| 引用本文: | 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,2022,41(8):114-121. |
| DIAO Ruixiang,QING Song,YUE Yalei,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,2022,41(8):114-121. |
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| 摘要: |
| 【目的】利用BP神经网络算法对内蒙古岱海水体的透明度进行遥感估算。【方法】基于内蒙古岱海实测透明度和光谱数据（地面遥感反射率和卫星遥感得到的反射率），建立BP神经网络水体透明度反演模型，并将此模型应用于Sentinel-2 MSI和Landsat-8 OLI卫星数据，遥感反演岱海水体透明度。【结果】①本文建立的BP神经网络模型中，最优模型OLI_insitu_220模型的测试集决定系数R2=0.66，均方根误差RMSE=0.23 m，平均绝对百分比误差MAPE=21.56%。②与传统计算方法相比，BP神经网络算法更适合岱海水体透明度的估算（R2>0.81，RMSE<0.18 m，MAPE<14.97%），反演透明度值与实测值有较高的一致性。【结论】实测与卫星匹配的独立验证进一步显示该算法的有效性，能够客观地反映湖泊水体透明度状况，证明了BP神经网络算法运用在内陆湖泊反演水体透明度的可行性。 |
| 关键词: 遥感；透明度；BP神经网络；岱海 |
| DOI：10.13522/j.cnki.ggps.2022021 |
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| Using Back Propagation Neural Network Algorithm and Remote Sensing to Estimate Lake Water Transparency |
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DIAO Ruixiang, QING Song, YUE Yalei, WANG Fang, LIU Nan, HAO Yanling, BAO Yuhai
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1.College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China;
2.College of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
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| 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. |
| Key words: remote sensing; transparency; BP neural network; Daihai Lake |
