双极化SAR数据反演裸露地表土壤水分
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摘要
为了较高精度地获取大范围地表土壤水分,提出一种基于双极化合成孔径雷达数据的裸露地表土壤水分反演模型即非线性方程组,通过改进的粒子群算法求解非线性方程组从而得到土壤水分。首先通过AIEM模型数值模拟和回归分析,得到一种新的组合粗糙度,然后模拟分析得到土壤水分与雷达后向散射系数的关系,从而建立雷达后向散射系数与组合粗糙度、土壤水分的经验关系。利用ASAR C波段双极化雷达数据,基于经验关系和改进的粒子群算法即可实现土壤水分的反演。经过黑河流域实测土壤水分数据对模型进行验证,反演结果与实测数据具备良好的相关性(R~2=0.778 6)。与以往同一区域研究成果比较,文中的方法反演精度有所提高,更适用于裸露地表土壤水分反演。
In order to retrieval soil moisture accurately,a model which is non-linear equations is introduced to estimate the surface soil moisture by using dual-polarization advanced synthetic aperture radar data.In this paper,the improved particle swarm optimization(PSO)is used to solve the non-linear equations for obtaining soil moisture.Firstly,a database linked to SAR backscattering coefficients,surface roughness parameters,and soil moisture are built by AIEM(advanced integral equation model).Through regression analysis of the simulated database,a new roughness parameter and the relationship between soil moisture and backscattering coefficient is obtained.Then the empirical relationship between radar backscattering coefficient and combined roughness and soil moisture is established.By using ASAR C-band dual polarization radar data,the soil moisture can be retrieved based on the empirical relationship and improved particle swarm optimization algorithm.The model is validated by the measured data in Heihe River.It concludes that there is a good relationship between the estimated data and measured data.The correlation coefficient is as high as 0.778 6.Compared with the previous regional research result,the inversion accuracy of this paper has been improved,which is more suitable for bare surface soil moisture inversion.
In order to retrieval soil moisture accurately,a model which is non-linear equations is introduced to estimate the surface soil moisture by using dual-polarization advanced synthetic aperture radar data.In this paper,the improved particle swarm optimization(PSO)is used to solve the non-linear equations for obtaining soil moisture.Firstly,a database linked to SAR backscattering coefficients,surface roughness parameters,and soil moisture are built by AIEM(advanced integral equation model).Through regression analysis of the simulated database,a new roughness parameter and the relationship between soil moisture and backscattering coefficient is obtained.Then the empirical relationship between radar backscattering coefficient and combined roughness and soil moisture is established.By using ASAR C-band dual polarization radar data,the soil moisture can be retrieved based on the empirical relationship and improved particle swarm optimization algorithm.The model is validated by the measured data in Heihe River.It concludes that there is a good relationship between the estimated data and measured data.The correlation coefficient is as high as 0.778 6.Compared with the previous regional research result,the inversion accuracy of this paper has been improved,which is more suitable for bare surface soil moisture inversion.
引文
[1]BECK H E,DE Jeu R M,SCHELLEKENS J,et al.Improving Curve Number Based Storm Runoff Estimates Using Soil Moisture Proxies[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2010,2(4):250-259.
[2]BARRETT B W,DWYER E,WHELAN P.Soil moisture retrieval from active spaceborne microwave observations:an evaluation of current techniques[J].Remote Sensing,2009,1(3):210-242.
[3]马红章,柳钦火,闻建光,等.裸露地表土壤水分的L波段被动微波最佳角度反演算法[J].农业工程学报,2010,26(11):24-29.
[4]鲍艳松,刘利,孔令寅,等.基于ASAR的冬小麦不同生育期土壤湿度反演[J].农业工程学报,2010,26(9):224-232.
[5]田国良.土壤水分的遥感监测方法[J].遥感学报,1991(2):89-98.
[6]DOBSON M C,ULABY F T.Active Microwave Soil Moisture Research[J].IEEE Transactions on Geoscience&Remote Sensing,1986,24(1):23-36.
[7]BOLTEN J D,CROW W T,ZHAN X,et al.Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring[J].Selected Topics in Applied Earth Observations&Remote Sensing IEEE Journal of,2010,3(1):57-66.
[8]FUNG A K.Microwave Scattering and Emission Models and Their Applications[M].Artech House,1994.
[9]ZRIBI M,DECHAMBRE M,IEEE,et al.A new empirical model to inverse soil moisture and roughness using two radar configurations[M].2002:2223-2225.
[10]乌拉比.微波遥感[M].北京:科学出版社,1988.
[11]FUNG A K,LI Z,CHEN K S.Backscattering from a randomly rough dielectric surface[J].IEEE Transactions on Geoscience and Remote Sensing,1992,30(2):356-69.
[12]CHEN K S,TZONG-DAR W,MU-KING T,et al.Note on the multiple scattering in an IEM model[J].IEEE Transactions on Geoscience and Remote Sensing,2000,38(1):249-56.
[13]WU T D,CHEN K S.A reappraisal of the validity of the IEM model for backscattering from rough surfaces[J].IEEE Transactions on Geoscience&Remote Sensing,2004,42(4):743-753.
[14]WU T D,CHEN K S,SHI J,et al.A transition model for the reflection coefficient in surface scattering[J].IEEE Transactions on Geoscience&Remote Sensing,2001,39(9):2040-2050.
[15]BRUCKLER L,WITONO H,STENGEL P.Near surface soil moisture estimation from microwave measurements[J].Remote Sensing of Environment,1988,26(2):101-121.
[16]OH Y,SARABANDI K,ULABY F T.An empirical model and an inversion technique for radar scattering from bare soil surfaces[J].Geoscience&Remote Sensing IEEE Transactions on,1992,30(2):370-381.
[17]DUBOIS P C,VAN Zyl J,ENGMAN T.Measuring soil moisture with imaging radars[J].IEEE Transactions on Geoscience and Remote Sensing,1995,33(4):915-26.
[18]CHEN K S,YEN S K,HUANG W P.A simple model for retrieving bare soil moisture from radar-scattering coefficients[J].Remote Sensing of Environment,1995,54(2):121-126.
[19]OH Y,SARABANDI K,ULABY F T.Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces[J].Geoscience&Remote Sensing IEEE Transactions on,2002,40(6):1348-1355.
[20]SHI J,WANG J,HSU A Y,et al.Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data[J].Geoscience&Remote Sensing IEEE Transactions on,1997,35(5):1254-1266.
[21]余凡,赵英时.合成孔径雷达反演裸露地表土壤水分的新方法[J].武汉大学学报(信息科学版),2010,35(3):317-321.
[22]孔金玲,甄佩佩,李菁菁,等.基于新的组合粗糙度参数的微波遥感土壤水分反演[J].地理与地理信息科学,2016,32(3):5-9.
[23]李苗,臧淑英,那晓东,等.ENVISAT-ASAR数据产品介绍与数据处理[J].测绘与空间地理信息,2012,35(1):63-65.
[24]陈长忆,叶咏春.基于粒子群算法的非线性方程组求解[J].计算机应用与软件,2006,23(5).
[25]李民录.GDAL源码剖析[M].北京:人民邮电出版社,2012.
[26]陈晶,贾毅,余凡.双极化雷达反演裸露地表土壤水分[J].农业工程学报,2013,29(10):109-115.
[2]BARRETT B W,DWYER E,WHELAN P.Soil moisture retrieval from active spaceborne microwave observations:an evaluation of current techniques[J].Remote Sensing,2009,1(3):210-242.
[3]马红章,柳钦火,闻建光,等.裸露地表土壤水分的L波段被动微波最佳角度反演算法[J].农业工程学报,2010,26(11):24-29.
[4]鲍艳松,刘利,孔令寅,等.基于ASAR的冬小麦不同生育期土壤湿度反演[J].农业工程学报,2010,26(9):224-232.
[5]田国良.土壤水分的遥感监测方法[J].遥感学报,1991(2):89-98.
[6]DOBSON M C,ULABY F T.Active Microwave Soil Moisture Research[J].IEEE Transactions on Geoscience&Remote Sensing,1986,24(1):23-36.
[7]BOLTEN J D,CROW W T,ZHAN X,et al.Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring[J].Selected Topics in Applied Earth Observations&Remote Sensing IEEE Journal of,2010,3(1):57-66.
[8]FUNG A K.Microwave Scattering and Emission Models and Their Applications[M].Artech House,1994.
[9]ZRIBI M,DECHAMBRE M,IEEE,et al.A new empirical model to inverse soil moisture and roughness using two radar configurations[M].2002:2223-2225.
[10]乌拉比.微波遥感[M].北京:科学出版社,1988.
[11]FUNG A K,LI Z,CHEN K S.Backscattering from a randomly rough dielectric surface[J].IEEE Transactions on Geoscience and Remote Sensing,1992,30(2):356-69.
[12]CHEN K S,TZONG-DAR W,MU-KING T,et al.Note on the multiple scattering in an IEM model[J].IEEE Transactions on Geoscience and Remote Sensing,2000,38(1):249-56.
[13]WU T D,CHEN K S.A reappraisal of the validity of the IEM model for backscattering from rough surfaces[J].IEEE Transactions on Geoscience&Remote Sensing,2004,42(4):743-753.
[14]WU T D,CHEN K S,SHI J,et al.A transition model for the reflection coefficient in surface scattering[J].IEEE Transactions on Geoscience&Remote Sensing,2001,39(9):2040-2050.
[15]BRUCKLER L,WITONO H,STENGEL P.Near surface soil moisture estimation from microwave measurements[J].Remote Sensing of Environment,1988,26(2):101-121.
[16]OH Y,SARABANDI K,ULABY F T.An empirical model and an inversion technique for radar scattering from bare soil surfaces[J].Geoscience&Remote Sensing IEEE Transactions on,1992,30(2):370-381.
[17]DUBOIS P C,VAN Zyl J,ENGMAN T.Measuring soil moisture with imaging radars[J].IEEE Transactions on Geoscience and Remote Sensing,1995,33(4):915-26.
[18]CHEN K S,YEN S K,HUANG W P.A simple model for retrieving bare soil moisture from radar-scattering coefficients[J].Remote Sensing of Environment,1995,54(2):121-126.
[19]OH Y,SARABANDI K,ULABY F T.Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces[J].Geoscience&Remote Sensing IEEE Transactions on,2002,40(6):1348-1355.
[20]SHI J,WANG J,HSU A Y,et al.Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data[J].Geoscience&Remote Sensing IEEE Transactions on,1997,35(5):1254-1266.
[21]余凡,赵英时.合成孔径雷达反演裸露地表土壤水分的新方法[J].武汉大学学报(信息科学版),2010,35(3):317-321.
[22]孔金玲,甄佩佩,李菁菁,等.基于新的组合粗糙度参数的微波遥感土壤水分反演[J].地理与地理信息科学,2016,32(3):5-9.
[23]李苗,臧淑英,那晓东,等.ENVISAT-ASAR数据产品介绍与数据处理[J].测绘与空间地理信息,2012,35(1):63-65.
[24]陈长忆,叶咏春.基于粒子群算法的非线性方程组求解[J].计算机应用与软件,2006,23(5).
[25]李民录.GDAL源码剖析[M].北京:人民邮电出版社,2012.
[26]陈晶,贾毅,余凡.双极化雷达反演裸露地表土壤水分[J].农业工程学报,2013,29(10):109-115.