基于BP神经网络算法的内蒙古岱海水体透明度遥感估算  

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

作　　者：刁瑞翔 青松[1] 越亚嫘 王芳 刘楠 郝艳玲 包玉海[1] DIAO Ruixiang;QING Song;YUE Yalei;WANG Fang;LIU Nan;HAO Yanling;BAO Yuhai(College of Geographical Science,Inner Mongolia Normal University,Hohhot 010022,China;College of Ecology and Environment,Inner Mongolia University,Hohhot 010021,China)

机构地区：[1]内蒙古师范大学地理科学学院,呼和浩特010022 [2]内蒙古大学生态与环境学院,呼和浩特010021

出　　处：《灌溉排水学报》2022年第8期114-121,共8页Journal of Irrigation and Drainage

基　　金：国家自然科学基金项目(41961057);内蒙古自治区高等学校青年科技英才支持计划项目(NJYT-17-B04);内蒙古自然科学基金项目(2019MS04013)。

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R~2=0.66,均方根误差RMSE=0.23m,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(R~2>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。【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】(1)The 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.(2)Compared 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.

关 键 词：遥感 透明度 BP神经网络 岱海 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]