Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS.
Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS.
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DOI：
10.1364/ao.50.003168
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发表时间：
2011-07
期刊：
影响因子：
1.9
通讯作者：
I. Ioannou;A. Gilerson;B. Gross;F. Moshary;Samir A. Ahmed
中科院分区：
文献类型：
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作者：
I. Ioannou;A. Gilerson;B. Gross;F. Moshary;Samir A. Ahmed
从遥感多光谱反射率测量中获取水的固有光学特性是困难的，这是由于前向建模的复杂性和逆问题的固有非线性。在这种情况下，神经网络（NN）技术在反演复杂的非线性系统有很长的历史。我们采用的过程使用两个并行的NN。第一个神经网络是用来相关的遥感反射率在现有的MODIS可见光波长（678 nm的荧光通道除外）的吸收和后向散射系数在442 nm（叶绿素吸收峰）。第二个神经网络分离藻类和非藻类吸收成分，输出藻类与非藻类吸收的比率。综合训练的算法进行测试，使用美国宇航局生物光学海洋算法数据集（NOMAD），以及我们自己的现场数据集从切萨皮克湾和长岛湾，纽约。NOMAD的总吸收、藻类吸收和非藻类吸收的R²值分别为93.75%、90.67%和86.43%。对于我们的现场数据，其中包括吸收沃茨高达约6 m ²，R²为总测量吸收的91.87%。
Retrieving the inherent optical properties of water from remote sensing multispectral reflectance measurements is difficult due to both the complex nature of the forward modeling and the inherent nonlinearity of the inverse problem. In such cases, neural network (NN) techniques have a long history in inverting complex nonlinear systems. The process we adopt utilizes two NNs in parallel. The first NN is used to relate the remote sensing reflectance at available MODIS-visible wavelengths (except the 678 nm fluorescence channel) to the absorption and backscatter coefficients at 442 nm (peak of chlorophyll absorption). The second NN separates algal and nonalgal absorption components, outputting the ratio of algal-to-nonalgal absorption. The resulting synthetically trained algorithm is tested using both the NASA Bio-Optical Marine Algorithm Data Set (NOMAD), as well as our own field datasets from the Chesapeake Bay and Long Island Sound, New York. Very good agreement is obtained, with R² values of 93.75%, 90.67%, and 86.43% for the total, algal, and nonalgal absorption, respectively, for the NOMAD. For our field data, which cover absorbing waters up to about 6 m⁻¹, R² is 91.87% for the total measured absorption.