多星融合的土壤湿度滚动式估算模型
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摘要
全球定位系统干涉反射测量(GPS-IR)是一种新的遥感技术,可用于估算近地表土壤水分含量。考虑到多卫星融合的优势和土壤湿度的时空尺度性,提出一种基于多星融合的土壤湿度最小二乘支持向量机(LS-SVM)滚动式估算模型。首先通过低阶多项式拟合分离GPS卫星直射和反射信号,进而建立反射信号正弦拟合模型,获取相对延迟相位。最后,通过线性回归模型有效分析和选取多卫星相对延迟相位,并建立基于多星融合的最小二乘支持向量机模型进行滚动式估算土壤湿度。以美国板块边界观测计划PBO提供的监测数据为例,对比分析利用单颗、多颗GPS卫星进行土壤湿度滚动式估算的可行性和有效性。经理论分析和两个测站实验表明:该模型充分发挥了LS-SVM的优势,有效综合了各卫星的性能,改善了采用单颗卫星进行土壤湿度估算时,其结果极易出现异常跳变的现象;模型只需较少的建模数据,采用滚动式能实现较长时间的估算,估算误差较为稳定;模型所估算的结果与土壤湿度实测值之间的相关系数R2以及均方根误差分别为0.942和0.962、0.072和0.032,相对于部分单一卫星至少提高了18.18%。因此,土壤湿度问题可作为非线性事件处理,采用多卫星融合估算是可行和有效的。
Soil moisture content is an important parameter in hydrology, meteorology, and agriculture and is vital for meteorological forecast, flood disaster, and water resource cycle. Global positioning system interferometric reflectometry is a new remote sensing technique with low cost and high efficiency and resolution. This technique can be used to estimate near-surface soil moisture for the area surrounding the antenna from signal-to-noise ratio(SNR) data.In this study, a non-linear sliding estimation method of soil moisture based on multi-satellite fusion was proposed in consideration of the advantages of multi-satellite convergence and the time and space scale of soil moisture. First, the direct and reflection signals of GPS satellites were separated by means of low-order polynomial fitting. Then, the sinusoidal fitting model of reflection signals was established,and the relative delay phase of the SNR interferogram was obtained. Finally, a linear regression model was used to analyze and select the phase of SNR interferogram, and the sliding estimation method of the soil moisture using the least square support vector machine based on multi-system fusion was established. On the basis of the monitoring data provided by US Plate Boundary Observations Project, the feasibility and effectiveness of using single and multiple GPS satellites for sliding estimation of soil moisture were compared and analyzed.Results of the two types of data showed that the linear regression equation could efficiently describe the relationship between relative retardation phase and soil moisture and effectively select GPS satellites by setting the threshold range of correlation coefficient. The optimal parameters of least square support vector machine were selected by grid search method. Over-fitting did not occur in the process of multistar fusion inversion, and the advantage of non-linear weight determination was fully exerted. When a single satellite was used for soil moisture inversion, the variation law of soil moisture was inaccurately obtained, and the error of inversion error fluctuated greatly, thereby resulting in the jump phenomenon. When the rolling multi-star fusion inversion model was used, the fluctuation of inversion error was relatively close, thereby effectively suppressing the transition phenomenon. The correlation coefficients between the estimated results and the measured values of soil moisture were 0.942 and 0.962, respectively. The root mean square errors were 0.072 and 0.032, which were at least18.18% higher than those of some single satellites.The theoretical analysis and experiment showed that this method had fully utilized the advantages of least square support vector machine and effectively integrated the performance of each satellite. Overall, the method required less modeling data, the sliding mode could achieve long time estimation, and the estimation error was relatively stable. This method not only ensured the stability of the local error in the estimation process but also effectively restrained the abnormal jump phenomenon easily when single satellite was estimated. The inversion process was not easily affected by a single satellite. Therefore, the estimation of soil humidity can be treated as non-linear event, and multi-system fusion estimation is feasible and effective.
Soil moisture content is an important parameter in hydrology, meteorology, and agriculture and is vital for meteorological forecast, flood disaster, and water resource cycle. Global positioning system interferometric reflectometry is a new remote sensing technique with low cost and high efficiency and resolution. This technique can be used to estimate near-surface soil moisture for the area surrounding the antenna from signal-to-noise ratio(SNR) data.In this study, a non-linear sliding estimation method of soil moisture based on multi-satellite fusion was proposed in consideration of the advantages of multi-satellite convergence and the time and space scale of soil moisture. First, the direct and reflection signals of GPS satellites were separated by means of low-order polynomial fitting. Then, the sinusoidal fitting model of reflection signals was established,and the relative delay phase of the SNR interferogram was obtained. Finally, a linear regression model was used to analyze and select the phase of SNR interferogram, and the sliding estimation method of the soil moisture using the least square support vector machine based on multi-system fusion was established. On the basis of the monitoring data provided by US Plate Boundary Observations Project, the feasibility and effectiveness of using single and multiple GPS satellites for sliding estimation of soil moisture were compared and analyzed.Results of the two types of data showed that the linear regression equation could efficiently describe the relationship between relative retardation phase and soil moisture and effectively select GPS satellites by setting the threshold range of correlation coefficient. The optimal parameters of least square support vector machine were selected by grid search method. Over-fitting did not occur in the process of multistar fusion inversion, and the advantage of non-linear weight determination was fully exerted. When a single satellite was used for soil moisture inversion, the variation law of soil moisture was inaccurately obtained, and the error of inversion error fluctuated greatly, thereby resulting in the jump phenomenon. When the rolling multi-star fusion inversion model was used, the fluctuation of inversion error was relatively close, thereby effectively suppressing the transition phenomenon. The correlation coefficients between the estimated results and the measured values of soil moisture were 0.942 and 0.962, respectively. The root mean square errors were 0.072 and 0.032, which were at least18.18% higher than those of some single satellites.The theoretical analysis and experiment showed that this method had fully utilized the advantages of least square support vector machine and effectively integrated the performance of each satellite. Overall, the method required less modeling data, the sliding mode could achieve long time estimation, and the estimation error was relatively stable. This method not only ensured the stability of the local error in the estimation process but also effectively restrained the abnormal jump phenomenon easily when single satellite was estimated. The inversion process was not easily affected by a single satellite. Therefore, the estimation of soil humidity can be treated as non-linear event, and multi-system fusion estimation is feasible and effective.
引文
Ao M S,Zhu J J,Hu Y J,Zeng Y and Liu Y D.2015.Comparative experiments on soil moisture monitoring with GPS SNR observations.Geomatics and Information Science of Wuhan University,40(1):117-120,127(敖敏思,朱建军,胡友健,曾云,刘亚东.2015.利用SNR观测值进行GPS土壤湿度监测.武汉大学学报(信息科学版),40(1):117-120,127)[DOI:10.13203/j.whugis20130170]
Bilich A and Larson K M.2007.Mapping the GPS multipath environment using the signal‐to‐noise ratio(SNR).Radio Science,42(6):RS6003[DOI:10.1029/2007RS003652]
Bishop C M.2006.Pattern Recognition and Machine Learning.New York:Springer
Cardellach E,Fabra F,Rius A,Pettinato S and D’Addio S.2012.Characterization of dry-snow sub-structure using GNSS reflected signals.Remote Sensing of Environment,124:122-134[DOI:10.1016/j.rse.2012.05.012]
Chew C,Small E E and Larson K M.2016.An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil.GPS Solutions,20(3):525-537[DOI:10.1007/s10291-015-0462-4]
Chew C C,Small E E,Larson K M and Zavorotny V U.2015.Vegetation sensing using GPS-interferometric reflectometry:theoretical effects of canopy parameters on signal-to-noise ratio data.IEEETransactions on Geoscience and Remote Sensing,53(5):2755-2764[DOI:10.1109/TGRS.2014.2364513]
Dobson M C,Ulaby F T,Hallikainen M T and El-Rayes M A.1985.Microwave dielectric behavior of wet soil-Part II:dielectric mixing models.IEEE Transactions on Geoscience and Remote Sensing,GE-23(1):35-46[DOI:10.1109/TGRS.1985.289498]
Ferrazzoli P,Guerriero L,Pierdicca N and Rahmoune R.2011.Forest biomass monitoring with GNSS-R:theoretical simulations.Advances in Space Research,47(10):1823-1832[DOI:10.1016/j.asr.2010.04.025]
Hallikainen M T,Ulaby F T,Dobson M C,El-Rayes M A and Wu L K.1985.Microwave dielectric behavior of wet soil-part 1:empirical models and experimental observations.IEEE Transactions on Geoscience and Remote Sensing,GE-23(1):25-34[DOI:10.1109/TGRS.1985.289497]
Huang XL,Shi L and Suykens J A K.2014.Asymmetric least squares support vector machine classifiers.Computational Statistics and Data Analysis,70:395-405[DOI:10.1016/j.csda.2013.09.015]
Jin S G,Zhang Q Y and Qian X D.2017.New progress and application prospects of Global Navigation Satellite System Reflectometry(GNSS+R).Acta Geodaetica et Cartographica Sinica,46(10):1389-1398(金双根,张勤耘,钱晓东.2017.全球导航卫星系统反射测量(GNSS+R)最新进展与应用前景.测绘学报,46(10):1389-1398)[DOI:10.11947/j.AGCS.2017.20170282]
Johnson M L,Correia J J,Yphantis D A and Halvorson H R.1981.Analysis of data from the analytical ultracentrifuge by nonlinear least-squares techniques.Biophysical Journal,36(3):575-588[DOI:10.1016/S0006-3495(81)84753-4]
Larson K M,Braun J J,Small E E,Zavorotny V U,Gutmann E D and Bilich A L.2010.GPS multipath and its relation to near-surface soil moisture content.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,3(1):91-99[DOI:10.1109/JSTARS.2009.2033612]
Larson K M and Nievinski F G.2013.GPS snow sensing:results from the Earth Scope Plate Boundary Observatory.GPS Solutions,17(1):41-52[DOI:10.1007/s10291-012-0259-7]
Le Hégarat-Mascle S,Zribi M,Alem F,Weisse A and Loumagne C.2002.Soil moisture estimation from ERS/SAR data:toward an operational methodology.IEEE Transactions on Geoscience and Remote Sensing,40(12):2647-2658[DOI:10.1109/TGRS.2002.806994]
Martin-Neira M.1993.A passive reflectometry and interferometry system(PARIS):application to ocean altimetry.ESA Journal,17(4):331-355
Njoku E G and Entekhabi D.1996.Passive microwave remote sensing of soil moisture.Journal of Hydrology,184(1/2):101-129[DOI:10.1016/0022-1694(95)02970-2]
Peng X F,Wan W,Li F and Chen X W.2017.The suitability analysis of soil moisture retrieval using GNSS-R technology.Journal of Remote Sensing,21(3):341-350(彭学峰,万玮,李飞,陈秀万.2017.GNSS-R土壤水分遥感的适宜性分析.遥感学报,21(3):341-350)[DOI:10.11834/jrs.20176198]
Powell M J D and Yuan Y.1990.A trust region algorithm for equality constrained optimization.Mathematical Programming,49(1/3):189-211[DOI:10.1007/BF01588787]
Rodriguez-Alvarez N,Bosch-Lluis X,Camps A,Vall-Llossera M,Valencia E,Marchan-Hernandez J F and Ramos-Perez I.2009.Soil moisture retrieval using GNSS-R techniques:Experimental results over a bare soil field.IEEE Transactions on Geoscience and Remote Sensing,47(11):3616-3624[DOI:10.1109/TGRS.2009.2030672]
Sabater J M,Rüdiger C,Calvet J C,Fritz N,Jarlan L and Kerr Y.2008.Joint assimilation of surface soil moisture and LAI observations into a land surface model.Agricultural and Forest Meteorology,148(8/9):1362-1373[DOI:10.1016/j.agrformet.2008.04.003]
Suykens J A K and Vandewalle J.1999.Least squares support vector machine classifiers.Neural Processing Letters,9(3):293-300[DOI:10.1023/A:101862860]
Tsang L,Kong J A and Shin R T.1985.Theory of Microwave Remote Sensing.London:Wiley-Interscience
Wu X R,Li Y and Xu J.2012.Theoretical study on GNSS-R vegetation biomass//Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium.Munich,Germany:IEEE:6380-6383[DOI:10.1109/IGARSS.2012.6352719
Xie S F,Liang Y J,Zheng Z T and Liu H F.2017.Combined forecasting method of landslide deformation based on MEEMD,approximate entropy,and WLS-SVM.ISPRS International Journal of Geo-Information,6(1):5[DOI:10.3390/ijgi6010005]
Yan S H,Gong J Y,Zhang X X and Li D X.2011.Ground based GNSS-R observations for soil moisture.Chinese Journal of Geophysics,54(11):2735-2744(严颂华,龚健雅,张训械,李冻秀.2011.GNSS-R测量地表土壤湿度的地基实验.地球物理学报,54(11):2735-2744)[DOI:10.3969/j.issn.0001-5733.2011.11.003]
Yang L X,Yang S Y,Li S J,Zhang R,Liu F and Jiao L C.2015.Coupled compressed sensing inspired sparse spatial-spectral LSS-VM for hyperspectral image classification.Knowledge-Based Systems,79:80-89[DOI:10.1016/j.knosys.2015.01.006]
Ye X F.2016.Research on Processing Method of GNSS Station Environment Error and Its Application Using SNR Data.Wuhan:China University of Geosciences(Wuhan).(叶险峰.2016.基于GNSS信噪比数据的测站环境误差处理方法及其应用研究.武汉:中国地质大学(武汉))
Zavorotny V U,Larson K M,Braun J J,Small E E,Gutman E D and Bilich A L.2010.A physical model for GPS multipath caused by land reflections:toward bare soil moisture retrievals.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,3(1):100-110[DOI:10.1109/JSTARS.2009.2033608]
Zhang S C,Dai K Y,Liu K,Hou X W and Zhao Y H.2016.Research of GPS-MR on snow depth monitoring.Progress in Geophysics,31(4):1879-1884(张双成,戴凯阳,刘凯,侯晓伟,赵迎辉.2016.GPS-MR技术用于降雪厚度监测研究.地球物理学进展,31(4):1879-1884)[DOI:10.6038/pg20160461]
Bilich A and Larson K M.2007.Mapping the GPS multipath environment using the signal‐to‐noise ratio(SNR).Radio Science,42(6):RS6003[DOI:10.1029/2007RS003652]
Bishop C M.2006.Pattern Recognition and Machine Learning.New York:Springer
Cardellach E,Fabra F,Rius A,Pettinato S and D’Addio S.2012.Characterization of dry-snow sub-structure using GNSS reflected signals.Remote Sensing of Environment,124:122-134[DOI:10.1016/j.rse.2012.05.012]
Chew C,Small E E and Larson K M.2016.An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil.GPS Solutions,20(3):525-537[DOI:10.1007/s10291-015-0462-4]
Chew C C,Small E E,Larson K M and Zavorotny V U.2015.Vegetation sensing using GPS-interferometric reflectometry:theoretical effects of canopy parameters on signal-to-noise ratio data.IEEETransactions on Geoscience and Remote Sensing,53(5):2755-2764[DOI:10.1109/TGRS.2014.2364513]
Dobson M C,Ulaby F T,Hallikainen M T and El-Rayes M A.1985.Microwave dielectric behavior of wet soil-Part II:dielectric mixing models.IEEE Transactions on Geoscience and Remote Sensing,GE-23(1):35-46[DOI:10.1109/TGRS.1985.289498]
Ferrazzoli P,Guerriero L,Pierdicca N and Rahmoune R.2011.Forest biomass monitoring with GNSS-R:theoretical simulations.Advances in Space Research,47(10):1823-1832[DOI:10.1016/j.asr.2010.04.025]
Hallikainen M T,Ulaby F T,Dobson M C,El-Rayes M A and Wu L K.1985.Microwave dielectric behavior of wet soil-part 1:empirical models and experimental observations.IEEE Transactions on Geoscience and Remote Sensing,GE-23(1):25-34[DOI:10.1109/TGRS.1985.289497]
Huang XL,Shi L and Suykens J A K.2014.Asymmetric least squares support vector machine classifiers.Computational Statistics and Data Analysis,70:395-405[DOI:10.1016/j.csda.2013.09.015]
Jin S G,Zhang Q Y and Qian X D.2017.New progress and application prospects of Global Navigation Satellite System Reflectometry(GNSS+R).Acta Geodaetica et Cartographica Sinica,46(10):1389-1398(金双根,张勤耘,钱晓东.2017.全球导航卫星系统反射测量(GNSS+R)最新进展与应用前景.测绘学报,46(10):1389-1398)[DOI:10.11947/j.AGCS.2017.20170282]
Johnson M L,Correia J J,Yphantis D A and Halvorson H R.1981.Analysis of data from the analytical ultracentrifuge by nonlinear least-squares techniques.Biophysical Journal,36(3):575-588[DOI:10.1016/S0006-3495(81)84753-4]
Larson K M,Braun J J,Small E E,Zavorotny V U,Gutmann E D and Bilich A L.2010.GPS multipath and its relation to near-surface soil moisture content.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,3(1):91-99[DOI:10.1109/JSTARS.2009.2033612]
Larson K M and Nievinski F G.2013.GPS snow sensing:results from the Earth Scope Plate Boundary Observatory.GPS Solutions,17(1):41-52[DOI:10.1007/s10291-012-0259-7]
Le Hégarat-Mascle S,Zribi M,Alem F,Weisse A and Loumagne C.2002.Soil moisture estimation from ERS/SAR data:toward an operational methodology.IEEE Transactions on Geoscience and Remote Sensing,40(12):2647-2658[DOI:10.1109/TGRS.2002.806994]
Martin-Neira M.1993.A passive reflectometry and interferometry system(PARIS):application to ocean altimetry.ESA Journal,17(4):331-355
Njoku E G and Entekhabi D.1996.Passive microwave remote sensing of soil moisture.Journal of Hydrology,184(1/2):101-129[DOI:10.1016/0022-1694(95)02970-2]
Peng X F,Wan W,Li F and Chen X W.2017.The suitability analysis of soil moisture retrieval using GNSS-R technology.Journal of Remote Sensing,21(3):341-350(彭学峰,万玮,李飞,陈秀万.2017.GNSS-R土壤水分遥感的适宜性分析.遥感学报,21(3):341-350)[DOI:10.11834/jrs.20176198]
Powell M J D and Yuan Y.1990.A trust region algorithm for equality constrained optimization.Mathematical Programming,49(1/3):189-211[DOI:10.1007/BF01588787]
Rodriguez-Alvarez N,Bosch-Lluis X,Camps A,Vall-Llossera M,Valencia E,Marchan-Hernandez J F and Ramos-Perez I.2009.Soil moisture retrieval using GNSS-R techniques:Experimental results over a bare soil field.IEEE Transactions on Geoscience and Remote Sensing,47(11):3616-3624[DOI:10.1109/TGRS.2009.2030672]
Sabater J M,Rüdiger C,Calvet J C,Fritz N,Jarlan L and Kerr Y.2008.Joint assimilation of surface soil moisture and LAI observations into a land surface model.Agricultural and Forest Meteorology,148(8/9):1362-1373[DOI:10.1016/j.agrformet.2008.04.003]
Suykens J A K and Vandewalle J.1999.Least squares support vector machine classifiers.Neural Processing Letters,9(3):293-300[DOI:10.1023/A:101862860]
Tsang L,Kong J A and Shin R T.1985.Theory of Microwave Remote Sensing.London:Wiley-Interscience
Wu X R,Li Y and Xu J.2012.Theoretical study on GNSS-R vegetation biomass//Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium.Munich,Germany:IEEE:6380-6383[DOI:10.1109/IGARSS.2012.6352719
Xie S F,Liang Y J,Zheng Z T and Liu H F.2017.Combined forecasting method of landslide deformation based on MEEMD,approximate entropy,and WLS-SVM.ISPRS International Journal of Geo-Information,6(1):5[DOI:10.3390/ijgi6010005]
Yan S H,Gong J Y,Zhang X X and Li D X.2011.Ground based GNSS-R observations for soil moisture.Chinese Journal of Geophysics,54(11):2735-2744(严颂华,龚健雅,张训械,李冻秀.2011.GNSS-R测量地表土壤湿度的地基实验.地球物理学报,54(11):2735-2744)[DOI:10.3969/j.issn.0001-5733.2011.11.003]
Yang L X,Yang S Y,Li S J,Zhang R,Liu F and Jiao L C.2015.Coupled compressed sensing inspired sparse spatial-spectral LSS-VM for hyperspectral image classification.Knowledge-Based Systems,79:80-89[DOI:10.1016/j.knosys.2015.01.006]
Ye X F.2016.Research on Processing Method of GNSS Station Environment Error and Its Application Using SNR Data.Wuhan:China University of Geosciences(Wuhan).(叶险峰.2016.基于GNSS信噪比数据的测站环境误差处理方法及其应用研究.武汉:中国地质大学(武汉))
Zavorotny V U,Larson K M,Braun J J,Small E E,Gutman E D and Bilich A L.2010.A physical model for GPS multipath caused by land reflections:toward bare soil moisture retrievals.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,3(1):100-110[DOI:10.1109/JSTARS.2009.2033608]
Zhang S C,Dai K Y,Liu K,Hou X W and Zhao Y H.2016.Research of GPS-MR on snow depth monitoring.Progress in Geophysics,31(4):1879-1884(张双成,戴凯阳,刘凯,侯晓伟,赵迎辉.2016.GPS-MR技术用于降雪厚度监测研究.地球物理学进展,31(4):1879-1884)[DOI:10.6038/pg20160461]