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耿德源，赵天杰，施建成，胡路，徐红新，胡建峰.2021.地基雷达的微波面散射模型对比与土壤水分反演.遥感学报，25（4）： 929-940
Geng D Y，Zhao T J，Shi J C，Hu L，Xu H X and Hu J F. 2021. Surface microwave scattering model evaluation and soil moisture retrieval based on ground-based radar data. National Remote Sensing Bulletin， 25（4）：929-940
为了探究地基合成孔径雷达（cGBSAR）后向散射信号的时空变化规律和研究雷达土壤水分反演的影响因素，在内蒙古闪电河流域的昕元牧场站进行了地基雷达观测试验，本文结合以上观测试验的地基雷达数据进行波段、入射角度、极化通道3个雷达参数以及地表粗糙度参数对雷达的后向散射系数影响的分析，然后利用以上分析结果选择地表微波面散射模型，最后利用选定的地表微波面散射模型构建人工神经网络数据集来反演地表土壤水分。结果表明：（1）在地基雷达视场内，各地表微波面散射模型的模拟结果与地基雷达实测的L波段全极化数据拟合效果最佳的是AIEM-Oh模型。（2）通过对20°—60°范围内的雷达入射角度的AIEM-Oh模型后向散射系数模拟的绝对残差分析发现，雷达入射角为25°、41°和53°时模拟结果最接近雷达实测值。（3）最后通过分析土壤水分反演结果发现，当雷达入射角度为41°时的土壤水分反演精度最高，相关系数
R
是0.8080，RMSE是0.0385
<math id="M1"><mrow><mrow><msup><mrow><mi mathvariant="normal">m</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msup></mrow><mo>/</mo><mrow><msup><mrow><mi mathvariant="normal">m</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msup></mrow></mrow></math>
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http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=11576418&type=
8.88999939
3.04799986
。本文的结论是雷达后向散射信号受到雷达入射角度和地表粗糙度相互作用的影响，因此通过考虑地表粗糙度来合理的选取雷达入射角能够提高土壤水分的反演精度。
In the process of the earth's water cycle
the status of soil moisture is very important. Soil moisture is an important parameter that controls the exchange of water
heat and energy within the various layers of the earth. In recent years
microwave remote sensing has become one of the important methods for monitoring surface soil moisture. There is a mathematical correlation between the amount of soil moisture and the dielectric constant of the soil. At the same time
the dielectric constant information of the soil directly has a systematic influence on the microwave backscattering intensity information
so by obtaining the backscattering information of the radar microwave signal
the dielectric information of the observation surface can be estimated
so as to carry out the soil moisture monitoring. It can be seen that using mathematical models to describe the correlation between backscatter information and soil moisture is helpful to obtain soil moisture information. A ground-based radar observation experiment is conducted at Xinyuan Ranch Station in the lightning river basin of Inner Mongolia to investigate the temporal and spatial variations of backscattered signals of ground-based synthetic aperture radar and study the influencing factors of radar soil moisture inversion. The radar backscattering coefficients are analyzed on the basis of the ground-based radar data from the above observation tests
including radar bands
incident angles
polarization channels
and other radar parameters. Then
the results of the preceding analysis are used to select a surface microwave surface scattering model. Lastly
an artificial neural network data set is constructed using the selected surface microwave surface scattering model to retrieve surface soil moisture. The results are as follows: (1) In the ground-based radar field of view
the simulation results of the surface microwave surface scattering model and the L-band full polarization data measured using the ground-based radar are the best fit for the AIEM-Oh model. (2) The absolute residual analysis of the AIEM-Oh model simulation results of radar incident angles in the range of 20°–60° indicated that the simulation results are closest to the radar measured values when the radar incident angles are 25°
41°
and 53°. (3) The results of soil moisture inversion show that when the radar incident angle is 41°
the soil moisture inversion accuracy is highest
the correlation coefficient R is 0.8080
and the RMSE is 0.0385 m³/ m³. The conclusion of this paper is that the radar backscatter signal is affected by the combination of the radar incident angle and surface roughness. Therefore
a reasonable selection of radar incident angle by considering surface roughness can improve the accuracy of soil moisture retrieval. On the one hand
this research uses the surface microwave surface scattering model to simulate the neural network training data set
which is equivalent to using the practical physical simulation data set to embed the mathematical model (neural network) with the physical foundation
so as to reasonably explain the effectiveness of the training data set. On the other hand
through the sensitivity analysis of radar measurement data
the law of backscattering strength with the radar incident angle is obtained
which weakens the inversion error caused by the spatial heterogeneity of radar data. The improvement of soil moisture retrieval methods also provides new ideas for improving soil moisture retrieval.
遥感地基雷达观测试验地表微波面散射模型神经网络土壤水分cGBSAR
remote sensingground-based radar observation testsurface microwave scattering modelneural networksoil moisturecGBSAR
Baghdadi N, Zribi M, Loumagne C, Ansart P and Anguela T P. 2008. Analysis of TerraSAR-X data and their sensitivity to soil surface parameters over bare agricultural fields. Remote Sensing of Environment, 112(12): 4370-4379 [DOI: 10.1016/j.rse.2008.08.004http://dx.doi.org/10.1016/j.rse.2008.08.004]
Baghdadi N, Cresson R, El Hajj M, Ludwig R and La Jeunesse I. 2012. Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks. Hydrology and Earth System Sciences, 16(6): 1607-1621 [DOI: 10.5194/hess-16-1607-2012http://dx.doi.org/10.5194/hess-16-1607-2012]
Bruckler L, Witono H and Stengel P. 1988. Near surface soil moisture estimation from microwave measurements. Remote Sensing of Environment, 26(2): 101-121 [DOI: 10.1016/0034-4257(88)90091-0http://dx.doi.org/10.1016/0034-4257(88)90091-0]
Champagne C, Berg A, Belanger J, McNairn H and De Jeu R. 2010. Evaluation of soil moisture derived from passive microwave remote sensing over agricultural sites in Canada using ground-based soil moisture monitoring networks. International Journal of Remote Sensing, 31(14): 3669-3690 [DOI: 10.1080/01431161.2010.483485http://dx.doi.org/10.1080/01431161.2010.483485]
Crow W T, Wagner W and Naeimi V. 2010. The impact of radar incidence angle on soil-moisture-retrieval Skill. IEEE Geoscience and Remote Sensing Letters, 7(3): 501-505 [DOI: 10.1109/lgrs.2010.2040134http://dx.doi.org/10.1109/lgrs.2010.2040134]
Del Frate F, Ferrazzoli P and Schiavon G. 2003. Retrieving soil moisture and agricultural variables by microwave radiometry using neural networks. Remote Sensing of Environment, 84(2): 174-183 [DOI: 10.1016/s0034-4257(02)00105-0http://dx.doi.org/10.1016/s0034-4257(02)00105-0]
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.1985.289498http://dx.doi.org/10.1109/tgrs.1985.289498]
Dubois P C, van Zyl J and Engman T. 1995. Measuring soil moisture with imaging radars. IEEE Transactions on Geoscience and Remote Sensing, 33(4): 915-926 [DOI: 10.1109/36.406677http://dx.doi.org/10.1109/36.406677]
Fung A K, Li Z and Chen K S. 1992. Backscattering from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 356-369 [DOI: 10.1109/36.134085http://dx.doi.org/10.1109/36.134085]
Fung A K, Liu W Y and Chen K S. 2002. A comparison between IEM-based surface bistatic scattering models//IEEE International Geoscience and Remote Sensing Symposium. Toronto, Ontario, Canada: IEEE 1: 441-443 [DOI: 10.1109/igarss.2002.1025066http://dx.doi.org/10.1109/igarss.2002.1025066]
Gherboudj I, Magagi R, Berg A A and Toth B. 2011. Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data. Remote Sensing of Environment, 115(1): 33-43 [DOI: 10.1016/j.rse.2010.07.011http://dx.doi.org/10.1016/j.rse.2010.07.011]
Hassan-Esfahani L, Torres-Rua A, Jensen A and McKee M. 2015. Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sensing, 7(3): 2627-2646 [DOI: 10.3390/rs70302627http://dx.doi.org/10.3390/rs70302627]
Kim Y, Jackson T, Bindlish R, Lee H and Hong S. 2012. Radar vegetation index for estimating the vegetation water content of rice and soybean. IEEE Geoscience and Remote Sensing Letters, 9(4): 564-568 [DOI: 10.1109/lgrs.2011.2174772http://dx.doi.org/10.1109/lgrs.2011.2174772]
Kseneman M, Gleich D and Cucej Ž. 2011. Soil moisture estimation using high-resolution spotlight TerraSAR-X data. IEEE Geoscience and Remote Sensing Letters, 8(4): 686-690 [DOI: 10.1109/lgrs.2010.2099641http://dx.doi.org/10.1109/lgrs.2010.2099641]
Kseneman M and Gleich D. 2013. Soil-moisture estimation from X-band data using tikhonov regularization and neural net. IEEE Transactions on Geoscience and Remote Sensing, 51(7): 3885-3898 [DOI: 10.1109/tgrs.2012.2228486http://dx.doi.org/10.1109/tgrs.2012.2228486]
Li L, Wang D, Pan C X and Niu H N. 2016. Active microwave scattering models used in soil moisture retrieval. Remote Sensing for Land and Resources, 28(4): 1-9
李俐, 王荻, 潘彩霞, 牛焕娜. 2016. 土壤水分反演中的主动微波散射模型. 国土资源遥感, 28(4): 1-9 [DOI: 10.6046/gtzyyg.2016.04.01http://dx.doi.org/10.6046/gtzyyg.2016.04.01]
Lieu Y A, Liu S F and Wang W J. 2001. Retrieving soil moisture from simulated brightness temperatures by a neural network. IEEE Transactions on Geoscience and Remote Sensing, 39(8): 1662-1672 [DOI: 10.1109/36.942544http://dx.doi.org/10.1109/36.942544]
Liu W, Shi J C, YU Q, Wang J M and Wu S L. 2004. The relationship between radar backscattering coefficient, incidence angle and surface soil moisture. Remote Sensing for Land and Resources, 16(3): 14-17
刘伟, 施建成, 余琴, 王建明, 武胜利. 2004. 地表土壤水分与雷达后向散射系数及入射角之间关系研究. 国土资源遥感, 16(3): 14-17 [DOI: 10.6046/gtzyyg.2004.03.04http://dx.doi.org/10.6046/gtzyyg.2004.03.04]
Lu H and Shi J C. 2012. Reconstruction and analysis of temporal and spatial variations in surface soil moisture in China using remote sensing. Chinese Science Bulletin, 57(22): 2824-2834
卢麾, 施建成. 2012. 基于遥感观测的21世纪初中国区域地表土壤水及其变化趋势分析. 科学通报, 57(16): 1412-1422 [DOI: 10.1007/s11434-012-5011-8http://dx.doi.org/10.1007/s11434-012-5011-8]
Macelloni G, Paloscia S, Pampaloni P, Sigismondi S, De Matthaeis P, Ferrazzoli P, Schiavon G and Solimini D. 1999. The SIR-C/X-SAR experiment on Montespertoli: sensitivity to hydrological parameters. International Journal of Remote Sensing, 20(13): 2597-2612 [DOI: 10.1080/014311699211958http://dx.doi.org/10.1080/014311699211958]
Oh Y, Sarabandi K and Ulaby F T. 1992. An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 370-381 [DOI: 10.1109/36.134086http://dx.doi.org/10.1109/36.134086]
Oh Y, Sarabandi K and Ulaby F T. 1994. An inversion algorithm for retrieving soil moisture and surface roughness from polarimetric radar observation//Proceedings of IGARSS ’94 - 1994 IEEE International Geoscience and Remote Sensing Symposium. Pasadena, CA, USA: IEEE, 3: 1582-1584 [DOI: 10.1109/igarss.1994.399504http://dx.doi.org/10.1109/igarss.1994.399504]
Oh Y, Sarabandi K and Ulaby F T. 2002. Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 40(6): 1348-1355 [DOI: 10.1109/tgrs.2002.800232http://dx.doi.org/10.1109/tgrs.2002.800232]
Paloscia S, Pettinato S, Santi E, Notarnicola C, Pasolli L and Reppucci A. 2013. Soil moisture mapping using Sentinel-1 images: algorithm and preliminary validation. Remote Sensing of Environment, 134: 234-248 [DOI: 10.1016/j.rse.2013.02.027http://dx.doi.org/10.1016/j.rse.2013.02.027]
Rumelhart D E, Hinton G E and Williams R J. 1986. Learning representations by back-propagating errors. Nature, 323(6088): 533-536 [DOI: 10.1038/323533a0http://dx.doi.org/10.1038/323533a0]
Shi J C, Van Zyl J J, Soares J V and Engman E T. 1992. Development of soil moisture retrieval algorithm for L-band SAR measurements//Proceedings of 1992 International Geoscience and Remote Sensing Symposium. Houston, TX, USA: IEEE: 495-497 [DOI: 10.1109/igarss.1992.576750http://dx.doi.org/10.1109/igarss.1992.576750]
Shi J C, Wang J, Hsu A Y, O’Neil P E and Engman E T. 1997. Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1254-1266 [DOI: 10.1109/36.628792http://dx.doi.org/10.1109/36.628792]
Wu T D and Chen K S. 2004. A reappraisal of the validity of the IEM model for backscattering from rough surfaces. IEEE Transactions on Geoscience and Remote Sensing, 42(4): 743-753 [DOI: 10.1109/tgrs.2003.815405http://dx.doi.org/10.1109/tgrs.2003.815405]
Yan G J， Zhao T J, Mu X H, Wen J G, Pang Y, Jia L, Zhang Y G, Chen D Q, Yao C B, Cao Z Y, Lei Y H, Ji D B, Chen L F, Liu Q H, Lyu L Q, Chen J M and Shi J C. 2021. Comprehensive Remote Sensing Experiment of Carbon Cycle, Water Cycle and Energy Balance in Luan River Basin. National Remote Sensing Bulletin， 25(4): 856-870
阎广建, 赵天杰, 穆西晗, 闻建光, 庞勇, 贾立, 张永光, 陈德清, 姚崇斌, 曹志宇, 雷永荟, 姬大彬, 陈良富, 柳钦火, 吕利清, 陈镜明, 施建成. 2021. 滦河流域碳、水循环和能量平衡遥感综合试验总体设计. 遥感学报, 25(4): 856-870） [DOI: 10.11834/jrs.20210341http://dx.doi.org/10.11834/jrs.20210341]
Yang H, Guo H D, Wang C L, Li X W and Yue H Y. 2002. Polarimetric SAR surface parameters inversion based on neural network. Journal of remote sensing, 6(6): 451-45
杨虎, 郭华东, 王长林, 李新武, 岳焕印. 2002. 基于神经网络方法的极化雷达地表参数反演. 遥感学报, 6(6): 451-455 [DOI: 10.11834/jrs.20060609http://dx.doi.org/10.11834/jrs.20060609]
Zhao T J, Zhang L X, Jiang L M, Chen Q, Zhang Z Y and Zhang Y P. 2009. Joint inversion of soil moisture using active and passive microwave data. Advances in Earth Science, 24(7): 769-775
赵天杰, 张立新, 蒋玲梅, 陈权, 张志玉, 张勇攀. 2009. 利用主被动微波数据联合反演土壤水分. 地球科学进展, 24(7): 769-775 [DOI: 10.3321/j.issn:1001-8166.2009.07.010http://dx.doi.org/10.3321/j.issn:1001-8166.2009.07.010]
Zhao T J, Shi J C, Xu H X, Sun Y L, Chen D Q, Cui Q, Jia L, Huang S, Niu S D, Li X W, Yan G J, Chen L F, Liu Q H, Zhao K, Zheng X M, Zhao L M, Zheng C L, Ji D B, Xiong C, Wang T X, Li R, Pan J M, Wen J G, Mu X H, Yu C, Zheng Y M, Jiang L M, Chai L N, Lu H, Yao P P, Ma J W, Lv H S, Wu J J, Zhao W, Yang N, Guo P, Li Y X, Hu L, Geng D Y, Zhang Z Q, Hu J F and Du A P. 2021. Comprehensive remote sensing experiment of water cycle and energy balance in the Shandian river basin. National Remote Sensing Bulletin， 25(4): 871-887
赵天杰, 施建成, 徐红新, 孙彦龙, 陈德清, 崔倩, 贾立, 黄硕, 牛升达, 李秀伟, 阎广建, 陈良富, 柳钦火, 赵凯, 郑兴明, 赵利民, 郑超磊, 姬大彬, 熊川, 王天星, 李睿, 潘金梅, 闻建光, 穆西晗, 余超, 郑姚闽, 蒋玲梅, 柴琳娜, 卢麾, 姚盼盼, 马建威, 吕海深, 武建军, 赵伟, 杨娜, 郭鹏, 李玉霞, 胡路, 耿德源, 张子谦, 胡建峰, 杜爱萍. 2021. 闪电河流域水循环和能量平衡遥感综合试验. 遥感学报, 25(4): 871-887 [DOI：10.11834/jrs.20219401http://dx.doi.org/10.11834/jrs.20219401]
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