摘要
地表土壤水分的时空分布信息在水文学、气候学、植物生态学等学科中有十分重要的地位，常常作为水文模型、气候模型、生态模型的重要输入参数，同时，也在预报旱情、估算农作物产量等方面起到重要作用。因此，监测地表土壤水分时空分布信息成为一个迫切需要解决的问题。传统测量方法、光学遥感和被动微波遥感获取土壤水分都表现出一定的不足。研究表明，主动微波遥感能够弥补光学遥感、被动微波遥感在土壤水分监测应用中的不足，为土壤水分监测提供新的方法和手段。
在微波波段，土壤电磁波的地表散射特性直接与地表粗糙度、介电常数、水分含量相关。而介电常数的大小与水分含量有很大的关系，因此，地表的微波散射特性的主要影响因素就是地表粗糙度和土壤水分。获取这两个重要参数对于地表散射特性的研究至关重要。
目前星载SAR系统多极化、多角度方向的快速发展，使采用不同极化方式及不同入射角条件下的后向散射系数组合研究雷达后向散射系数与地表参数之间的关系成为可能。本研究在比较三种典型的地表微波散射模型(Oh模型、Dubois模型、AIEM模型)的基础上，用理论模型AIEM模型对地表的微波散射特征进行了模拟和分析，提出了用多极化SAR后向散射系数数据来反演地表粗糙度和土壤水分的经验模型。本研究的主要研究成果包括以下几方面：
（1）比较Dubois模型、Oh模型和AIEM模型，结果表明，Dubois模型对入射角变化、介电常数的响应与实际情况有所不同。因此不推荐选择Dubois模型对雷达数据进行土壤水分反演。Oh模型能够模拟交叉极化的后向散射特征，对同极化的后向散射数据模拟值误差较大，因此，也不推荐使用。而AIEM模型模拟不论是模拟同极化微波后向散射特征，还是模拟交叉极化微波后向散射特征，都能够比较好地刻画地表微波散射特征，因此在进行模拟时选择AIEM模型。
（2）根据雷达图像原理，应用NEST DAT4A-1.5软件对研究区的RADARSAT-2数据进行定标，并对不同地物和不同程度盐渍地的HH、VV、VH、VH/VV的后向散射系数特征进行统计和对比，并结合这些特征，从雷达图像成像原理及物理机制的角度出发，初步探讨了不同地物类型的极化特征与成因。
（3）利用AIEM模型模拟盐渍地地表C波段雷达信号的后向散射特征，分析了均方根高度、相关长度、土壤含水量等地表参数以及入射角、极化方式等系统参数对雷达微波后向散射特征的影响。揭示了雷达后向散射系数随这些参数的变化而变化的规律，对雷达反演地表土壤水分的理想系统参数设置进行了探讨。
（4）详细分析了C波段同极化后向散射系数差、交叉极化后向散射系数差同土壤含水量、组合粗糙度参数之间的关系，考虑了雷达入射角的效应，并在此基础上建立了经验的盐渍地地表散射模型，最后针对RADARSAT-2观测数据组合方式的不同，提出了具体的土壤水分、地表粗糙度反演方案。
（5）将建立的反演算法应用到新疆渭库三角洲绿洲外围盐渍化土壤试验区，应用HH/VV、HH/HV、VV/VH三种极化组合方式的RADARSAT-2数据，得到了新疆渭库绿洲盐渍地地表粗糙度空间分布图和土壤水分空间分布图，将反演数据与实测数据进行对比，并比较三种组合极化方式下的精度，其中VV-VH极化方式取得了较高的精度，土壤水分实测值与反演值的相关性达到0.9148。
Spatial and temporal distribution of soil moisture information play a very significantposition in the disciplines of hydrology, climatology and plant ecology, it often beused as the input parameters in hydrologic models, climate models and ecologicalmodels and as the important indicators in prediction of drought, crop yield estimatesand other. Therefore, get the information of the wide range of soil moisture spatialand temporal distribution is an urgent problem. There are some restrictions in thetraditional measurement methods, optical remote sensing and passive microwaveremote sensing to get soil moisture. Studies have shown that active microwave remotesensing can compensate the shortcoming in the optical remote sensing and passivemicrowave remote sensing in the soil moisture monitoring applications and provide anew method and means for soil moisture monitoring.
In the microwave band, the soil surface scattering of the soil electromagneticproperties is related to the surface roughness, dielectric constant and the moisturecontent. The dielectric constant and the moisture content have a great relationship, sothe surface roughness and the soil moisture surface microwave scattering propertiesare the main factors, so obtain these two important parameters is essential to researchin the surface scattering properties.
The current SAR system is developing to the multi-polar, multi-angle direction, so it’spossible to research the relationship in the radar backscattering coefficient and the soilsurface parameters under the conditions of different polarization and incidence angles.This study based on comparing the three typical surface microwave scattering model(Oh model, Dubois model, the AIEM model) and used the AIEM model as atheoretical model to simulate and analysis the characteristics of soil surfacemicrowave scattering and proposed an empirical model to inversion surface roughnessparameters and soil moisture with multi-polarization SAR backscattering coefficientdata. The results of the study indicate that:
(1) Comparing the Dubois model, Oh model and AIEM model, the results show thatDubois model has a different response with the actual situation to incidence angle and the dielectric constant. So we don’t recommend selecting this Dubois model for soilmoisture inversion of radar data. The Oh model can simulate the scatteringcharacteristics of cross-polarization but it has the large errors in the analog value ofsame polarizationscattering data, so we don’t recommend, too. The AIEM model cancharacterize the surface microwave scattering characteristics better both in simulatingthe same polarization scattering characteristics and the cross-polarization microwavescattering characteristics. Therefore,we should choose the AIEM model to simulate.
(2) According to the principle of radar images, we used the NEST the DAT4A-1.5software to do the calibration of the study area RADARSAT2data and compareddemographic characteristics of different objects’ backscattering coefficient featureswith different saline degrees in HH, VV, HV and VH, On this basis, exploring thecharacteristics and the reason of different objects polarization from the perspective ofthe imaging principle of radar images and the physical mechanism.
(3)Using the AIEM model to simulate the C-band radar signal’s backscatteringcharacteristics of the exposed salinization surface,analyzing the affection of the soilsurface parameters include the rms height, length, soil moisture and the radar systemparameters include the angle of incidence, polarization to radar microwave’s backscattering characteristics.Revealing the variation of the radar backscatteringcoefficient changes with these parameters and discussed the set of the ideal systemparameters in inversion of surface soil moisture.
(4) Analyzing the relationship of the C-band same polarization and the cross-polarizer’s backscattering coefficient to the soil moisture and the combined roughnessparameters, considering the effects of radar incidence angle and established theexperience bare salinized surface scattering model. Finally，proposed a inversionprogram of soil moisture and surface roughness for the combinations of differentRADARSAT-2observational data.
(5) Appling the inversion algorithm to Wei-Ku strangle oasis peripheral salinizedsoil test area and obtain the soil moisture spatial distribution figure and surfaceroughness space figure with the HH/VV,HH/HV,VV/VH three polarizationcombinations of RADARSAT-2data. The inversion results have a good correlation with the measured data of soil moisture, and compareing the precision of threepolarization mode,VV-VH polarization achieved high accuracy, and the corelationbetween the measured value and inversion value reached0.9148.
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