西北寒旱灌区裸露地表粗糙度SAR反演建模方法研究
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摘要
快速获取大范围土壤地表粗糙度的空间分布是一个急需解决的科学难题。以快速获取内蒙古河套灌区解放闸灌域土壤地表粗糙度为目的,研究了RADARSAT-2雷达影像数据的地表粗糙度信息提取技术,通过剖面板法实测地表粗糙度数据。利用BP(Back Propagation)人工神经网络和LMBP(Levenberg-Marquardt Back Propagation)人工神经网络模型2种方法建立了土壤地表粗糙度的定量反演模型,并对模型进行验证。结果表明,LMBP模型的反演效果优于BP模型,其决定系数R2分别为0.888 3、0.689 2。建立的雷达后向散射系数反演土壤地表粗糙度的人工智能模型,能够在一定程度上满足快速获取土壤地表粗糙度的需要,为微波遥感监测土壤墒情及土壤盐渍化提供重要基础参数。
Quick calculation of spatial distribution of soil surface roughness is important both practically and scientifically. In this paper we investigated the feasibility of using the radar image of RADARSAT-2 to inversely calculate the surface roughness of Jiefangzha Irrigation area in Hetao Irrigation District of Inner Mongolia. The surface roughness in the radar image was calculated by the sectional plate method. We used both back propagation(BP) artificial neural network and the Levenberg-Marquardt back propagation(LMBP) artificial neural network to calculate and verify the inverse model for quantifying the surface roughness. The results showed that the LMBP model was superior to the BP model, witha R-squared coefficient of 0.888 3 and 0.689 2 respectively. Calculating soil surface roughness inversely using artificial intelligent model and the radar backscatter coefficient is quick, providing important basic parameters for using microwave remote sensing to monitorsoil moisture and soil salinization.
Quick calculation of spatial distribution of soil surface roughness is important both practically and scientifically. In this paper we investigated the feasibility of using the radar image of RADARSAT-2 to inversely calculate the surface roughness of Jiefangzha Irrigation area in Hetao Irrigation District of Inner Mongolia. The surface roughness in the radar image was calculated by the sectional plate method. We used both back propagation(BP) artificial neural network and the Levenberg-Marquardt back propagation(LMBP) artificial neural network to calculate and verify the inverse model for quantifying the surface roughness. The results showed that the LMBP model was superior to the BP model, witha R-squared coefficient of 0.888 3 and 0.689 2 respectively. Calculating soil surface roughness inversely using artificial intelligent model and the radar backscatter coefficient is quick, providing important basic parameters for using microwave remote sensing to monitorsoil moisture and soil salinization.
引文
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[3]GORRAB A,ZRIBI M,BAGHDADI N,et al.Retrieval of both soil moisture and texture using Terra SAR-X images[J].International Journal of Remote Sensing,2015,7:10 098-10 116.
[4]JAGDHUBER T,HAJNSEK I,PAPATHANASSIOU K P.An iterative generalized hybrid decomposition for soil moisture retrieval under vegetation cover using fully polarimetric SAR[J].IEEE Journal of Selected Topics in Applied Earth Observations and.Remote Sensing,2015,8:3 911-3 922.
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[6]曾珍,杨本勇,范建容,等.基于地表粗糙度地学意义的地表真实面积计算[J].遥感技术与应用,2014,29(5):846-852.
[7]FUNG A K,CHEN K S.An update on the IEM surface backscattering model[J].IEEE Geoscience and Remote Sensing Letters,2004,2(1):75-77.
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[9]BAGHDADI N,HOLAH N,ZRIBI M.Soil Moisture Estimation Using Multiincidence and Multipolarization ASAR Data[J].International Journal of Remote Sensing,2006,27(10):1 907-1 920.
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[12]OH Y,SARABANDI K,ULABY F T.Semi-empirical Model of the Ensemble Averaged Differential Muller Matrix for Microwave Backscattering from Bare Soil Surfaces[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(6):1 348-1 355.
[13]孔金玲,甄佩佩等.基于新的组合粗糙度参数的土壤水分微波遥感反演[J].地理与地理信息科学,2016,5(3):34-38.
[14]廖静娟,庞自振.多极化SAR数据反演额济纳冲积扇地表参数[J].地球信息科学学报,2009,11(1):77-83,131.
[15]林国青,郭华,张东露.基于SAR数据的干旱区冲积扇地表粗糙度反演[J].遥感技术与应用,2013,8(4):660-665.
[16]BAGHADAI N,CHAAYA J A,ZRIBI M.Semi-empirical calibration of the integral equation model for SAR data in C-band and cross polarization using radar images and field measure-ments[J].IEEE Geoscience and Remote Sensing Letters,2011,8(1):14-18.
[17]杨虎,郭华东,王长林,等.基于神经网络方法的极化雷达地表参数反演[J].遥感学报,2002,6(6):451-455.
[18]赵旭峰.LMBP神经网络算法改进及其应用[D].北京:中国科学技术大学,2014.
[19]SHI J C,DU Y,DU J Y,et al.Progresses on microwave remote sensing of land surface parameters[J].Science in China Series B,Chemistry,life sciences&Earth Sciences,2012,5(5):1 052-1 078.
[20]RADARSAT-2卫星介绍[J].中国科学院对地观测与数字地球科学中心用户简讯,2008,(73).
[21]马腾,王耀强,李瑞平,等.基于微波遥感极化目标分解的土地覆盖/土地利用分类[J].农业工程学报,2015,31(2):259-265.
[22]MOZOS J A,CASALI J,AUDIACANA M G,et al.Correlation be-tween ground measured soil moisture and RADARSAT-1 derived backscattering coefficient over an agricultural catchment of Navarre(north of Spain)[J].Bioprocess and Biosystems Engineering,2005,92(1):119-133.
[23]MORENO R G,LVAREZ M D,ALONSO A T,et al.Tillage and soil typeeffects on soil surface roughness at semiarid climatic conditions[J].Soil and Tillage Research,2008,98:35-44.
[24]SHI J,CHEN K S,VAN ZYL J,et al.Estimate relative soil moisture change with multi-temporal L band radar measurements[C]//IEEE International Geoscience and Remote Sensing Symposium,2002,1:647-649.
[25]ISOK O H.Quantitative and retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(4):596-601.
[26]ZRIBI M,HE′GARAT-MASCLE S L,OTTL′E,et al.Surface soil moisture estimation from the synergistic use of the(multi-incidence and multi-reso lution)active microwave ERS Wind Scatterometer and SAR data[J].Remote Sensing of Environment,2003,86(1):30-41.
[2]LIEVENS H,VERNIEUWE H,A LVAREZ-MOZOS J,et al.Error in SAR derived soil moisture due to roughness parameterization:An analysis based on synthetical surface profiles[J].Sensors,2009,9:1 067-1 093.
[3]GORRAB A,ZRIBI M,BAGHDADI N,et al.Retrieval of both soil moisture and texture using Terra SAR-X images[J].International Journal of Remote Sensing,2015,7:10 098-10 116.
[4]JAGDHUBER T,HAJNSEK I,PAPATHANASSIOU K P.An iterative generalized hybrid decomposition for soil moisture retrieval under vegetation cover using fully polarimetric SAR[J].IEEE Journal of Selected Topics in Applied Earth Observations and.Remote Sensing,2015,8:3 911-3 922.
[5]NOTARNICOL A C.A bayesian change detection approach for retrieval of soil moisture variations under different roughness conditions[J].IEEE Geoscience and Remote Sensing Letters,2014,11:414-418.
[6]曾珍,杨本勇,范建容,等.基于地表粗糙度地学意义的地表真实面积计算[J].遥感技术与应用,2014,29(5):846-852.
[7]FUNG A K,CHEN K S.An update on the IEM surface backscattering model[J].IEEE Geoscience and Remote Sensing Letters,2004,2(1):75-77.
[8]BAGHDADI N,GHERBOUDJ I,ZRIBI M,et al.Semiempirical Calibration of the IEM Backscttering Model Using Radar Imagesand Moisture and Roughness Field Measurements[J].International Journal of Remote Sensing,2004,25(18):3 593-3 623.
[9]BAGHDADI N,HOLAH N,ZRIBI M.Soil Moisture Estimation Using Multiincidence and Multipolarization ASAR Data[J].International Journal of Remote Sensing,2006,27(10):1 907-1 920.
[10]DHARSSI I,BOVIS K J,MACPHERSON,B,et al.Operational assimilation of ASCAT surface soil wetness at he Met Office[J].Hydrology and Earth System Sciences Discussions,2011,15(8):2 729-2 746.
[11]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):915-926.
[12]OH Y,SARABANDI K,ULABY F T.Semi-empirical Model of the Ensemble Averaged Differential Muller Matrix for Microwave Backscattering from Bare Soil Surfaces[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(6):1 348-1 355.
[13]孔金玲,甄佩佩等.基于新的组合粗糙度参数的土壤水分微波遥感反演[J].地理与地理信息科学,2016,5(3):34-38.
[14]廖静娟,庞自振.多极化SAR数据反演额济纳冲积扇地表参数[J].地球信息科学学报,2009,11(1):77-83,131.
[15]林国青,郭华,张东露.基于SAR数据的干旱区冲积扇地表粗糙度反演[J].遥感技术与应用,2013,8(4):660-665.
[16]BAGHADAI N,CHAAYA J A,ZRIBI M.Semi-empirical calibration of the integral equation model for SAR data in C-band and cross polarization using radar images and field measure-ments[J].IEEE Geoscience and Remote Sensing Letters,2011,8(1):14-18.
[17]杨虎,郭华东,王长林,等.基于神经网络方法的极化雷达地表参数反演[J].遥感学报,2002,6(6):451-455.
[18]赵旭峰.LMBP神经网络算法改进及其应用[D].北京:中国科学技术大学,2014.
[19]SHI J C,DU Y,DU J Y,et al.Progresses on microwave remote sensing of land surface parameters[J].Science in China Series B,Chemistry,life sciences&Earth Sciences,2012,5(5):1 052-1 078.
[20]RADARSAT-2卫星介绍[J].中国科学院对地观测与数字地球科学中心用户简讯,2008,(73).
[21]马腾,王耀强,李瑞平,等.基于微波遥感极化目标分解的土地覆盖/土地利用分类[J].农业工程学报,2015,31(2):259-265.
[22]MOZOS J A,CASALI J,AUDIACANA M G,et al.Correlation be-tween ground measured soil moisture and RADARSAT-1 derived backscattering coefficient over an agricultural catchment of Navarre(north of Spain)[J].Bioprocess and Biosystems Engineering,2005,92(1):119-133.
[23]MORENO R G,LVAREZ M D,ALONSO A T,et al.Tillage and soil typeeffects on soil surface roughness at semiarid climatic conditions[J].Soil and Tillage Research,2008,98:35-44.
[24]SHI J,CHEN K S,VAN ZYL J,et al.Estimate relative soil moisture change with multi-temporal L band radar measurements[C]//IEEE International Geoscience and Remote Sensing Symposium,2002,1:647-649.
[25]ISOK O H.Quantitative and retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(4):596-601.
[26]ZRIBI M,HE′GARAT-MASCLE S L,OTTL′E,et al.Surface soil moisture estimation from the synergistic use of the(multi-incidence and multi-reso lution)active microwave ERS Wind Scatterometer and SAR data[J].Remote Sensing of Environment,2003,86(1):30-41.