基于极化SAR的水稻物候期监测与参数反演研究

发布时间：2018-12-14 11:01

【摘要】：水稻是世界三大粮食作物之一,其生产状况与整个世界的粮食安全、社会稳定息息相关。为此,各国政府、农业管理部门以及农业从业者迫切希望对水稻农情信息实现及时、有效地监控。近年来,遥感技术以其覆盖广、重访周期短等特点引起了相关从业人员和管理部门的浓厚兴趣。由于水稻生长周期内往往长时间被云雨覆盖,不能确保实时获取清晰可用的光学遥感数据,因此全天时、全天候的雷达遥感成为水稻监测和估产的有效观测手段。随着雷达技术从单极化、多极化向全极化不断发展,以及紧致极化雷达等新型极化雷达数据的出现,极化雷达遥感成为人们的关注热点。它能获得水稻冠层在不同极化方式下的雷达响应特征,包含散射强度和相位信息,更好地反映水稻冠层含水量、形态结构和长势等各方面信息,为水稻农情监测提供丰富的数据支撑。本文以多时相全/紧致极化雷达遥感影像为主要数据源,辅以近同步的多光谱光学数据,以高精度水稻物候期识别和参数反演为目标,在极化信息挖掘、特征提取算法、反演模型以及结果对比分析等方面进行了深入研究,尝试改进或解决现有水稻物候期识别和参数反演研究中存在的重要问题,在考虑研究科学价值的同时注重研究方法和结果在实际应用中的可推广性,为区域/田块尺度高精度水稻物候期识别和参数反演提供可靠的理论依据和可行的实践方法。本文的主要研究内容包括:1)基于紧致极化SAR数据,不仅实现插秧籼稻田和撒播粳稻田的高精度分类(精度高于85%),而且较好地识别两类水稻田的7个物候期。2)协同利用光学植被指数和雷达特征参数,发展了基于蒙特卡洛随机抽样和相关性抑制的特征选择算法(MCCL),构建最优特征子集,实现水稻物候期自动识别。最终水稻8个物候期区间的识别总体精度为86.59%。3)深入、定量地讨论了水稻物候期识别过程中的关键问题。首先,讨论基于多时相极化SAR和多光谱数据的水稻物候期识别最优方案。接着,发现考虑水稻种类和种植方式的差异对物候监测至关重要。在考虑插秧籼稻田和撒播粳稻田的差异后,物候识别精度提高了至少16%。此外,仅使用雷达或光学数据对水稻物候期识别的精度较低(80%),光学植被指数和极化SAR特征参数分别对分蘖后期-蜡熟早期和水稻生长后期的水稻冠层生长变化不敏感。4)本文改进的极化分解方法可以有效地降低体散射过高估计和负像元的现象。水稻作为时变目标,在不同物候期内呈现出不同的散射机制。在改进的极化分解方法中,广义体散射模型通过考虑HH和VV极化方式下后向散射系数的比值,可以更好地刻画水稻在不同物候期内的体散射机制,为改善水稻参数反演的精度提供模型支撑。5)考虑水稻冠层水平方向含水量的差异,将二次散射引入水云模型,提出改进的水云模型,建立改进水云模型与改进极化分解的耦合架构,实现水稻全生育期参数反演。反演结果优于传统的水云模型,尤其是在水稻营养生长阶段(幼苗期-孕穗期)。本文的创新性贡献如下:1)首次基于紧致极化SAR数据,考虑水稻品种、种植方式差异,实现两类水稻田7个物候期的识别,论证了紧致极化SAR数据在水稻监测中的潜力,拓展了其在农情监测中的应用,为新一代对地观测SAR系统中紧致极化SAR卫星的先期验证提供可靠依据。2)协同利用极化SAR和多光谱数据,发展了基于蒙特卡洛随机抽样和相关性抑制的最优特征选择算法,构建水稻物候期自动识别框架,首次构建水稻8个物候期识别的最优特征矩阵,并给出基于多时相极化SAR和多光谱数据的水稻物候期识别最优方案。3)考虑了水稻冠层的水平异质性,首次将二次散射机制引入传统水云模型,发展了一种考虑物候期信息的改进水云模型。结合改进的极化分解方法,首次建立改进的水云模型与极化分解信息耦合架构,发展了一种自适应的水稻全生育期参数反演高精度反演方法。未来研究方向主要包括:第一,获取更多不同频段、不同角度的数据,尝试提出一种针对水稻物候期识别和水稻参数反演的最优成像模式。第二,本文提出的改进水云模型主要针对插秧水稻田,对撒播水稻田的改进建模需要进一步思考。
[Abstract]:Rice is one of the three major food crops in the world, and its production status is closely related to food security and social stability of the whole world. To that end, governments, agricultural management and agricultural practitioners are eager to monitor the information of rice farmers in a timely and effective manner. In recent years, the remote sensing technology has aroused the strong interest of the relevant practitioners and the management departments with the features of wide coverage and short period of repeated visits. As long as the rice growth period is covered by the cloud rain for a long time, it is not possible to ensure that the clear and available optical remote sensing data is acquired in real time, and therefore, all-weather radar remote sensing becomes an effective observation means for rice monitoring and estimation all day. With the development of the radar technology from the single polarization, the multi-polarization to the full polarization, and the emergence of new polarized radar data such as the compact-induced polarization radar, the polarization radar remote sensing has become the focus of attention. The radar response characteristics of the rice crown layer in different polarization modes can be obtained, the scattering intensity and the phase information are included, and the information of the water content, the morphological structure and the long potential of the rice crown layer is better reflected, and the rice crown layer is provided with abundant data support for the rice agricultural condition monitoring. In this paper, a multi-time-phase full/ compact polarization radar remote sensing image is used as the main data source, and the near-synchronous multi-spectral optical data is used as the target for high-precision time-period identification and parameter inversion of the rice, In order to improve or solve the important problems existing in the research of the identification of the phenological period and the inversion of the parameters of the existing rice, the paper tries to improve or solve the important problems in the research of the identification of the phenological period and the inversion of the parameters of the existing rice, and to pay more attention to the research methods and the replicability of the results in the practical application while considering the scientific value. and provides a reliable theoretical basis and a feasible practical method for the identification and the parameter inversion of the region/ field block scale high-precision rice phenological period identification and parameter inversion. The main research contents of this paper are as follows: 1) Based on the compact-induced polarization SAR data, not only the high-precision classification of the rice-planting rice field and the seed-sowing japonica rice field is realized (the precision is higher than 85%), A feature selection algorithm (MCCL) based on Monte Carlo random sampling and correlation suppression is developed to realize the automatic identification of the waiting period of rice. The key problems in the identification process of rice phenological period were discussed in depth and quantificationally. First, the optimal scheme for identifying the waiting period of rice based on multi-time-phase polarization SAR and multi-spectral data is discussed. Next, it was found that the difference in the type of rice and the planting pattern was of great importance to the monitoring of phenology. After considering the difference of rice field and sowing rice field, the accuracy of phenological recognition is increased by at least 16%. In addition, the accuracy of the identification of the rice phenological period using only radar or optical data is low (80%), The parameters of the optical vegetation index and the polarization SAR are insensitive to the change of the growth of the crown of the rice in the later stage of the stage-wax and the later stage of the growth of the rice. As a time-varying target, rice presents different scattering mechanisms for different phenological periods. in the improved polarization decomposition method, the generalized body scattering model can better characterize the body scattering mechanism of the rice in different phenological periods by taking into account the ratio of the back scattering coefficient in the HH and the VV polarization mode, in order to improve the precision of the rice parameter inversion, a model support is provided. 5) considering the difference of the water content in the horizontal direction of the rice crown layer, the secondary scattering is introduced into the water cloud model, an improved water cloud model is proposed, and a coupling structure for improving the water cloud model and the improved polarization decomposition is established, and the full growth period parameter inversion of the rice is realized. The result of the inversion is better than that of the traditional water cloud model, especially in the vegetative growth stage of rice (seedling stage-booting stage). The innovative contribution of the paper is as follows: 1) The first time based on the compact-induced polarization SAR data, considering the difference of the rice variety and the planting mode, the identification of the seven phenological periods of the two kinds of paddy fields is realized, the potential of the compact-induced polarization SAR data in the rice monitoring is demonstrated, the application of the compact-induced polarization SAR data in the monitoring of the agriculture is expanded, in order to provide a reliable basis for the early verification of the compact-induced polarization SAR satellite in the ground-observation SAR system of the next generation, the optimal feature selection algorithm based on the Monte Carlo random sampling and the correlation suppression is developed in cooperation with the polarized SAR and the multispectral data, and the automatic identification frame for the waiting period of the rice is constructed, In this paper, the optimal characteristic matrix for the first time-period identification of rice is constructed, and the optimal scheme for the waiting period of rice based on multi-time-phase polarization SAR and multi-spectral data is given. 3) The horizontal heterogeneity of the rice crown layer is considered, and the secondary scattering mechanism is first introduced into the traditional water cloud model. An improved water cloud model considering the phenological information was developed. In combination with the improved polarization decomposition method, the improved water cloud model and the polarization decomposition information coupling architecture are established for the first time, and a high-precision inversion method for the adaptive rice full growth period parameter inversion is developed. The future research direction mainly includes: firstly, acquiring more data of different frequency bands and different angles, and attempting to propose an optimal imaging mode aiming at the identification of the waiting period of the rice and the inversion of the rice parameters. Secondly, the improved water cloud model proposed in this paper is mainly aimed at the rice field of rice transplanting, and the improved modeling of the rice field is needed to be further thought.
【学位授予单位】：中国科学院大学(中国科学院遥感与数字地球研究所)
【学位级别】：博士
【学位授予年份】：2017
【分类号】：S511;S127

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