Using Back Propagation Neural Network Algorithm and Remote Sensing to Estimate Lake Water Transparency

Abstract

【Objective】 Water transparency (depth of the secchi disk) is an important index to quantify quality of lake water but is difficult to measure in-situ at large scale. In this paper, we proposed a new method to estimate lake water transparency. 【Method】 The method was based on the back propagation (BP) neural network algorithm and remote sensing. Using measured water transparency and spectral data obtained from ground remote sensing and satellite remote sensing, a BP neural network model was established to inversely calculate water transparency. Using the Sentinel-2 MSI and Landsat-8 OLI satellite imageries, we applied the model to calculate water transparency of Daihai lake in inner Mongolia. 【Result】 ①The determination coefficient of the optimal model for the test set was R2=0.66, and its associated root mean square error and average absolute percentage error were RMSE=0.23 m and MAPE=21.56%, respectively. ②Compared with the traditional method, the BP neural network is more suitable for estimating lake water transparency with R2>0.81, RMSE<0.18 m and MAPE<14.97%. The inversely calculated water transparency agreed well with the ground-truth data. An independent verification of the method further proved its robustness. 【Conclusion】 The proposed method is accurate and reliable; it can be used to estimate lake water transparency at large scales.

Keywords

• remote sensing
• transparency
• bp neural network
• daihai lake