主被动微波协同反演植被覆盖地表土壤水分方法研究

发布时间：2018-01-03 12:39

本文关键词：主被动微波协同反演植被覆盖地表土壤水分方法研究　出处：《山东农业大学》2017年硕士论文　论文类型：学位论文

【摘要】：水是地球上最重要的资源,是人类生命存活的基本元素。土壤水分作为全球陆地自然环境生态系统中的重要组成部分,控制着陆地与大气间水热能量交换。微波遥感具有宏观、高时效和经济性的特点,且穿透性强、对水分敏感性强,适合进行土壤水分监测,比传统的可见光和红外遥感有很大的优势。是目前对地表土壤水分进行监测最有效的手段之一。在土壤水分反演研究中,被动微波遥感对水分更为敏感,可用来反演土壤水分绝对值,而主动微波遥感对地表形态与植被类型比较敏感,常用来进行土壤水分变化相对值的估算,且其空间分辨率较高。主动和被动微波遥感各有特点,二者的联合可充分发挥各自的优势,达到优势互补的效果,特别是当有农作物覆盖在土壤表面时,主被动微波的联合反演方法能够简化土壤水分监测的过程,同时提高土壤水分估算的质量。目前,光学与微波的协同遥感技术已经受到广大研究学者的重视,现在发射的卫星基本都同时搭载着微波和光学传感器。因此,光学和微波的协同遥感成为了遥感反演地表参数的一个重要方法,两者的结合将能更准确的实现地表参数的反演,提高其反演精度,充分发挥遥感卫星的作用。本文联合了光学与微波遥感的各自优势,采用以主动、被动微波反演土壤水分模型为主,光学影像参数为辅的方法,将光学模型反演的植被参数(植被指数VI、叶面积指数LAI)输入到微波模型中,共同实现地表土壤水分的估算实验,提高土壤水分的反演精度。本研究选择地势平坦区域且植被覆盖均匀的黑河流域阿柔地区进行土壤水分的反演试验,基于此本文提出两种土壤水分反演方法:1.利用水云模型去除掉植被层含水量对土壤水分信息获取产生的影响,再结合主动微波反演方法来实现土壤水分的估算。2.在单频率、单入射角的条件下,考虑植被指数VI对土壤水分变化的影响,融合L波段辐射计地面观测数据与L波段ALOS PALSAR雷达影像数据,建立起土壤体积水分与雷达后向散射之间的关系,在不考虑土壤粗糙度影响的前提下来实现土壤水分监测。本研究的主要成果包括以下几个方面:(1)分析阿柔加密观测区农田水分的蒸发蒸腾机理,植被、土壤及粗糙度等参数对卫星雷达回波信号的散射作用机理,以及光学卫星Landsat-5影像、雷达卫星ALOS-PLASAR数据以及辐射计遥感数据的特点。并分析植被、土壤等参数对卫星雷达回波信号的散射作用机理。(2)针对观测区拥有植被覆盖,造成土壤对雷达后向散射影响的问题,通过去除土壤散射模型中植被层的散射贡献,建立了参数优化的水云模型。本文基于农作物覆盖地表的后向散射机制,利用半经验水云模型,从雷达体散射信号中扣除掉植被水分的贡献,最后,开展植被覆盖下土壤水分的高分辨率遥感反演。(3)基于PLASAR雷达数据进行主动微波反演土壤水分算法,将校正后的雷达影像及TM影像通过重采样的方法做降尺度处理,形成30m分辨率的影像数据。随后针对植被覆盖地表,分别在HH和VV极化下,建立起针对土壤的后向散射系数同土壤水分之间的线性关系,进行中分辨率下植被覆盖地表的土壤水分估算实验。(4)选择10m高分辨率的PLASAR雷达卫星数据及120m低分辨率的辐射计数据,以尺度融合为基础,考虑植被指数VI的基础上,分析三者之间的数学关系,从而建立起主被动微波联合反演30m中分辨率下的土壤水分算法,来实现大范围针对植被覆盖的土壤水分估测。(5)利用观测区中分辨率下的土壤水分实测数据,分别进行对主动微波反演土壤水分方法和主被动微波联合反演算法进行验证,并对其反演的土壤水分模拟值精度进行比较和评价。
[Abstract]:Water is the most important resource on the earth, is the basic element of human survival. Soil moisture is an important part of the global terrestrial ecosystem in the natural environment, control of water and heat energy exchange between land and atmospheric. Microwave remote sensing has the characteristics of high efficiency and macro economy, and strong penetration of water strong sensitivity, suitable for monitoring soil moisture, compared with the traditional optical and infrared remote sensing has great advantage. Is present on the surface soil moisture is one of the most effective means of monitoring. In the study of soil moisture retrieval, passive microwave remote sensing of water is more sensitive, and can be used to inverse the soil moisture content, and active microwave remote sensing the surface morphology and the vegetation types are more sensitive, commonly used in estimating soil moisture change relative value, and its high spatial resolution. The active and passive microwave remote sensing with different characteristics, the two joint He can give full play to their strengths, to achieve complementary effects, especially when there are crop mulch on the soil surface, the process of the joint inversion method of active and passive microwave can simplify the monitoring of soil moisture, and improve the quality of soil moisture estimation. At present, optical and microwave remote sensing technology cooperation has attracted the attention of scholars research now, the basic satellite are also equipped with microwave and optical sensors. Therefore, optical and microwave remote sensing coordination has become an important method for remote sensing inversion of surface parameters, the combination of the two will be able to realize more accurate surface parameter retrieval to improve the retrieval accuracy, and give full play to the role of remote sensing satellite. This paper combined with the the advantages of optical and microwave remote sensing, using active, passive microwave soil moisture retrieval model, supplemented by optical imaging parameters, optical model The inversion of vegetation parameters (leaf area index LAI vegetation index VI, microwave) input to the model, to realize estimation of experimental soil moisture, improve the inversion precision of soil moisture inversion test. A soft area of Heihe basin of flat area and vegetation coverage in uniform soil moisture, this paper put forward two soil moisture inversion method based on the cloud model: 1. remove vegetation layer moisture generated effects on soil moisture information, estimation of.2. combined with active microwave inversion method to achieve the soil moisture in the single frequency, single incident angle under the condition of considering the influence of vegetation on soil moisture index VI, fusion L band radiometer ground observation data and L band ALOS PALSAR radar image data, establish the volume of soil moisture and the relationship between radar backward scattering, without considering the soil roughness. Ring down the premise of implementation of soil moisture monitoring. The main results of this study include the following aspects: (1) analysis of a soft encryption evaporation mechanism, observation area of farmland water vegetation, soil and roughness parameters on the scattering mechanism of satellite radar echo signal, and optical characteristics of Landsat-5 satellite image, radar satellite ALOS-PLASAR the data and radiometer data. And analysis of vegetation, soil and other parameters on the scattering mechanism of satellite radar echo signal. (2) in the observation area with vegetation cover, causing the radar backward scattering effect of soil, soil removal contribution by scattering scattering model of vegetation layer, established cloud model parameters optimization. Based on the crop covers the surface of the back scattering mechanism, using semi empirical water cloud model, radar scattering signal from the body after deducting the vegetation water contribution, finally, High resolution remote sensing inversion of vegetation cover soil moisture. (3) active microwave soil moisture retrieval algorithm based on PLASAR radar data, the radar images and TM images corrected by means of resampling the downscaling image data processing, forming the 30m resolution. Then according to the vegetation cover, respectively in HH and VV polarization, establish a linear relationship between the soil backscattering coefficient with soil moisture, soil moisture in the surface vegetation cover resolution estimation experiment. (4) choose radiation 10m high resolution PLASAR radar satellite data and 120m low resolution data fusion based on scale, considering the vegetation the VI index, a mathematical analysis of the relationship between the three, so as to establish the soil moisture resolution of active and passive microwave inversion in the 30m, to achieve a wide range for vegetation coverage Estimation of soil moisture. (5) the use of soil moisture data resolution observation area under, respectively, to validate the active microwave soil moisture retrieval method and passive microwave combined inversion algorithm, and soil moisture on the retrieval precision of simulation value comparison and evaluation.

【学位授予单位】：山东农业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：S152.7;S127

【参考文献】