。在本研究中，我们以近海案例为例，在东京湾ASTER数据分析中采用了NN方法，以证明高分辨率空间遥感的有用性。在使用模拟数据集和从船上观察到的现场数据集对NN进行验证之后，TSS和Chl-a浓度的估算是相当准确的。但是，对于CDOM，结果不可靠。本文讨论了神经网络方法的缺点。

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