Abstract
The importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor (AWV) information and Landsat-8 (L8) remote sensing image into the input layer of radical basis function (RBF) neural network. All image information taken in RBF have been radiometrically calibrated. Except model(a), image data used in the other seven models were not atmospherically corrected. The eight models have different inputs and the same output (TLI). The models are as follows: (1) model(a), the inputs are seven single bands; (2) model(c), besides seven single bands (b1, b2, b3, b4, b5, b6, b7), we added the AWV parameter k1 to the inputs; (3) model(c1), the inputs are AWV difference coefficient k2 and the seven bands; (4) model(c2), the input layers include seven single bands, k1 and k2; (5) model(b), seven band ratios (b3/b5, b1/b2, b3/b7, b2/b5, b2/b7, b3/b6, and b3/b4) were used as input parameters; (6) model(b1), the inputs are k1 and seven band ratios; (7) model(b2), the inputs are k2 and seven band ratios; (8) model(b3), the inputs are k1, k2, and seven band ratios. We estimated models with root mean squared error (RMSE), model(a) > model(b3) > model(b1) > model(c2) > model(c) > model(b) > model(c1) > model(b2). RMSE of the eight models are 12.762, 11.274, 10.577, 8.904, 8.361, 6.396, 5.389, and 5.104, respectively. Model b2 and c1 are two best models in these experiments, which confirms both the seven single bands and band ratios with k2 are superior to other models. Results also corroborate that most lakes in Wuhan urban area are in mesotrophic and light eutrophic states.
Highlights
Available water resources, including rivers, reservoirs, lakes, coastal waters, and oceans, are emerging as a limiting factor, in quantity, and in quality, for human development and ecological stability
The advantages mentioned above indicate that Radical basis function (RBF) is more suitable than the back propagation (BP) neural network for water quality inversion [13,14]
The inversion results (model(b2) and model (c1)) show that waters of most lakes in Wuhan central urban area are mesotrophic and light eutrophic, and the trophic level index (TLI) decreased from center to lake branches, which indicate the impact of human activities on water quality
Summary
Available water resources, including rivers, reservoirs, lakes, coastal waters, and oceans, are emerging as a limiting factor, in quantity, and in quality, for human development and ecological stability. Water quality retrieval by remote sensing can quickly and punctually reflect spatial distribution of the water quality of specific lakes or areas [5,6], which makes up for the shortage of conventional monitoring and saves a lot of manpower, materials, and financial resources. Radical basis function (RBF) neural network has a good generalization ability and superiority in avoiding the tedious calculation of backpropagation. It can overcome problems where the calculation falls into the local minimum. The advantages mentioned above indicate that RBF is more suitable than the back propagation (BP) neural network for water quality inversion [13,14]
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