航空高光谱遥感反演城市河网水质参数
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摘要
针对多光谱遥感对内陆城市河网水体水质参数反演精度不高的问题,基于航空和水表高光谱遥感数据,利用半经验法对COD_(cr)、BOD_5、TP和TN进行定量反演。对水质采样化验数据和水表反射率进行相关性分析,计算最佳波段组合分别为650nm/683nm、689nm/667nm、692nm/649nm、787nm/678nm;建立研究区COD_(cr)、BOD_5、TP和TN水质参数反演模型,水质参数决定系数R~2分别为0.74、0.70、0.69、0.71,均方根误差RMSE分别为2.79、1.92、0.02、0.16,拟合效果次序为COD_(cr)>TN> BOD_5>TP。利用验证样点对实验结果进行定量分析,反演效果次序为TN>COD_(cr)>BOD_5>TP,平均相对误差分别为2.6%、12.9%、16.7%、22%,基本与模型拟合效果次序一致,反演的水质浓度分布与城市河网的特点和实际情况相符,为流动性大、水质状况分布错综复杂的城市河网水质监测提供参考。
To resolve the problem of poor accuracy of inversion of water quality parameters of river network in inland cities while using multi-spectral remote sensing method,this paper carried out quantitative inversion of COD_(cr),BOD_5,TP and TN based on aeronautical and water surface hyperspectral remote sensing data using semi-empirical method.It analyzed the correlation between water quality sampling data and water surface reflectivity;calculated the optimal combination of band ratios which is 650 nm/683 nm,689 nm/667 nm,692 nm/649 nm,787 nm/678 nm,respectively;established an inversion model based on water quality parameters which are COD_(cr),BOD_5,TP and TN in the study area.The determination coefficients R~2 were0.74,0.70,0.69,0.71,and the root mean square errors(RMSE)were 2.79,1.92,0.02,and 0.16.The order of the fitting effect was COD_(cr)>TN>BOD_5>TP.The order of inversion effects was TN>COD_(cr)>BOD_5>TP based on quantitative analysis of experimental results using verification samples.The average relative error was 2.6%,12.9%,16.7%,22%,respectively,which was roughly consistent with the fitting effect order of the model.The inversion of the water quality concentration distribution was consistent with the characteristics and actual conditions of the urban river network.The above result of this study provided reference for monitoring water quality of urban river networks with large liquidity and complicated water quality distribution.
To resolve the problem of poor accuracy of inversion of water quality parameters of river network in inland cities while using multi-spectral remote sensing method,this paper carried out quantitative inversion of COD_(cr),BOD_5,TP and TN based on aeronautical and water surface hyperspectral remote sensing data using semi-empirical method.It analyzed the correlation between water quality sampling data and water surface reflectivity;calculated the optimal combination of band ratios which is 650 nm/683 nm,689 nm/667 nm,692 nm/649 nm,787 nm/678 nm,respectively;established an inversion model based on water quality parameters which are COD_(cr),BOD_5,TP and TN in the study area.The determination coefficients R~2 were0.74,0.70,0.69,0.71,and the root mean square errors(RMSE)were 2.79,1.92,0.02,and 0.16.The order of the fitting effect was COD_(cr)>TN>BOD_5>TP.The order of inversion effects was TN>COD_(cr)>BOD_5>TP based on quantitative analysis of experimental results using verification samples.The average relative error was 2.6%,12.9%,16.7%,22%,respectively,which was roughly consistent with the fitting effect order of the model.The inversion of the water quality concentration distribution was consistent with the characteristics and actual conditions of the urban river network.The above result of this study provided reference for monitoring water quality of urban river networks with large liquidity and complicated water quality distribution.
引文
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[20]刘飞,董大明,赵贤德,等.水体COD的光谱学在线测量方法:紫外和近红外光谱比较分析[J].光谱学与光谱分析,2017,37(9):2724-2729.
[2]LI L,LI L,SONG K,et al.An inversion model for deriving inherent optical properties of inland waters:establishment,validation and application[J].Remote Sensing of Environment,2013,135:150-166.
[3]XIE F,ZHANG C,SHAO H,et al.Retrieving inherent optical properties for turbid inland waters:an improved quasianalytical algorithm based on the linear spectral backscattering coefficient constraint[J].Proceedings of the SPIE,2017,255:102551Z.
[4]汪文琦,段辉,吴易超,等.基于QAA和GSM算法的水库水体吸收系数反演[J].甘肃科技,2017(20):55-56.
[5]王翔宇,汪西莉.结合灰色扩充的GA-BP神经网络模型在渭河水质遥感反演中的应用[J].遥感技术与应用,2010,25(2):251-256.
[6]何同弟,李见为,黄鸿.基于GA优选参数的RBF神经网络水质评价[J].计算机工程,2011,37(11):13-15.
[7]吕航,马蔚纯,周立国,等.淀山湖微量水质参数卫星高光谱遥感估算[J].复旦学报(自然科学版),2013,52(2):238-246.
[8]王丽艳,史小红,孙标,等.基于MODIS数据遥感反演呼伦湖水体COD浓度的研究[J].环境工程,2014,32(12):103-108.
[9]杨国范,阎孟冬,殷飞.清河水库叶绿素a浓度反演模型研究[J].遥感信息,2016,31(5):74-78.
[10]王代堃,国巧真.天津滨海新区地表水悬浮物浓度遥感反演研究[J].测绘科学,2016,41(5):67-71.
[11]ESSAM S E D,ZHANG Y.Estimation of both optical and nonoptical surface water quality parameters using Landsat 8OLI imagery and statistical techniques[J].Journal of Applied Remote Sensing,2017,11(11(4)):046008.
[12]王云霞.基于Landsat8的清河水库总氮、总磷反演研究[D].沈阳:沈阳农业大学,2017.
[13]赵文宇,姜亮亮,焦键,等.基于Landsat8数据的东道海子叶绿素a的遥感反演和监测研究[J].新疆环境保护,2017,39(3):28-35.
[14]种丹,李浩杰,范硕,等.基于MODIS数据的云南九大高原湖泊叶绿素a浓度反演[J].生态学杂志,2017,36(1):277-286.
[15]夏晓芸,解启蒙,杨国范,等.大伙房水库叶绿素a浓度反演模型研究[J].节水灌溉,2018(8):39-42.
[16]周媛,郝艳玲,刘东伟,等.基于Landsat8影像的黄河口悬浮物质量浓度遥感反演[J].海洋学研究,2018(1):38-48.
[17]周亚东,何报寅,寇杰锋,等.基于GF-1号遥感影像的武汉市及周边湖泊综合营养状态指数反演[J].长江流域资源与环境,2018,27(6):132-139.
[18]龚绍琦,黄家柱,李云梅,等.水体氮磷高光谱遥感实验研究初探[J].光谱学与光谱分析,2008,28(4):839-842.
[19]刘洪英,李庆利,刘锦高,等.基于便携式高光谱成像的BOD实时检测研究[J].影像科学与光化学,2011,29(1):63-69.
[20]刘飞,董大明,赵贤德,等.水体COD的光谱学在线测量方法:紫外和近红外光谱比较分析[J].光谱学与光谱分析,2017,37(9):2724-2729.