Water Quality Parameter COD Retrieved From Remote Sensing Based on Convolutional Neural Network Model
LI Ai-min1, FAN Meng2*, QIN Guang-duo2, WANG Hai-long2, XU You-cheng2
1. School of Geo-Science and Technology, Zhengzhou University, Zhengzhou 450001, China
2. School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China
Abstract：Chemical Oxygen Demand (COD) is a commonly used water quality indicator in water pollution monitoring. Traditional collection methods are time-consuming and labor-consuming, but the inversion of COD concentration by remote sensing method can quickly obtain the spatial distribution of COD concentration in the whole water area, which is of great significance for water pollution control and water environment protection.Using multi-spectral remote sensing data inversion of COD concentration is low precision. Because at present, a lot of the inversion models based on the Pearson correlation coefficient index selection experience method, modeling band for multi-spectral remote sensing data, its wide spectral bands, and band combination of quantity is limited, hard to find effective variables as modeling.In order to solve this problem, this study in Zhengzhou city, lake as an example, based on the Planet multi-spectral high-resolution remote sensing image and the remote sensing image preprocessing and hyperspectral data for analysis of water samples, using convolution neural network method to inversion of days lake COD concentration. At the same time, choose the single variable regression model, a multivariate regression model accuracy comparison test. The main conclusions are as follows:(1) Compared with the inversion method using Pearson correlation coefficient as the measurement standard to select different band combinations, convolutional neural network inversion has higher spatial inversion accuracy, with the determination coefficient of 0.89 and RMSE of 2.22 mg·L-1. This is because a convolutional neural network not only makes full use of the spectral characteristics of its remote sensing images. Moreover, the spatial information of the domain around the target pixel can be extracted. The abstract features of the deep layer of the image, as well as the"internal law" between the water quality parameter concentration and remote sensing data, can be learned, which can avoid the instability caused by the traditional modeling method to a certain extent. (2) Select the optimal convolutional neural network model to make the thematic map of the spatial distribution of COD concentration in Tiande Lake water quality. Tiande Lake has typical spectral characteristics of inland water, and its spatial distribution of COD concentration is generally characterized by high in the west, low in the east, low in the southeast inlet and high in the northeast outlet.The average value of concentration in the Tiande Lake region retrieved by the convolutional neural network is 23.96 mg·L-1, the standard deviation is 7.11 mg·L-1, and the coefficient of variation is 0.29, which is closer to the statistical value of actual sampling points.The results of COD retrieval based on a convolutional neural network model and multi-spectral image show that the convolutional neural network has good application potential in remote sensing COD retrieval of water quality parameters.
李爱民，范 猛，秦光铎，王海隆，许有成. 卷积神经网络模型的遥感反演水质参数COD[J]. 光谱学与光谱分析, 2023, 43(02): 651-656.
LI Ai-min, FAN Meng, QIN Guang-duo, WANG Hai-long, XU You-cheng. Water Quality Parameter COD Retrieved From Remote Sensing Based on Convolutional Neural Network Model. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 651-656.
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