基于BP神经网络算法的内蒙古岱海水体透明度遥感估算

刁瑞翔; 青松; 越亚嫘; 王芳; 刘楠; 郝艳玲; 包玉海

Cite this article:	刁瑞翔,青松,越亚嫘,等.基于BP神经网络算法的内蒙古岱海水体透明度遥感估算[J].灌溉排水学报,0,():-.
	DiaoRuixiang,QingSong,YueYalei,et al.基于BP神经网络算法的内蒙古岱海水体透明度遥感估算[J].灌溉排水学报,0,():-.

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Remote Sensing Estimation of Transparency of Daihai Lake in the Inner Mongolia Based on Back Propagation Neural Network Algorithm
DiaoRuixiang¹, QingSong¹, YueYalei¹, Wang Fang¹, Liu Nan¹, Hao Yanling², Bao Yuhai¹
1.College of Geography Science, Inner Mongolia Normal University;2.College of Ecology and Environment,Inner Mongolia University
Abstract:
【Objective】Lakes play an important role in the earth. The water transparency (Depth of The Secchi disk) is an important index to measure the water quality of lakes. It can directly reflect the clarity and turbidity of lakes, and provide effective information about the water for lake ecosystem.In this study, the BP neural network algorithm is used to estimated the transparency of Daihai Lake in Inner Mongolia by remote sensing.【Method】Based on the measured transparency and spectral data(reflectance obtained from ground remote sensing and satellite remote sensing) of Daihai Lake, Inner Mongolia, a BP neural network water transparency inversion model was established, and the model was applied to Sentinel-2 MSI and Landsat-8 OLI satellite data to invert the water transparency of Daihai Lake. 【Result】1) In the BP neural network model established in this paper, the test set determination coefficient of the optimal model OLI_insitu_220 model R2=0.66, the root mean square error RMSE=0.23 m, and the average absolute percentage error MAPE=21.56%. 2) Compared with the traditional calculation method, the BP neural network algorithm is more suitable for the estimation of the transparency of the Daihai Lake (R2>0.81, RMSE<0.18 m, MAPE<14.97%), and the inversion transparency value has a high consistency with the measured value. .【Conclusion】The independent verification of satellite and measured data further shows the effectiveness of the algorithm, which can objectively reflect the water transparency of the lake, and proves that the BP neural network algorithm is a feasible method for inversion of water transparency in inland lakes.
Key words: remote sensing; transparency; BP neural network; Daihai Lake